# Validity of household survey indicators to monitor food security in time and space: Burkina Faso case study

Hugo Deléglise, Yves Gérard Bazié, Agnès Bégué, Roberto Interdonato, Mathieu Roche, Maguelonne Teisseire, Elodie Maître D'hôtel

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Hugo Deléglise, Yves Gérard Bazié, Agnès Bégué, Roberto Interdonato, Mathieu Roche, et al. Validity of household survey indicators to monitor food security in time and space: Burkina Faso case study. Agriculture and Food Security, 2022, 11 (1), pp.64. 10.1186/s40066-022-00402-4. hal-04061363

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# Validity of household survey indicators to monitor food security in time and space: Burkina Faso case study

Hugo Deléglise<fnref n="1" />,³, Yves Gérard Bazié⁶, Agnès Bégué<fnref n="1" />,³, Roberto Interdonato<fnref n="1" />,³, Mathieu Roche<fnref n="1" />,³* , Maguelonne Teisseire<fnref n="1" />,⁵ and Elodie Maître d’Hôtel<fnref n="2" />,⁴*

**Abstract**

**Background:** The timely and accurate identification of food insecurity situations represents a challenging issue. Household surveys are routinely used in low-income countries and are an essential tool for obtaining key food security indicators that are used by decision makers to determine the targets of food security interventions.

**Methodology:** This paper investigates the spatial and temporal quality of the food security indicators obtained through household surveys. The empirical case of Burkina Faso is used in this paper, where a large-scale rural household survey has been conducted yearly since 2009. From this data set, three food security indicators (the Food Consumption Score, the Household Dietary Diversity Score and the Coping Strategies Index) are calculated at the regional level for each year during the 2009–2017 period.

**Results:** Results highlight that observed spatiotemporal variations in these indicators are consistent with the major regional food shocks reported in food warning system reports and are significantly correlated with variations computed from other sources of data, such as satellite images, rainfall and food prices.

**Conclusion:** These results raise new research questions on food security monitoring systems and on the use of heterogeneous data and multiple food security indicators.

**Keywords:** Indicators, Food consumption score, Household Dietary Diversity Score, Coping strategies index, Household surveys, Spatiotemporal analysis

**Introduction**

The food situation in Africa is worsening again after an improvement of several years. Progress in reducing hunger in West Africa stabilized between 2000 and 2014. During these years, the prevalence of undernourishment progressively decreased from 12.3 to 10.7% before increasing again to almost 15% in 2019 and reaching a

worrying projection of 23% for 2030 [14]. Burkina Faso ranks among the most affected countries by food insecurity in West Africa, recording a prevalence of undernourishment of 21.3% between 2015 and 2017 [12]. The situation in the region is strongly impacted by the “triple burden of malnutrition”, namely, undernutrition, micronutrient deficiency and obesity. By 2017, three key indicators of food security—the prevalences of childhood wasting, childhood stunting, and adult obesity—were 7.6%, 27.3% and 4.5%, respectively, which are some of the most critical rates in West Africa [13]. Several interrelated (climatic, socioeconomic and political) reasons

<footnote>*Correspondence: mathieu.roche@cirad.fr; elodie.maitredhotel@cirad.fr</footnote>

<footnote n="1">¹ TETIS, AgroParisTech, CIRAD, CNRS, INRAE, Univ Montpellier, Montpellier, France</footnote>

<footnote n="2">² MOISA, CIHEAM-IAMM, CIRAD, INRAE, Institut Agro, Univ Montpellier, Montpellier, France</footnote>

<footnote>Full list of author information is available at the end of the article</footnote>
have led to the deterioration of the food situation in Burkina Faso over the past few years.

Dissatisfaction regarding food security measurement persists, especially in the wake of food and financial crises [16]. To prevent food crises and design appropriate interventions, food security agricultural monitoring systems have been set up by non-governmental organizations and state organizations, such as GIEWS created by the FAO (Global Information and Early Warning System of the Food and Agriculture Organization) or FEWS-NET funded by USAID (Famine Early Warning Systems Network of the United States Agency for International Development). These systems rely on collaborations with stakeholders at local and national levels, on the use of data collected from households and markets, and on the use of agrometeorological data extracted from Earth observation data that are jointly analyzed by food security experts [15]. Within countries, governments may have set their own food security monitoring systems that utilize individual nutritional data.

It has now been clearly established that food security is a complex concept and results from the interactions among several agro-environmental, socioeconomic and biological factors. There is no consensus on how food security should be measured or on what data should be preferentially collected. Food security monitoring systems integrate multiple sources of data, among which household surveys represent a fundamental source of information [4, 21, 40]. At the national level, governments tend to rely heavily on their own surveys both to compute proxies of domestic cereal production and to characterize food security at local levels. At the global level, the World Food Program [54], the FAO and the World Bank produce food security indicators from food consumption data collected in household surveys [34].

According to [16], four criteria are needed to gauge the usefulness of a food security indicator: its cost effectiveness, its capacity to deliver timely information, its nutritional relevance, and its temporal and spatial validity. Compared to indicators derived from individual consumption surveys, indicators derived from household surveys provide information on a similar time frame, but they are more cost effective as they do not enter into the very detailed quantification of consumed food items. On the other hand, in terms of nutritional relevance, household surveys provide indicators that are thus considered as less detailed but are still valid proxies for food security situations [27, 57]. On the temporal and spatial validity of food security indicators, most studies led have either focused on the spatial or on the temporal dimension. On the spatial dimension, household data indicators are commonly aggregated to generate food security maps that are used to determine the targets of food interventions (see

https://hungermap.wfp.org/), and the spatial validity has been investigated by studies conducted at the country [41], city [50], and household [57] scales. However, these studies are generally conducted for one specific time period. On the temporal dimension, longitudinal studies have been led to gauge the capacity of household indicators to adequately measure food security trends over time [32].

