a systematic literature review of indicators measuring food security

• Open access
• Published: 05 May 2023

A systematic literature review of indicators measuring food security

• Ioannis Manikas 1 ,
• Beshir M. Ali   ORCID: orcid.org/0000-0002-5865-8468 1 &
• Balan Sundarakani 1  

Agriculture & Food Security volume  12 , Article number:  10 ( 2023 ) Cite this article

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Measurement is critical for assessing and monitoring food security. Yet, it is difficult to comprehend which food security dimensions, components, and levels the numerous available indicators reflect. We thus conducted a systematic literature review to analyse the scientific evidence on these indicators to comprehend the food security dimensions and components covered, intended purpose, level of analysis, data requirements, and recent developments and concepts applied in food security measurement. Data analysis of 78 articles shows that the household-level calorie adequacy indicator is the most frequently used (22%) as a sole measure of food security. The dietary diversity-based (44%) and experience-based (40%) indicators also find frequent use. The food utilisation (13%) and stability (18%) dimensions were seldom captured when measuring food security, and only three of the retrieved publications measured food security by considering all the four food security dimensions. The majority of the studies that applied calorie adequacy and dietary diversity-based indicators employed secondary data whereas most of the studies that applied experience-based indicators employed primary data, suggesting the convenience of collecting data for experience-based indicators than dietary-based indicators. We confirm that the estimation of complementary food security indicators consistently over time can help capture the different food security dimensions and components, and experience-based indicators are more suitable for rapid food security assessments. We suggest practitioners to integrate food consumption and anthropometry data in regular household living standard surveys for more comprehensive food security analysis. The results of this study can be used by food security stakeholders such as governments, practitioners and academics for briefs, teaching, as well as policy-related interventions and evaluations.

Introduction

Providing sufficient, affordable, nutritious, and safe food for the growing global population remains a challenge for human society; this task is made further difficult when governments are expected to provide food security without causing climate change, degrading water and land resources, and eroding biodiversity [ 1 ]. As long as food self-sufficiency and citizens’ wellbeing depend on sustainable food security, food security will remain a global priority [ 2 , 3 ]. According to the 1996 World Food Summit definition, food security is achieved ‘when all people, at all times, have physical and economic access to sufficient, safe and nutritious food to meet their dietary needs and food preferences for an active and healthy life’ [ 4 ].

This definition by the Food and Agriculture Organization has laid the foundation for the four food security dimensions [ 5 ]: availability , access , utilisation , and stability . Relatedly, any kind of food security analysis, programme, and monitoring, with respect to predefined targets, requires valid and reliable food security measurement. However, measuring such a non-observable concept as a latent construct has remained challenging because of its complex and evolving nature: it has many dimensions and components [ 6 ], and involves a continuum of situations , invalidating the application of dichotomous/binary measures [ 7 ]. Food security measurement poses two fundamental yet distinct problems [ 8 ]: determining what is being measured and how it is measured . The what question refers to the use of appropriate indicators for the different dimensions (availability, access, utilisation, and stability) and components (quantity, quality, safety, and cultural acceptability/preference), while the how question refers to the methodology applied for computing the indicators (i.e. data, methods, and models).

Scholars have proposed a variety of indicators to measure food security. Over this time, the definition and operational concept of food security has changed as well, and, with it, the type of indicators and methodologies used to gauge it. One such important change is the paradigm shift ‘from the global and the national to the household and the individual, from a food-first perspective to a livelihood perspective, and from objective indicators to subjective perception’ [ 6 ]. Despite the call to harmonize measurements for better coordination and partnerships, to date, there remains no consensus among governments, quasi-legal agencies, or researchers on the indicators and methodologies that should be applied for measuring and monitoring food security at global, national, household, and individual levels [ 9 ]. Instead, an overabundance of indicators makes it difficult to ascertain which indicators reflect which dimensions (availability, access, utilization, or stability), components (quantity, quality, safety, cultural acceptability/preferences), and levels (global, national, regional, household or individual) of food security [ 10 ]. The number of food security dimensions or components assessed also greatly vary in the literature. Indicators that assess only a specific dimension or component oversimplify the outcomes and do not reveal the full extent of food insecurity, for example. Although such highly specific indicators do help conceptualise and reveal food insecurity, they still fail to accurately show trade-offs among the different dimensions, components, and intervention strategies. There is ultimately a possibility of shifting the food insecurity problem from one dimension/component to another.

The practical limitations of existing food security measurements were once again exposed by 2019 coronavirus pandemic (COVID-19), the Scientific Group for the United Nations Food Systems Summit [ 11 ] that ‘the world does not have a singular source of information to provide real-time assessments of people facing acute food insecurity with the geographic scale to cover any country of concern, the ability to update forecasts frequently and consistently in near real-time’. They further stated that current early warning systems lack suitable indicators to monitor the degradation of food systems. Aggravating this problem, these measurement indicators are not standardised, making comparisons among indicators over space and time complicated [ 9 ]. First, some of the indicators are composite indicators measuring two or more food security dimensions, whereas others measure individual dimensions. Second, some of the indicators focus on factors contributing to food security than on food security outcomes. Third, some indicators are quantitative, whereas others are qualitative measures based on individuals’ perceptions. Fourth, the levels of analysis greatly vary as well because some indicators are global and national measures, whereas others are household and individual measures. Fifth, the intended purposes of the indicators range from advocacy tools to monitoring and evaluating progress towards defined policy targets.

Although numerous food security indicators have been developed for use in research, there is no agreement on the single ‘best’ food security indicator among scientists or practitioners for measuring, analysing, and monitoring food security [ 12 , 9 ]. The different international agencies also use their own sets of food security indicators (e.g. World Food Programme: Food Consumption Score (FCS), United States Agency for International Development (USAID): Household Food Insecurity Access Scale (HFIAS); FAO: Prevalence of Undernourishment (POU) and Food Insecurity Experience Scale (FIES); and Economic Intelligence Unit (EIU): Global Food Security Index (GFSI)). An ideal food security indicator should capture all the four food security dimensions at individual level (rather than at national or regional or household levels) to reflect the 1996 World Food Summit definition of food security. However, most of the available indicators are measures of food access at the household level. Footnote 1 In practical use, only a few indicators that ‘satisfactorily capture each requisite dimension of food security and that are relatively easy to collect can be identified and adopted at little detriment to a broader agenda’ [ 9 ], which we attempt herein. In the light of the foregoing discussion, the main objective of this study was to critically review food security indicators and methodologies published in scientific articles using systematic literature review (SLR). The specific objectives were as follows:

To identify and characterize food security indicators with respect to dimensions and components covered, methods and models of measurement, level of analysis, data requirements and sources, intended purpose of application, and strengths and weaknesses;

To review and summarise the scientific articles published since the last decade by the indicators used, intended purpose, level of analysis, study region/country, and data source;

To quantitatively characterize the food security dimensions and components covered in the literature, and to review scientific articles that measured all the four food security dimensions; and

To identify and review recent developments and concepts applied in food security measurement.

Although there exist a few review studies on food security measurement in the literature (e.g. [ 8 , 10 , 13 , 14 , 15 ], the present study is more comprehensive as it covers a wide range of food security indicators, levels of measurement, and analysis of data requirements and sources. Moreover, unlike the existing review studies in the literature, the current study applies the SLR methodology to the analysis of food security indicators and measurement.

Review methodology

We followed a two-stage approach in this review. First, we identified the commonly used food security indicators based on recent (review) articles on food security measurement [ 8 , 9 , 10 , 14 , 15 ]. Using the retrieved information from these articles (and their references), the identified indicators were characterised (in terms of the dimensions and components covered, methods of measurement, level of analysis, intended uses, validity and reliability, and data requirements and sources). Tables 1 , 2 , 3 , 4 present the summary of the characterisation of the identified food security indicators: experience-based indicators (Table 1 ), national-level indicators (Table 2 ), dietary intake, diversity and expenditure-based indicators (Table 3 ), and indicators reflecting coping strategies and anthropometry measures (Table 4 ). This first-stage analysis was used to address the first objective of the study. In the second stage, the SLR was conducted.

Literature searching and screening processes

We applied the SLR methodology to systematically search, filter, and analyse scientific articles on food security measurement. The SLR is a commonly applied and accepted research methodology in the literature [ 39 ]. Although the SLR methodology is widely applied in different disciplines such as the health and life sciences, its application in economics is limited. However, it has recently been applied in agricultural economics (e.g. [ 40 – 43 ]. In this study, we closely followed the six steps of a systematic review process [ 39 ], namely, (a) defining research questions, (b) formulating search strings, (c) filtering studies based on inclusion and exclusion criteria, (d) conducting quality assessment of the filtered studies, (e) collecting data from the studies that passed quality assessment, and (f) analysing the data. The literature screening process that we followed is also in line with the guidelines in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Statement (PRISMA) [ 44 ].