Studies on the spatio-temporal validity of food security indicators derived from household surveys are missing. This paper argues that, despite the biases inherent in household surveys [2, 8, 59], food security indicators derived from household surveys can be used to detect food crises on a sub-national scale and for different years. The approach is based on official household survey data collected in Burkina Faso over the last decade ("Materials and methods" section). The analysis is built on three household indicators that are commonly used as proxies of food access at the household level: HDDS (Household Dietary Diversity Score), FCS (Food Consumption Score) and rCSI (reduced Coping Strategy Index) [27]. In "Results and discussion" section, the spatiotemporal concordances of the three indicators in terms of the food security situation at the regional scale is analyzed ("Analysis of the spatiotemporal variability in food security" subsection), along with their consistency with indicators from other data sources ("Consistency between household surveys and other data sources" subsection). "Conclusion" section draws conclusions about the strengths and limitations of household surveys in delivering food security information and about future research directions regarding food security monitoring systems and the use of heterogeneous data.

## Materials and methods

### The study area: Burkina Faso

Burkina Faso is a developing country located in West Africa, where food security is closely linked to agricultural production. It is a Soudano-Sahelian country with a semiarid climate in the north and a subhumid climate in the south, characterized by two seasons: a long dry season (approximately 8 months) and a short rainy season. Rainfall is generally low, irregular and unevenly distributed [47]. The rainy season is shorter and less intense in the north, which allows for the production of traditional rainfed cereals (millet, sorghum) and cattle breeding only. In the south, the rains last longer and are sufficient to enable crop diversification (sorghum, millet, maize, rice, cotton and groundnuts) (see Fig. 1). Food security largely depends on the production of rainfed agriculture, which is undermined by the effects of climate change (an intensification of severe climatic events, including droughts and floods, affecting food availability [47]) and by the
<figure><img src="image_3.png" /><figcaption>**Fig. 1** Climate zones in Burkina Faso, based on May to October cumulative rainfall calculated over the 2014–2017 period for the 13 regions of Burkina Faso. Source: Daily CHIRPS precipitation data set</figcaption></figure>

consequences of conflicts in the northern and eastern parts of the country (a decrease in food production and distribution networks due to population displacement [25]). These two phenomena are responsible for an economic slowdown, which has been worsened by the still fragile world economic context.

## The analytical framework

### Measuring food security at the household level

The concept of food security is complex and multifaceted, arising from many interrelated and interdependent factors. Food security is guaranteed “when all people have, at all times, physical and economic access to sufficient, safe and nutritious food” [44]. This definition involves four dimensions: the availability of appropriate quantities of food of adequate quality; access among all people to the resources needed to procure food; the stability of access to food over time despite natural or economic crises; and the adequate utilization of food (e.g., hygiene, storage, cooking). As a result, there are many food security indicators (Hoddinott [18] evaluated approximately 450 food security indicators), and it is important to use several indicators to fully estimate food security given its complexity [6, 32]. The plurality of existing indicators and the lack of consensus on how to compare food security situations has led to the development of composite indicators [42] whose construction is not neutral for a policy point of view [43]. It is nowadays recognized than the use of one single indicator gives an incomplete picture of food security [30] and that multiple indicators should be used to capture the complexity of food insecurity [32, 35]. In this paper, three food security indicators are used, who had been obtained at the household level, through surveys. The diversity in the types of food security indicators implies diversity in data collection scales, ranging

<table><caption>**Table 1** Overview of food security indicators at different scales</caption><thead><tr><td>Indicator</td><td>Scale</td></tr></thead><tbody><tr><td>FAO Undernourishment [52]</td><td>National</td></tr><tr><td>Global Hunger Index [20]</td><td>National</td></tr><tr><td>Global Food Security Index [48]</td><td>National</td></tr><tr><td>Food Consumption Score [58]</td><td>Household</td></tr><tr><td>Household Dietary Diversity Score [24]</td><td>Household</td></tr><tr><td>Coping Strategies Index [31]</td><td>Household</td></tr><tr><td>Food expenditures [45]</td><td>Household</td></tr><tr><td>Household Hunger Scale [1]</td><td>Household</td></tr><tr><td>Anthropometric measures [28]</td><td>Individual</td></tr><tr><td>Caloric intake [7]</td><td>Individual</td></tr></tbody></table>

from national and regional scales (country population, country cereal balance sheets, vegetation indexes, rainfall estimations, food prices) to household and individual scales (consumption proxies, caloric intakes and anthropometric measures) (Table 1). Indicators derived from individual observations are considered the most relevant for characterizing food security, but they are very costly, as they imply either measuring consumption directly or measuring health status. Indicators derived from remote sensing imagery may be delivered in a more timely manner and be cheaper than individual-level data, but they do not always match consumption measures well. Falling in between these extremes, indicators generated at the household scale through surveys are good compromises. They are considered a cost-effective means to proxy food consumption, and they have become central to the analysis of food security, as they adequately capture the food access dimension of food security [27]. Most food security indicators are constructed from data collected at the household level, and several indicators used at the regional or national level are derived from household data (through aggregation).