The bibliographic databases of Scopus and Web of Science (WoS) were used to search scientific articles on food security measurement (i.e. indicators, data, and methods) and help us answer the research question ‘How has food in/security been measured in the literature?’ Two categories of search strings were applied: One focussing on food security indicators ( Category A ), and another one on data requirement and sources of food security measurement ( Category B ). Specifically, the search strings (“food security” OR “food insecurity” OR “food availability” OR “food affordability” OR “food access” OR “food utilization” OR “food utilisation” OR “food stability” OR “nutrition security” OR “nutrition insecurity”) AND (“measurement” OR “indicators” OR “metrics” OR “index” OR “assessment” OR “scales”) were used for Category A . For Category B , we used (“food security” OR “food insecurity” OR “food availability” OR “food affordability” OR “food access” OR “food utilization” OR “food utilisation” OR “food stability” OR “nutrition security” OR “nutrition insecurity”) AND (“data” OR “big data” OR “datasets” OR “survey” OR “questionnaire”). The retrieved articles together with some of the inclusion and exclusion criteria, and the number of retrieved articles at each step, are presented in Fig.  1 . The following inclusion and exclusion criteria were also used during the literature searching and screening process in addition to those criteria presented in Fig.  1 : (a) Search field: title–abstract–keywords (Scopus); topic (WoS), (b) Time frame: 2010–09/03/2021, (c) Language: English, (d) Field of research: Agricultural and Biological Sciences Footnote 2 ; Economics, Econometrics and Finance (Scopus); Agricultural Economics Policy; Food Sciences Technology (WoS), and (e) Type: journal articles ( Category A ); journal articles, data, survey, database ( Category B ). We limited our literature search to publications from 2010 onwards since it was during this period that due attention has been given to the harmonisation of food security measurement. Footnote 3 This was also evident from the 2013 special issue of Global Food Security journal on the theme Measuring Food and Nutrition Security . Footnote 4

Literature searching and screening criteria

As we noted above, an ideal food security indicator should capture all the four food security dimensions at individual level to reflect the 1996 World Food Summit definition of food security. We reviewed only those articles that have explicitly measured food in/security by applying at least one food security indicator. These indicators, measuring at least one of the four food security dimensions, were identified based on recent (review) articles on food security measurement [ 8 , 9 , 10 , 14 , 15 ]. A total of 110 articles were selected for full content review after the pre-screening process based on title, keyword and abstract review (Fig.  1 ). After the full content review, 32 articles were further excluded. Fourteen of these were excluded, as they did not measure food security explicitly (e.g. [ 45 , 46 ] or the food security indicator/method of measurement was not described (e.g. [ 47 ] or they used ‘inappropriate’ indicators that do not capture at least one of the four food security dimensions (e.g. [ 48 ]. For example, Koren and Bagozzi [ 48 ] used per capita cropland as a food security measure, which is not a valid indicator for the multidimensional food security concept (it cannot even fully capture the food availability dimension). Thirteen publications that we classified as methodological, two review articles [ 49 , 50 ], and three articles on seed insecurity [ 51 ], marine food insecurity [ 52 ] and political economy of food security [ 53 ] were also excluded. Finally, we reviewed, analysed, and summarised the scientific evidence of 78 articles on food security measurement (see Additional file 1  for the list of the articles and the data). The validity and reliability of the SLR have been ensured by specifying the SLR setting following Kitchenham et al. [ 39 ], and by providing sufficient information regarding the literature extraction and screening processes. Moreover, the three authors have double-checked the correctness of the processes such as definitions of search strings and inclusion–exclusion criteria, and confirming the retrieved data and data interpretation to reduce bias. The limitations of the study are also discussed (see under the “ Discussion ” section).

Review of articles by region, indicators used, intended purpose, and level of analysis

Following the exclusion of the non-pertinent articles (Fig.  1 ), 78 articles were included in our food security measurement dataset for the analysis (Additional file 1 ). Relatively, more publications were retrieved from the years 2019 and 2020 whereas there were no articles from 2010. Footnote 5 The journals of Food Security (33%) and Food Policy (14%) are the main sources of the retrieved articles (Fig.  2 ). The journals in the field of agricultural economics are also important sources of the retrieved articles (15%). Figure  3 depicts the distribution of the retrieved articles by region/country of study focus. Sub-Sahara Africa has been the main focus of the studies, followed by Asia. At country level, USA (8 studies) and Ethiopia (7 studies) were the most studied countries. Besides the studies represented in Fig.  3 , we identified nine other studies focusing at global and regional levels: global [ 7 , 12 , 54 , 55 ], developing countries (Slimane et al. [ 56 ]), Middle East and North Africa (MENA) region [ 57 ], Latin America and Caribbean [ 58 ], and Sub Sahara Africa [ 59 , 23 ]. Despite food insecurity being a global issue, there is lack of studies covering the different parts of the world (e.g. MENA region, Latin America and Europe).

Number of articles per journal (total number of articles: 78)

Summary of articles by country (Note: Some articles focus on more than one country, resulting in 89 articles by study area)

Figure  4 shows the summary of the number of articles by the type of food security indicator that they applied. Seventeen articles applied the household-level calorie adequacy (i.e. undernourishment) indicator, making it the most frequently used one. This indicator measures calorie availability relative to the calorie requirement of the household by accounting for age and sex differences of the household members (note that this indicator is different from FAO’s Prevalence of Undernourishment (POU) indicator (Table 2 ; [ 13 ]). A household is considered as food insecure if the available calorie is lower than the household’s calorie requirement. This indicator has been used in the literature to assess the prevalence of food insecurity [ 35 , 36 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 ], for programme evaluation [ 68 , 66 ], and to analyse food security determinants [ 35 , 60 , 66 , 67 , 69 , 70 , 71 ]. Some studies addressed the main drawback of the calorie adequacy indicator (its failure to account for diet quality) by measuring both calorie and micronutrient adequacy [ 54 , 65 , 70 , 72 ].

Summary of the publications by the type of food security indicators employed

Out of the 17 studies that applied the calorie adequacy indicator, three articles [ 35 , 69 , 71 ] classified households into food secure and food insecure based on the amount of expenditure on food that is required to purchase the minimum caloric requirement. A household is classified as food insecure if the expenditure on food is less than the predetermined threshold amount required for achieving the minimum caloric requirement. This measure allows us to account for the effect of food price inflation on household’s food access.

A subjective (self-reported) version of the household calorie adequacy indicator, the Food Adequacy Questionnaire (FAQ), was also used in 4 of the 78 articles (Fig.  4 ). Tambo et al. [ 73 ] and Smith and Frankenberger [ 74 ] measured food insecurity as the number of months of inadequate food provisioning during the last year owing to lack of resources. Bakhtsiyarava et al. [ 75 ] used FAQ to derive a binary measure of food security based on self-reported shortage of food in the last year, whereas Verpoorten et al. [ 23 ] measured food security using the question ‘Over the past year, how often, if ever, have you or anyone in your family gone without enough food to eat? Never/Just once or twice/Several times/Many times/Always’. Although these simple food security measures based on FAQ can usefully capture a household’s experience of food insecurity and for conducting preliminary assessments, they are prone to subjective biases [ 24 ]. A comparison of studies is complicated because FAQ’s measures are not standardised (e.g. differences in phrases and scales used in the questions).

The dietary diversity indicators Household Diet Diversity Score (HDDS), Women Diet Diversity Score (WDDS), Individual Diet Diversity Score (IDDS), and Food Consumption Score (FCS) were also frequently used in the literature (Fig.  4 ). About 44% of the publications used diet diversity indicators for measuring food security. (Additional file 2 : Tables S1, S2) summarise the studies that applied the dietary diversity score measures (HDDS, WDDS, IDDS) and FCS. Most of the studies applied the diversity score indicators for estimating food insecurity prevalence (Additional file 2 : Table S1). Bakhtsiyarava et al. [ 75 ], Bolarinwa et al. [ 76 ], Islam et al. [ 77 ], and Sibhatu and Qaim [ 78 ] applied HDDS when analysing the determinants of food security. Tambo et al. [ 73 ] and Islam et al. [ 68 ] used HDDS as a measure of food security for program evaluation.

The main weakness of the dietary diversity measures is that they do not account for the quantity and quality of the consumed diet (nutritional value); for instance, consumption of very small quantities of certain foods would raise the diversity score without contributing much to a household’s/individual’s nutritional and micronutrient supply [ 78 ]. HDDS does not also account for intra-household diet diversity. Thus, a higher diet diversity score does not necessarily mean a better household/individual food security. Most of the retrieved articles addressed these drawbacks by combining diversity measures with other food security indicators (Additional file 2 : Table S1). For example, Sibhatu and Qaim [ 78 ] applied HDDS and WDDS in combination with measures of calorie and micronutrient adequacy. Tambo et al. [ 73 ] combined HDDS and WDDS with the Food Insecurity Experience Scale (FIES) and FAQ, whereas Bolarinwa et al. [ 76 ] integrated HDDS and per capita food expenditure.