### The Permanent Agricultural Survey data in Burkina Faso

In Burkina Faso, the Permanent Agricultural Survey of rural households has been conducted annually by the Ministry of Agriculture since 1982. This survey is a policy tool used in the field of agriculture and food security that provides decision-makers and food organizations with provincial crop forecasts during the growing period (around August) and with agricultural production estimates during the harvest period (around October). Since 2009, information on household food consumption has also been collected to calculate food security indicators after harvest (around December). Collected data are nationally representative and agricultural production estimates and food security indicators derived from
this official survey are key elements used in national and regional food security warning systems [39]. The Permanent Agricultural Survey provides indeed the only available and nationally representative data set that explores the spatial and temporal dimensions of food security.<fnref n="1" /> The data set used in this paper covers the 2009 to 2017 period and contains information from 41,751 farm households, i.e., an average of 4,640 farm households per year. The number of households varies from year to year. A farm household is defined as a household practicing at least one of the following activities: temporary crop cultivation (either rainfed or irrigated crops), fruit growing, or animal husbandry. Such households are distributed throughout 887 municipalities over 45 provinces and 13 regions. The paper is built upon three indicators that are validated as proxies of the food access dimension of food security [27] and that were obtained through the Permanent Agricultural Survey. The FCS and HDDS food security indicators have been calculated from 2009 to 2017 and the rCSI indicator has been calculated from 2014 to 2017 (information about coping strategies is available as of 2014).

As in any survey, biases can affect the reliability of the data obtained from household surveys. Some authors have shown that since the early 2000s, the quality of survey data has declined, as people are increasingly solicited to participate in surveys and are increasingly concerned about protecting their privacy [33]. The non-observance biases related to the failure to collect information (coverage and sampling bias, non-consent bias, non-response bias) [26] and the measurement bias due to measurement error during collection (related to the investigator, the respondent or the questionnaire) [22] are the two main types of biases. In “Results and discussion” section, some biases and data limitations are discussed based on an analysis of the Permanent Agricultural Survey guidelines produced by the Burkinabe Ministry of Agriculture [39].

### The household food security indicators

This paper focuses on three indicators: the Food Consumption Score (FCS), the Household Dietary Diversity Score (HDDS) and the reduced Coping Strategies Index (rCSI). These indicators furnish useful information on food frequency, quantity and quality and on households' economic access to food, and are some of the indicators

<table><caption>**Table 2** Food groups and corresponding weights used to compute the Food Consumption Score (FCS). Source: Wiesmann et al. [58]</caption><thead><tr><td>Food group</td><td>Weight (nutritional value)</td></tr></thead><tbody><tr><td>Cereals and tubers</td><td>2</td></tr><tr><td>Pulses</td><td>3</td></tr><tr><td>Vegetables and leaves</td><td>1</td></tr><tr><td>Fruits</td><td>1</td></tr><tr><td>Animal proteins</td><td>4</td></tr><tr><td>Dairy products</td><td>4</td></tr><tr><td>Sugars</td><td>0.5</td></tr><tr><td>Oils</td><td>0.5</td></tr><tr><td>Condiments</td><td>0</td></tr></tbody></table>

that are most widely used in the research community by international organizations and governments [21, 32, 51].

Food Consumption Score (FCS): FCS is a measure of nutrient and energy intake. It represents an estimation of the cumulative frequency with which the various food groups are consumed over 7 days in each household surveyed. The frequency of consumption for each food group is weighted by its nutrient value (Eq. 1; Table 2). Thresholds set by the World Food Program are used to differentiate food consumption between households, as follows: acceptable (> 42), limit (28–42), and low (< 28) [58]

$$FCS = \sum_{i=1}^{9} x_i \cdot p_i \qquad (1)$$

$x_i \in \{\text{Frequency of consumption for each food group } i\}, p_i \in \{\text{Weight of food group } i\}$

Household Dietary Diversity Score (HDDS): The HDDS captures the number of food groups consumed in the last 24 h and is considered an acceptable proxy for food consumption [23]. There is no consensus on the number of groups to use and their boundaries [24]. In this paper, the HDDS (Eq. 2) is computed according to FAO guidelines, based on 12 food groups (cereals; roots and tubers; vegetables; fruits; meat products; eggs; fish and seafood; legumes, nuts and seeds; milk and dairy products; oils and fat; sweets; and condiments, spices and beverages; [46]).

$$HDDS = \sum_{i=1}^{12} x_i \qquad (2)$$

$x_i \in \{0: food i is not consumed, 1: food i is consumed\}$

Reduced Coping Strategies Index (rCSI): The rCSI takes into account the severity of the strategies that households

<footnote n="1">¹ Data from the Permanent Agricultural Survey are not publicly available but can be obtained upon request at the Ministry of Agriculture of Burkina Faso. A Living Standard Measurement Survey was led in 2014 in Burkina Faso by the Institut National de Statistique et de Démographie: data are publicly available. Statistical Pearson tests have been led between indicators obtained from the Permanent Agricultural Survey and from the Living Standard Measurement Survey in 2014: Pearson correlations are significantly positive, indicating that results are consistent.</footnote>
<table><caption>**Table 3** Food groups and corresponding weights used to calculated the reduced Coping Strategies Index (rCSI). Source: Maxwell [31]</caption><thead><tr><td>Behavior</td><td>Weight</td></tr></thead><tbody><tr><td>Use less popular and less expensive foods</td><td>1</td></tr><tr><td>Borrow food or seek help from a friend or relative</td><td>2</td></tr><tr><td>Limit the size of portions during meals</td><td>1</td></tr><tr><td>Reduce adult consumption to feed children</td><td>3</td></tr><tr><td>Reduce the number of meals per day</td><td>1</td></tr></tbody></table>

use to cope with deficits in their food consumption. It is an estimate of the cumulative frequency of five potential food reduction strategies used over 7 days within each household surveyed. The frequency of each behavior is weighted by its severity (Eq. 3; Table 3):

$$rCSI = \sum_{i=1}^{5} x_i \cdot p_i \qquad (3)$$

$x_i \in \{\text{Frequency of behavior } i\}, p_i \in \{\text{Behavior weight}\}$

These three food security indicators are calculated for Burkina Faso using the food consumption data that have been collected through the Permanent Agricultural Survey since 2009. These indicators, defined at the households level, are then averaged at the province and region scales. The distribution of the indicators averaged by province is given Fig. 2. In “Consistency between household surveys and other data sources” subsection, they are also centered reduced (i.e., by subtracting the average and dividing by the standard deviation) in relation to provinces and years for comparison with food security proxies.