There is also a difference in the literature regarding the recall period used when measuring dietary diversity, namely, 7 days vs 24 h (Additional file 2 : Table S1). A 7 day recall period leads to higher diversity scores than a 24 h recall period because it considers the daily variation in food consumption [ 78 ]. Although the 7 day recall period is associated with higher respondent bias, conclusions drawn from a 24 h recall period may also be misleading, as some relevant food groups might not be considered in the food security assessment (e.g. livestock products that food insecure households seldom consume daily) [ 78 ]. It is therefore important to consider the differences in recall periods when designing measurement.

About 57% of the studies that employed FCS (Additional file 2 : Table S2) used it to estimate food insecurity prevalence [ 36 , 65 , 70 , 71 ,, 79 , 80 , 81 , 83 , 84 ]. Four other studies applied FCS to analyse the determinants of food security [ 85 – 88 ], whereas two used it for impact evaluation [ 89 , 90 ].

D'Souza and Jolliffe [ 85 ] showed how applying two different food security indicators (per capita daily caloric intake and FCS) could lead to different conclusions when analysing the effect of food price shock on household food security. They estimated the marginal effects of wheat price increase on per capita daily caloric intake and FCS using unconditional quantile regression for each decile of the food security distribution. They found that households with lower calorie intake (food insecure households) did not exhibit a decline in per capita calorie intake because of the wheat price increase. However, households with higher calorie intake (food secure households) exhibited a higher reduction in per capita calorie intake in response to the price increase. On the other hand, the FCS estimation results showed that the most vulnerable households exhibited larger reductions in dietary diversity (FCS) in response to higher wheat prices compared with the households at the top of the FCS distribution (households with higher FCS). Thus, the most vulnerable households might maintain their calorie intake by compromising diet quality. These results imply that food security monitoring or impact assessments based solely on calorie intake could be misleading, and may have severe long-term implications for households’ well-being. In this regard, analysis based on dietary diversity-based measures (e.g. FCS) provides more insights into the effects of shocks on household food security (diet quality) across the entire food security distribution [ 85 ]. However, Ibok et al. [ 36 ] noted that FCS (and per capita calorie adequacy) are not good indicators of household’s vulnerability to food insecurity compared with CSI. In response, they developed the Vulnerability to Food Insecurity Index.

About 40% of the retrieved publications used experience-based indicators (Household Food Insecurity Access Scale [HFIAS], Household Hunger Scale [HHS], Household Food Security Survey Module [HFSSM], Latin American and Caribbean Household Food Security Scale [ELCSA], Food Insecurity Experience Scale [FIES]) for measuring food security (Fig.  4 ). HFIAS is the most widely used experience-based indicator (11 articles), followed by HFSSM (9 articles) and FIES (5 times). ELCSA and HHS have been used three times each. HFIAS was primarily used for estimating the prevalence of food insecurity, whereas its adapted version HHS was mainly used for analysing the determinants of food insecurity (Additional file 2 : Table S3). The HFSSM was mainly used to analyse the determinants of household level food security in the US (six articles) (Additional file 2 : Table S4). Courtemanche et al. [ 91 ] and Burke et al. [ 19 ] used HFSSM for program evaluation, respectively, to analyse the effects of Walmart Supercenters (which increase food availability at lower food prices) on household food security and school-based nutrition assistance programs on child food security (Additional file 2 : Table S4).

Romo-Aviles and Ortiz-Hernández [ 92 ] used the ELCSA food security indicator to analyse the differences in food, energy, and nutrients supplies among Mexican households according to their food insecurity status (Additional file 2 : Table S4). In the first stage, they applied an ordinal regression model to analyse the determinants of household food insecurity status. In the second stage, they analysed the effect of food insecurity (i.e. a household’s food insecurity state as an independent variable) on household’s energy and nutrient supplies by using the ordinary least squares (OLS) model. Sandoval et al. [ 66 ] compared ELCSA and the household calorie adequacy indicator in food security analysis: prevalence estimation, determinants analysis, and program evaluation. They concluded that the two indicators provided very different food insecurity prevalence estimates, and the determinants were shown to vary significantly. The results of the programme evaluation also showed that the magnitude of the effect of a cash transfer program was significantly larger when using the ‘objective’ undernourishment indicator than the ‘subjective’ ELCSA food security indicator.

The majority of the five studies that used the FAO’s FIES indicator analysed the determinants of food security at regional and global levels, whereas one study [ 73 ] used it for program evaluation to assess the effect of provisions of a plant health service on food insecurity prevalence among farming households (Additional file 2 : Table S5).

Figure  5 summarises the data on the proportion of articles according to the number of indicators used per article. About 58% of the 78 articles used only one indicator in their food security analysis. The HFSSM and household calorie adequacy indicator have respectively been used eight and seven times as the sole food security indicator in food security analyses. HFIAS (four times), FIES (three times), and FCS (three times) were also used as the only measures of food security. The experience-based indicators (HFSSM, HFIAS, and FIES) are the most frequently used indicators as a single measure of food security in the literature, whereas the other categories of food security indicators (dietary diversity, anthropometric, and coping strategy) are mostly used in combination with other indicators.

Summary articles by the number of indicators used per article ( N  =  78 )

Three studies (out of the 78 articles) applied at least six food security indicators (one study used eight indicators while the other two studies used six indicators each). Islam et al. [ 68 ] applied eight food security indicators to analyse the effects of microcredit programme participation on household food security. They applied the calorie adequacy indicator, HDDS (number of food groups consumed), Food Variety Score (FVS, number of food items consumed), three child anthropometry measures (stunning, wasting, underweight), and two women anthropometry measures (body mass index [BMI] and mid-upper arm circumference [MUAC]) as measures of food security. Bühler et al. [ 79 ] applied six indicators (FCS, Reduced Coping Strategy Index [RCSI], HFIAS, and child stunning, wasting and underweight) to evaluate the relationship between household’s food security status and individual’s nutritional outcomes. The indicators FCS, RCSI, and HFIAS were used to measure a household’s food security status, whereas the anthropometry measures were used as indicators of individual’s nutritional outcomes. Maxwell et al. [ 83 ] also applied six food security indicators (Coping Strategy Index [CSI], RCSI, FCS, HDDS, HFIAS, and HHS) to compare the estimates of food insecurity prevalence over seasons of the most frequently used indicators.

About 45% and 37% of the retrieved articles applied food security indicators to analyse food security determinants and for food insecurity prevalence estimation, respectively. The calorie adequacy indicator (11 articles), FCS (8 articles), HDDS (7 articles), HFSSM (7 articles), and HFIAS (7 articles) were the most frequently used indicators in this regard. The calorie adequacy indicator (11 articles), FCS (10 articles), HDDS (8 articles), and HFIAS (7 articles) were the most applied indicators for estimating food insecurity prevalence.

About 60% of the retrieved studies measured food security at household-level while 20% of them assessed food security at individual-level. The most frequently used household-level indicators were the calorie adequacy indicator (14 articles), FCS (13 articles), and HDDS (12 articles). The experience-based household food security indicators HFIAS and HFSSM were also used nine and seven times, respectively. For individual-level analyses, the following child anthropometry measures were mostly used: stunning (four times), wasting (three times), and underweight (three times). The individual-level food security indicators WDDS and BMI were also used four times each.

Summary of indicators by study region and data source

As shown in Fig.  3 , the main focus areas of the 78 publications were Sub Sahara Africa and South (east) Asia. These studies employed different indicators in different countries. The type of FS indicator employed in these studies by country is summarised in Fig.  6 (reported only for those countries where at least two indicators were used). The HFSSM indicator was used 7 times in the USA (the highest at country level), which is expected as the HFSSM is used for monitoring household-level food security in the USA. The HDDS was used four times in Kenya whereas the calorie adequacy indicator and HDDS were used three-times each in Ethiopia and Bangladesh.

Summary of studies by country and indicators applied [Note: Multiple indicators could be used per study, and a study may cover multiple countries]

About 42% of the 78 studies employed primary data. The majority of these 33 studies applied experience-based indicators: HFIAS (9 articles), HFSSM (6 articles), and other experience-based indicators (4 articles). Dietary diversity-based indicators (12 articles) and calorie adequacy indicator (8 articles) were also applied frequently by studies that employed primary data (Fig.  7 ). The distributions of the 33 studies that employed primary data by region is as follow: Africa (15 articles), Asia (7 articles), Central and South America (4 articles), Europe (2 articles) and North America (5 articles). The USA and Ethiopia are the countries with the highest number of studies by country (5 and 4 studies, respectively) (Fig.  7 ). The majority of the studies that applied calorie adequacy indicator and FCS have employed secondary data whereas most of the studies that applied experience-based indicators have employed primary data (Fig.  8 ). This may imply the fact that collecting data for experience-based indicators is convenient compared to the other type indicators such as the dietary-based ones.