### Other food security proxies

Other food security proxies that are routinely used in food security alert and monitoring systems have been identified [15], namely, a vegetation index, rainfall estimation, and market food prices:

1. Vegetation index: Moderate resolution imaging spectroradiometer (MODIS) satellite images of the composite Normalized Difference Vegetation Index (NDVI), taken over 16 days at 250 m resolution (MOD13Q1 V6 product) [36]. The S2 prototype land cover map at 20 m for Africa from 2016 [9] is used to mask the pixels that are located outside the cropland.

2. Rainfall: Climate hazards group infrared precipitation with station (CHIRPS) data, with a frequency of 10 days and a resolution of 6 km [5].

<figure><img src="image_4.png" /><figcaption>**Fig. 2** Histograms of annual FCS, HDDS and rCSI distributions from 2014 to 2017, averaged by province</figcaption></figure>

3. Food price: Monthly maize prices by market, provided by the Société Nationale de Gestion du Stock de Sécurité Alimentaire (SONAGESS) in Burkina Faso (personal communication, 2019).

These three proxies are first aggregated monthly (maximum NDVI and the sum of the rainfall and the prices) and by province for the months from May to October (the agricultural period) and are then transformed into deviations (normalized) by being centered reduced in relation to all the provinces and months presented.
<figure><img src="image_5.png" /><figcaption>**Fig. 3** Evolution of the mean annual (a) Food Consumption Score (FCS) and (b) Household Dietary Diversity Score (HDDS) in Burkina Faso (solid line) and in the Est and Boucle du Mouhoun regions (dashed lines) for the 2009–2017 period. Source: Permanent Agricultural Survey data</figcaption></figure>

Finally, the deviations are aggregated into annual mean deviations.

"Consistency between household surveys and other data sources" subsection analyzes the correlations between the normalized food security proxies (rainfall, NDVI and maize price) and the normalized food security indicators computed from the Permanent Agricultural Survey (FCS, HDDS and rCSI), at province level, between 2014 and 2017 (179 observations per indicator).

# Results and discussion

## Analysis of the spatiotemporal variability in food security

This section analyzes and compares the spatiotemporal variations in the FCS, HDDS and rCSI indicators, and discuss these variations in light of the information contained in independent data sources such as food security bulletins and reports.

To illustrate the inter-annual variability in food security in Burkina Faso, the annual values of the FCS and HDDS indicators have been used, calculated from the Permanent Agricultural Survey data over the 2009–2017 period (Fig. 3). The rCSI indicator is not depicted here, as a 4-year period (2014–2017) is too short for temporal analysis. At the national scale, the results of the Permanent Agricultural Survey show an increase in the average FCS and HDDS values between 2009 and 2013, followed by a sharp drop between 2013 and 2017 (resulting in the lowest value of the period in 2017). The two indicators provide different estimates of the prevalence of food insecurity, but depict generally similar food security trends

over time. This confirms previous results where the FCS and HDDS were highly correlated [49].

Since 2009, many shocks have affected food security in Burkina Faso. Three events stand out because of their seriousness and have been widely reported in newspapers and NGOs’ reports: the repeated floods in 2009 and 2010 [3, 37], the severe drought in 2011 that led to a famine [55, 60], and the general deterioration of the security situation that has affected the whole country since 2014 [10], worsening globally each year and degenerating into a food and humanitarian crisis [11]. With regard to the floods of 2009 and 2010, two regions were particularly affected, the Est and Boucle du Mouhoun regions, which had among the highest number of victims in 2009 [3]. Indeed, the data reveal that while Burkina Faso experienced an overall increase in its FCS and HDDS over the period 2009 to 2011, these two regions experienced a decrease in their FCS and HDDS values over at least 1 year between 2010 and 2011 (Fig. 3). The effect of the drought in 2011 is not clearly visible in the Permanent Agricultural Survey data. The HDDS decreased slightly in 2012, while the FCS increased in the same year. This could be explained by the fact that the FCS indicator collected during this household survey does not capture a decrease in caloric intake, which is a key indicator of a food crisis. The rCSI might have captured this effect, but it is only available from 2014 onward. The years 2012 and 2013 were two favorable years in terms of climate, which is reflected in a rise in the HDDS and FCS at the national level. In contrast, 2014 and 2015 were rain deficit years, which is reflected in the decrease in both the HDDS and
FCS. Overall, it seems that both the HDDS and FCS indicators are able to capture the inter-annual variability in food security and notably to gauge the severe decrease of food security that is related to the occurrence of armed conflicts since 2015. In structurally food insecure areas as the eastern and the northern parts of Burkina Faso, the occurrence of violent conflicts had been severely impacting the ability of rural households to feed themselves.

To analyze the spatial variability in food security, three maps have been provided to represent the spatial distribution of the FCS, HDDS and rCSI indicators in Burkina Faso over the 2014–2017 period (Fig. 4). Figure 4a shows the distribution by region of the proportion of households with a low FCS (i.e., below 28). Figure 4b and c shows the rCSI and HDDS distributions by region. There is no cut-off point for determining critical HDDS [19] and rCSI [31] values, and the class thresholds in Fig. 4b and c have been set empirically to illustrate the regional variability. The results show that between 2014 and 2017, the FCS and HDDS were lowest in the center of the country (Centre Nord, Plateau Central and Centre regions) and highest in the southwest (Cascades and Hauts-Bassins regions) and Sahel regions, while the rCSI was most critical in the north and the east of the country (Centre Nord, Sahel and Est regions) and better in the southwest (Cascades and Hauts-Bassins regions).

Several bulletins indicate that the Centre Nord, the Est and the Sahel are the most food insecure regions. Conversely, the regions in which food security is higher are the Hauts-Bassins, Cascades and Centre regions [38, 56, 61]. This directly reflects the agro-climatic conditions that are favorable in southern regions and unfavorable in eastern and northern regions. For most regions, the data obtained from the Permanent Agricultural Survey are consistent with NGOs’ reports: the Cascades and Hauts-Bassins regions present the best HDDS (6.3 and 5.8, respectively) and rCSI (0.72 and 0.73, respectively) values, while the Est and Centre Nord regions have the worst HDDS (4.98 and 4.6, respectively) and rCSI (2.54 and 2.47, respectively) values. In the Sahel region, the results are more contrasting: The region has the worst rCSI score (2.76), which is consistent with NGOs’ reports and the structural classification of this region as being in a food crisis, but has both an HDDS and a FCS higher than the average. An interpretation of this discrepancy could be that the HDDS and FCS take into account food groups such as meat and dairy products that may not be consistent in terms of caloric intake but that are nutritionally important food items. The relatively high levels of HDDS and FCS in the northern regions can be related to cultural food consumption habits. It appears that rCSI indicator is a good proxy for caloric consumption and reflects agro-climatic conditions, northern and eastern

regions being “deficit regions”, where grain production is regularly affected by drought while southern and western regions are “surplus regions”, where production exceeds self consumption. This example shows how crucial it is to use several indicators to explain a phenomenon, and it appears that the HDDS and FCS are less useful for detecting food crises in the Sahel than the rCSI. Finally, the Centre region has critical HDDS and rCSI values (4.83 and 2.57, respectively), which contradicts what appears in the NGO reports [38, 61]. An interpretation of these discrepancies could be that in the Centre region, the population of surveyed farmers is not representative of the total population. Indeed, unlike the other regions

<figure><img src="image_6.png" /></figure>
<table><caption>**Table 4** Correlations between the three annual food security indicators' deviations (FCS, HDDS and rCSI) and the annual mean deviations of NDVI, rainfall and maize prices for the year (Yt) in which the food security indicators were collected and for the previous year (Yt – 1) between 2014 and 2017 (*p*-value < 0.05; **p*-value < 0.01; ***p*-value < 0.001)</caption><thead><tr><th></th><th>NDVI Yt</th><th>Rainfall Yt</th><th>Maize price Yt</th><th>NDVI Yt – 1</th><th>Rainfall Yt – 1</th><th>Maize price Yt – 1</th></tr></thead><tbody><tr><td>FCS</td><td>– 0.02</td><td>0.13</td><td>– 0.16*</td><td>– 0.02</td><td>– 0.02</td><td>– 0.13</td></tr><tr><td>HDDS</td><td>0.16*</td><td>0.30***</td><td>– 0.28***</td><td>0.17*</td><td>0.16*</td><td>– 0.25***</td></tr><tr><td>rCSI</td><td>– 0.21**</td><td>– 0.01</td><td>0.19*</td><td>– 0.20**</td><td>– 0.29***</td><td>– 0.01</td></tr></tbody></table>

where approximately 80% of workers have jobs related to agriculture, the Centre region contains the city capital Ouagadougou and its agglomeration [17]. If the results of the Permanent Agricultural Survey indicate that farmers in the Centre region are food insecure, this finding cannot be generalized to the rest of the population because of undercoverage bias.

Overall, it seems that HDDS, FCS and rCSI relate to different dimensions of food security: rCSI is a valid indicator for detecting food insecurity situations that can arise in specific regions because of a lack of access, while HDDS and FCS are valid indicators to detect food insecurity situations that are characterized by a low quality of the diets, either related to low access or cultural food consumption habits. In such a situation, using more than one indicator is advisable, as each indicator portrays different aspects of food insecurity. While the FCS and HDDS tend to indicate situations of low nutrient density diets, the rCSI tends to indicate situations of low caloric intake.

## Consistency between household surveys and other data sources

In this section, the correlations between the three food security indicators' annual deviations (FCS, HDDS and rCSI) and indirect food security proxy variables are analyzed. The proxy variables used are the annual mean deviations of NDVI, rainfall and maize prices calculated for the year in which the food security indicators were collected and for the previous year (Table 4). The food security indicators obtained from the household surveys are globally poorly correlated with the proxies that represent only one aspect of food insecurity, with a maximum correlation of 0.3.

The NDVI correlates positively with the HDDS and negatively with the rCSI, which confirms the expected effect that good crop conditions generally lead to high crop production and thus to potential agricultural incomes (stored grains or marketed surplus), resulting in higher caloric intakes (no need to implement food reduction strategies, so rCSI decreases) and increased food consumption (possibility of eating more diverse foods, so HDDS increases). However, a high level of food

consumption is not equivalent to having a diet with high nutritional value because the correlation between NDVI and the FCS is not significant. This could be interpreted as farm households having more diverse diets but that this diversity may be related to the consumption of food items that are not beneficial from a nutritional point of view, such as oils, sugar and drinks.

Rainfall correlates positively with the HDDS and the correlation with the FCS and the rCSI are not significant. The interpretation is the same as that provided for the NDVI (higher crop production, fewer food reduction strategies, increased food consumption, but not necessarily more nutritious consumption).

Maize prices correlate negatively with the HDDS and positively with the rCSI. Considering that most farm households are net buyers (they produce less maize than what they consume, and thus they have to buy part of their maize consumption from the market), this confirms the intuition that an increase in maize prices is detrimental to food security because households have lower economic access to maize and are obliged to reduce their consumption of grains and of other food items (increase in rCSI and decrease in HDDS).