Summary of indicators used by country and data source [Note: Multiple indicators could be used per study, and a study may cover multiple countries]

Summary of indicators used by data source [Note: Multiple indicators could be used per study]

Quantitative characterization of food security dimensions and components

An ideal food security indicator should capture all the four food security dimensions (availability, access, utilization and stability) and components (quantity, quality, safety and preference). Because ‘measuring food security explicitly’ was one of our inclusion criteria for selecting articles (Fig.  1 ), and as the most commonly used food security indicators in the literature are measures of food access (Tables 1 , 2 , 3 , 4 ), all the 78 articles measured the food access dimension. However, the utilisation (13%) and stability (18%) dimensions of food security were seldomly captured. For measuring food utilisation, six of the ten articles applied anthropometry measures [ 64 , 68 , 79 , 93 , 94 , 95 , 96 ]. Izraelov and Silber [ 7 ] applied the Global Food Security Index (GFSI), which allows measuring food utilisation as a construct using 11 indicators. Slimane et al. [ 56 ] derived an indicator of food utilisation from ‘ access to improved water sources and access to improved sanitation facilities ’, which are two of the ten indicators of the food utilisation dimension in FAO’s Suite of Food Security Index (Table 2 ; [ 29 ]. In the literature, the stability dimension has commonly been captured by using (i) composite indices [ 7 , 12 ], (ii) the concepts of vulnerability [ 35 , 36 , 61 , 69 , 86 ] and resilience [ 74 , 88 , 90 ], (iii) econometric approaches [ 76 , 88 , 96 ] (iv) dynamic farm household optimisation model [ 97 ], and (v) measuring food security over time/seasons [ 76 , 83 ].

Almost all the studies analysed the quantity and quality components of food security, whereas the food safety and preference/cultural acceptability components were rarely captured during food security measurements. Although these components are critical in achieving food security according to the 1996 World Food Summit definition of food security, only 2 and 18 studies (out of the 78 articles) captured the food safety and preference components, respectively. Most of the studies (11 articles) that captured the preference component applied the HFIAS indicator, as the second question of the HFIAS 9-items questionnaire addresses the preference food security component. On the other hand, Izraelov and Silber [ 7 ] using the GFSI and Ambikapathi et al. [ 98 ] using an experience-based food security indicator captured the food safety component.

Only 3 of the 78 publications employed a comprehensive food security measurement, where they measured food security by explicitly considering all the four food security dimensions [ 7 , 12 , 96 ]. Caccavale and Giuffrida [ 12 ] and Izraelov and Silber [ 7 ] used composite food security indices to capture the four food security dimensions, while Upton et al. [ 96 ] applied a moment-based panel data econometric approach to the concept of development resilience in food security measurement. Caccavale and Giuffrida [ 12 ] developed the Proteus Composite Index (PCI) for measuring food security at national level. PCI can be used to monitor the food security progresses of countries by comparing within (over time) and between countries. It addresses the shortcomings of other composite indicators in terms of weighting, normalisation, and sensitivity. The PCI is constructed from 21 indicators: availability (2 indicators), access (7 indicators), utilisation (2 indicators), and stability (10 indicators) (Table 5 ). Eleven of these indicators were adopted from FAO’s Suite of food security Index [ 30 ].

Izraelov and Silber [ 7 ] is the only study (out of the 78 publications) that applied the GFSI for measuring food security at national level. Like FAO’s Suite of Food Security Index, the GFSI is a composite food security indicator that measures all the four dimensions of food security. Because the GFSI primarily assesses and monitors food security at a national level (i.e. ranking of countries based on the GFSI score), Izraelov and Silber [ 7 ] investigated the sensitiveness of the rankings of countries to the list of indicators used for the different dimensions and to the set of weights elicited from the panel of experts of the Economic Intelligence Unit by employing PCA and/or data envelopment analysis (DEA) methods. The authors concluded that the rankings based on the GFSI are robust in relation to both the expert weights used and the choice of indicators. The Economist Intelligence Unit (EIU) (2021) produces the GFSI index each year by using 69 indicators covering the four dimensions of food security: availability, affordability (accessibility), quality and safety (utilization), and natural resources and resilience (stability).

Upton et al.’s [ 96 ] defined four axioms that an ideal food security measure must reflect. Relying on the 1996 World Food Summit food security definition [ 4 ], they defined the following four axioms:

Scale axiom: it addresses both individuals and households at different scale of aggregation (e.g. regions) reflecting ‘all people’;

Time axiom: reflecting ‘at all times’, it captures the food stability dimension to account for both predictable and unpredictable variability of food security over time;

Access axiom: derived from ‘physical, social and economic access’, it captures the food access (and implicitly the availability) dimensions; and

Outcomes axiom: reflecting on “an active and healthy life”, it reflects the food utilization dimension, which captures the dietary, nutrition, and/or health outcomes.