Overall, it appears that the FCS has very little correlation with the indirect proxy variables of food security and that the HDDS and rCSI are more strongly correlated with the indirect proxy variables. The correlations of the HDDS and rCSI with the indirect proxy variables for the same year are significant (5 out of 6 significant correlations) but are also comparable to the indirect proxy variables of the previous year (5 out of 6 significant correlations). This indicates that food security is linked not only to the current climatic and economic context but also to elements of the past climatic and economic context that have an impact on food security. The comparison results are similar with normalized or non-normalized food security indicators for comparison to proxies deviations. One result is thus that there are concordances between the indicators and proxies of food security but that these concordances are limited, probably because food security consists of a complex combination of these proxies that cannot be fully captured through correlation calculations. One alternative
explanation could be that our data set does not enable
any seasonal analysis of what is going on within a year.
Food consumption can differ widely between the har-
vest season and the lean season, but data are lacking to
appraise the intra-annual evolution of food security situ-
ations. This could be further investigated in the future.

Conclusion

This paper analyzes the contribution of household sur-
veys to our understanding of food security situations.
Previous studies have established that indicators derived
from household surveys are reliable in qualifying food
consumption and are cheaper to obtain than individual
consumption indicators [27, 32]. Our study shows that
indicators derived from household surveys contain con-
sistent spatial (structural) and inter-annual (conjunc-
tural) information and that they can be used to monitor
food crises at the sub-national scale despite their inher-
ent biases. In particular, we show that household surveys
provide information on food security that permits the
identification of key trends that are consistent with food
security warning systems, as well as with climatic and
economic variables related to food security.

However, our study also highlights three drawbacks
related to the use of indicators derived from household
surveys: the first one is that it takes time to calculate indi-
cators from household surveys; the second one is that
in the context of armed conflicts, it can be risky to con-
duct household surveys; and the third one is that indica-
tors from household surveys provide only a partial view
of food security situations. The fact that the process is
time consuming is related to the definition of the sam-
pling strategy, to the design of the questionnaire and its
administration across a considerable number of house-
holds, and finally to the data entry, cleaning and analysis
by experts. On the opposite, data on crop conditions and
rainfall obtained from remote sensing imagery can be
obtained more quickly, which is a major advantage. The
fact that collecting data within households in conflict
affected areas can be hazardous is clearly an argument
in favor of mobilizing other data sources to predict food
security situations. The fact that indicators derived from
household surveys provide only a partial view is a draw-
back common to the use of any indicator. Variations in
food security indicators correlate only partially with the
climatic and economic factors that are considered to be
indirect proxies of food security. Each indicator or proxy
tends to focus on a specific component of food security,
and the choice to consider several complementary indi-
cators and proxies provides a more complete picture of
food security situations.

In summary, if household surveys are a reliable tool for
the characterization of food security situations and their

spatial and temporal diversity, such surveys provide only
a partial understanding of food security and have to be
combined with other data sources that may not directly
measure food consumption but rather its determinants.
A major task is to understand how climatic, agronomic,
economic, policy-related, and security-related events
are interlinked and cause food insecurity at a given time
and in a given place. This is a huge challenge given the
complexity of food security. One way to address such
challenging tasks would be to resort to data science tech-
niques, and notably machine learning algorithms which
are being increasingly used to extract relevant informa-
tion from complex and heterogeneous data in the field
of food security [29, 53]. This type of research could
contribute to food security warning and monitoring sys-
tems by detecting famines more quickly and accurately.
The use of multiple data sources to generate knowledge
on local food security situations is of special importance
in the contexts where information in the ground may be
lacking because of armed conflicts, which are nowadays
considered as the primary driver of ongoing food crises
in the world (72% of the people facing acute food inse-
curty in 2021 are living in conflict-affected countries<fnref n="2" />).
Indeed, the conduction of household surveys could be
a risky activity in armed conflict contexts, so that food
security monitoring systems should encourage analysis
combining heterogeneous data, obtained from different
sources and processed through machine learning.

Acknowledgements

This study was conducted with the help of the Ministry of Agriculture, Water Resources, Sanitation and Food Security of Burkina Faso and the Société Nationale de Gestion du Stock de Sécurité Alimentaire (SONAGESS), which provided data.

Author contributions

Conceptualization: HD, AB, EMH, data curation: HD, formal analysis: HD, AB, EMH, funding acquisition: AB, MR, methodology: all authors, validation: HD, AB, EMH, writing—original draft: HD, writing—review and editing: all authors reviewed the manuscript. All authors read and approved the final manuscript.

Funding

This work was supported by the French National Research Agency under the Investments for the Future Program #DigitAg, referred to as ANR-16-CONV-0004.

Availability of data and materials

Not applicable (no open access data).

Declarations

Ethics approval and consent to participate

The authors confirm compliance with the ethical policies of the journal.

Consent for publication

We confirm that the manuscript has been read and approved by all named
authors.

<footnote n="2">² https://www.fsinplatform.org/sites/default/files/resources/files/GRFC%202022%20KM%20ENG%20ARTWORK.pdf.</footnote>
Competing interests

The authors declare no conflicts of interest.

Author details

¹TETIS, AgroParisTech, CIRAD, CNRS, INRAE, Univ Montpellier, Montpellier, France. ²MOISA, CIHEAM-IAMM, CIRAD, INRAE, Institut Agro, Univ Montpellier, Montpellier, France. ³CIRAD, UMR TETIS, Montpellier, France. ⁴CIRAD, UMR MOISA, Montpellier, France. ⁵INRAE, UMR TETIS, Montpellier, France. ⁶Ministère de l'Agriculture, Ouagadougou, Burkina Faso.

Received: 7 March 2022 Accepted: 21 November 2022

Published online: 02 February 2023

References

1. Ballard T. Household hunger scale: indicator definition and measurement guide. Food and Nutrition Technical Assistance (FANTA). 2011.

2. Biemer PP. Total survey error: design, implementation, and evaluation. Public Opin Q. 2010;74:817–48.

3. Burkina Faso Government. Inondations du 1er Septembre 2009 au Burkina Faso. Evaluation des dommages, pertes et besoins de construction, de reconstruction et de relèvement. Burkina Faso Government, FAO. 2009.

4. Carletto C, Zezza A, Banerjee R. Towards better measurement of household food security: harmonizing indicators and the role of household surveys. Glob Food Secur. 2013;2:30–40.

5. CHIRPS. Description and data of Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) product. 2020. https://www.chc.ucsb.edu/data/chirps. Accessed 01 Apr 2020.

6. Coates J. Build it back better: deconstructing food security for improved measurement and action. Glob Food Secur. 2013;2:188–94.

7. De Araujo ML, de Deus Mendonça R, Lopes Filho JD, Souza Lopes AC. Association between food insecurity and food intake. Nutrition. 2018;54:54–9.

8. Dussaix A-M. La qualité dans les enquêtes. Modulad. 2009;39:137–71.

9. ESA. Description and data of S2 prototype Land Cover map at 20m of Africa 2016. 2020. http://2016africalandcover20m.esrin.esa.int/. Accessed 01 Apr 2020.

10. FAO. Vue d'ensemble régionale de la sécurité alimentaire et la nutrition. Le lien entre les conflits et la sécurité alimentaire et la nutrition: renforcer la résilience pour sécurité alimentaire, la nutrition et la paix. FAO. 2017.

11. FAO. Burkina Faso: Aperçu de la réponse - juillet 2019. Rome: FAO; 2019.

12. FAO and ECA. Addressing the threat from climate variability and extremes for food security and nutrition. Rome: FAO; 2018. ISBN 978-92-5-131157-8.

13. FAO, FIDA, OMS, WFP, and UNICEF. L'état de la sécurité alimentaire et de la nutrition dans le monde en 2018: renforcer la Résilience face aux changements climatiques pour La sécurité alimentaire et la nutrition. Rome: FAO; 2018. ISBN 978-92-5-130840-0.

14. FAO, FIDA, OMS, WFP, and UNICEF. The State of Food Security and Nutrition in the World—Transforming Food Systems for affordable healthy diets. Technical report, FAO, 2020.

15. Fritz S, See L, Bayas JCL. A comparison of global agricultural monitoring systems and current gaps. Agric Syst. 2018;168:258–72.

16. Headey D, Ecker O. Rethinking the measurement of food security: from first principles to best practice. Food Secur. 2013;5(3):327–43.

17. Herrera R, Ilboudo L. Les défis de l'agriculture paysanne: Le cas du burkina faso. Paris: L'Harmatta; 2012. ISBN 978-23-3-600449-5.

18. Hoddinott J. Choosing outcome indicators of household food security, Vol. Technical guide no 7. Washington: International Food Policy Research Institute; 1999.

19. INDDEX Project. Data4Diets: building blocks for diet-related food security analysis. Boston: Tufts University; 2018.

20. International Food Policy Research Institute. The concepts of the Global Hunger Index. Washington: International Food Policy Research Institute; 2017.

21. Jones AD, Gurney FM, Pelto G, Young SL. What are we assessing when we measure food security? A compendium and review of current metrics. Adv Nutr. 2013;4:481–505.

22. Kasprzyk D. Measurement error in household surveys: sources and measurement. Washington: United States Federal Committee on Statistical Methodology; 2001.

23. Kennedy G, Berardo A, Papavero C, Horjus P, Ballard T, Dop MC, Delbaere J, Brouwer ID. Proxy measures of household food consumption for food security assessment and surveillance: comparison of the household dietary diversity and food consumption scores. Public Health Nutr. 2010;13(12):2010–8.

24. Kennedy G, Ballard T, Dop M-C. Guide pour mesurer la diversité alimentaire au niveau du ménage et de l'individu. Technical report. FAO; 2013.

25. Lacher W. Organized crime and conflict in the sahel-sahara region. Technical report. Carnegie Endowment for International Peace; 2012.

26. Lepkowski JM. Non-observation error in household surveys in developing countries. Washington: United States Federal Committee on Statistical Methodology; 2001.

27. Leroy JL, Ruel M, Frongillo EA, Harris J, Ballard T. Measuring the food access dimension of food security: a critical review and mapping of indicators. Food Nutr Bull. 2015;36(2):167–95. https://doi.org/10.1177/0379572115587274.

28. Leyna GH, Mmbaga EJ, Mnyika KS, Akhtar H, Knut Inge K. Food insecurity is associated with food consumption patterns and anthropometric measures but not serum micronutrient levels in adults in rural Tanzania. Public Health Nutr. 2010;13:1438–44.

29. Martini G, Bracci A, Riches L, Jaiswal S, Corea M, Rivers J, Husain A, Omd dei E. Machine learning can guide food security efforts when primary data are not available. Nat Food. 2022;3:716–28.

30. Matavel C, Hoffmann H, Rybak C, Steinke J, Sieber S, Müller K. Understanding the drivers of food security among agriculture-based households in Gurué district, Central Mozambique. Agric Food Secur. 2022;11(1):1–15.

31. Maxwell D: The Coping Strategies Index; A tool for measurement of household food security and the impact of aid programs in humanitarian emergency; Field Method Manual. Boston: Feinstein International Center; 2008.

32. Maxwell D, Vaitla B, Coates J. How do indicators of household food insecurity measure up? an empirical comparison from Ethiopia. Food Policy. 2014;47:107–16. https://doi.org/10.1016/j.foodpol.2014.04.003.

33. Meyer BD, Mok WKC, Sullivan JX. Household surveys in crisis. J Econ Perspect. 2015;29:199–226.

34. Moltedo A, Troubat N, Lokshin M, Zurab S. Analyzing Food security using household survey data: streamlined analysis with ADePT Software. Washington: World Bank; 2014. ISBN 978-14-6-480140-2.

35. Moroda GT, Tolossa D, Semie N. Food insecurity of rural households in Boset district of Ethiopia: a suite of indicators analysis. Agric Food Secur. 2018;7(1):1–16.

36. NASA. Description and data of MODerate resolution Imaging Spectroradiometer (MODIS) satellite images of composite Normalized Difference Vegetation Index (NDVI) (MOD13Q1) product. 2020. https://lpdaac.usgs.gov/products/mod13q1v006/. Accessed 01 Apr 2020.

37. OCHA. Burkina Faso; Inondations; période du 1er au 30 septembre 2010. New York: United Nations Office for the Coordination of Humanitarian Affairs (OCHA); 2010.

38. OCHA. Plan de réponse stratégique - Burkina Faso. New York: United Nations Office for the Coordination of Humanitarian Affairs (OCHA); 2015.

39. Permanent Agricultural Survey. Résultats Définitives de la Campagne Agricole 2014/2015 et Perspectives de la Situation Alimentaire et Nutritionnelle. Ministère de l'Agriculture, des Ressources Hydrauliques, de l'Assainissement et la Sécurité Alimentaire. 2015.

40. Pérez-Escamilla R, Segall-Corrêta AM. Food insecurity measurement and indicators. Revista de Nutricão. 2008;21:15s–26s.

41. Rautela G, Ali MK, Prabhakaran D, Venkat Narayan KM, Tandon N, Mohan V, Jaacks LM. Prevalence and correlates of household food insecurity in Delhi and Chennai, India. Food Secur. 2020;12:1–14.

42. Santeramo FG. Food security composite indices: implications for policy and practice. Dev Pract. 2015;25(4):594–600. https://doi.org/10.1080/09614524.2015.1029439.

43. Santeramo FG. On the composite indicators for food security: decisions matter! Food Rev Int. 2015;31(1):63–73. https://doi.org/10.1080/87559129.2014.961076.
44. Shaw DJ. World food security: a history since 1945, vol. 1. London: Palgrave MacMillan; 2007. (ISBN 10: 0230553559).

45. Smith L, Subandoro A. Measuring food security using household expenditure surveys. Washington: International food policy Research Institute (IFPRI); 2007. ISBN 978-08-9-629767-8.

46. Swindale A, Bilinsky P. Household Dietary Diversity Score (HDDS) for measurement of household food access: indicator guide. Washington: Food and Nutrition Technical Assistance (FANTA); 2006.

47. Tapsoba A, Combes Motel P, Combes J. Remittances, food security and climate variability: the case of Burkina Faso. HAL. 2019.

48. The Economist Intelligence Unit. Global Food Security Index 2018. London: The Economist Intelligence Unit; 2018.

49. Tiwari S, Skoufias E, Sherpa M. Shorter, cheaper, quicker, better: linking measures of household food security to nutritional outcomes in Bangladesh, Nepal, Pakistan, Uganda, and Tanzania. World Bank policy research working paper, (6584), 2013.

50. Tuholske C, Andam K, Blekking J, Evans T, Caylor K. Comparing measures of urban food security in Accra, Ghana. Food Secur. 2020;12:1299–316.

51. Vhurumuku E. Food security indicators—WFP. In: Integrating Nutrition and Food Security Programming for Emergency response workshop. 2014.

52. Wanner N, Cafiero C, Troubat N, Conforti P. Refinements to the FAO methodology for estimating the prevalence of undernourishment indicator. Rome: FAO; 2014.

53. Westerveld JJL, van den Homberg MJC, Nobre GG, van den Berg DLJ, Teklesadik AD, Stuit SM. Forecasting transitions in the state of food security with machine learning using transferable features. Sci Tot Environ. 2021;786:147366. https://doi.org/10.1016/j.scitotenv.2021.147366.

54. WFP. Comprehensive food security and vulnerability analysis guidelines. Technical. Rome: WFP; 2009.

55. WFP. Burkina Faso: Avril 2012: Rapport d'évaluation approfondie sur la sécurité alimentaire des ménages en situation d'urgence (EFSA) dans 170 communes déclarées à risque d'insécurité alimentaire. Rome: WFP; 2012.

56. WFP. Burkina Faso: Analyse Globale de la Vulnérabilité, de la Sécurité Alimentaire et de la Nutrition. Rome: WFP; 2014.

57. Wichern J, van Heerwaarden J, de Bruin S, Descheemaeker K, van Asten PJA, Giller KE, van Wijk MT. Using household survey data to identify large-scale food security patterns across Uganda. PLoS ONE. 2018;13(12): e0208714.

58. Wiesmann D, Bassett L, Benson T, Hoddinott J. Validation of the world food programme's food consumption score and alternative indicators of household food security. Washington: International Food Policy Research Institute (IFPRI); 2009.

59. Winter J. Response bias in survey based measures of household consumption. Econ Bull. 2004;3:1–12.

60. World Bank. Sahel drought situation report no. 10. Washington: World Bank; 2012.

61. Zida Y, Kambou SH. Cartographie de la pauvreté et des inegalités au Burkina Faso. New York: Programme des Nations Unies pour le Développement (PNUD); 2014.

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