Upton et al. [ 96 ] did note that no food security measure at the time satisfied all these four axioms in the literature. In response, they employed a stochastic dynamic measure of well-being based on the concept of development resilience [ 99 ]. Barrett and Constas [ 99 ] defined development resilience as ‘the capacity over time of a person/household... to avoid poverty in the face of various stressors and in the wake of myriad shocks. If and only if that capacity is and remains high over time, then the unit is resilient’ (p. 14). [ 100 , 101 ] demonstrated the econometric implementation of the stochastic dynamic measure of well-being at multiple scales using household or individual survey data. They showed how a measure of household or individual well-being and resilience can be estimated, and aggregated at regional or national level using a system of conditional moment functions. By adopting the [ 100 , 101 ] moments-based (dynamic) panel data econometric approach, Upton et al. [ 96 ] used the resilience concept in food security measurement to reflect the above four axioms as follows:

The scale axiom is satisfied by estimating food security at the individual or household level, and then by aggregating it into higher-level groups (e.g. regions).

The time/stability axiom is captured by using [ 100 , 101 ] dynamic approach.

The access axiom is considered by conditioning the moments of the food security distribution regarding economic, physical, and social factors that influence food access.

The outcome (utilisation) axiom is considered by using nutritional status indicators as dependent variables in the econometric model. Upton et al. [ 96 ] used HDDS and child MUAC as outcome indicators.

Recent developments in food security measurement

The concepts of vulnerability and resilience have only recently been introduced in food security measurement and analysis. Rather than directly measuring food security or food insecurity, researchers have been seeking to measure vulnerability to food insecurity and food security resilience, and their respective determinants/drivers. Out of the 78 publications, 5 and 4 articles respectively employed the concepts of vulnerability [ 35 , 36 , 61 , 69 , 86 ] and resilience [ 74 , 88 , 90 , 96 ] in their food security measurement and analysis.

Ibok et al. [ 36 ] developed the Vulnerability to Food Insecurity Index (VFII) for measuring the vulnerability of households to food insecurity, and validated it by comparing the estimates of vulnerability to food insecurity with the traditional food insecurity measures (calorie adequacy, CSI, FCS). The VFII is a composite index constructed from three dimensions (Table 6 ): exposure (probability of covariate shock occurring), sensitivity (previous/accumulative experience of food insecurity), and adaptive capacity (how households respond, exploit opportunities, resist or recover from food insecurity shocks, which is the coping ability of households). A set of indicators are used for each of the three dimensions (Table 6 ). By defining thresholds, Ibok et al. [ 36 ] assigned households into one of the three categories: highly vulnerable, mildly vulnerable, and not vulnerable to food insecurity. The results showed that VFII has a weak positive correlation with FCS and per capita calorie adequacy, whereas it has a negative correlation with CSI. Some of the households with poor calorie per capita consumption were classified as not vulnerable to food insecurity, whereas some households with acceptable calorie per capita consumption were identified as highly vulnerable to food insecurity. The authors concluded that a household’s vulnerability to food insecurity can be better measured using CSI than using FCS and per capita calorie adequacy (using the VFII as a benchmark).

[ 86 ] analysed the effects of households’ vulnerability to different climatic hazards on their food access by employing a generalised linear regression model. They used FCS as a measure of household food access, concluding that households that are vulnerable to flood were found to be more likely to be food insecure (i.e. to have a low FCS) than less vulnerable households.

Vaitla et al. [ 88 ] and Upton et al. [ 96 ] employed dynamic panel data modelling to measure the food security resilience of households. They analysed the determinants of food security status at a point in time, and its food security resilience by using different food security indicators. They defined resilience as ‘the probability that a household is truly above a chosen food security cut-off, given its underlying assets, demographic characteristics, and past food security status’. Similar to Upton et al. [ 96 ], they used the moments (mean and variance) of the food security score over time to estimate resilience as the probability of attaining a given level of food security. Vaitla et al. [ 88 ] used FCS and RCSI as a dependent variable in their dynamic panel data model. They concluded that the determinants of a household’s food security status and food security resilience are different. They also showed that the drivers of food security resilience vary across the two food security measures used as dependent variables.

Lascano Galarza [ 90 ] investigated the effects of food assistance on a household’s food security status at a point in time, and its food security resilience, by applying FAO’s Resilience Index Measurement and Analysis II framework. The author used FCS and food expenditure as measures of food security when evaluating the effects of the food assistance program and the household’s resilience on food security status. Factor analysis and multiple indicators multiple causes models were used to construct the resilience score and to analyse its effect on food security. The resilience score was derived from four indicators: assets, access to basic services, social safety nets, and adaptive capacity. The author ultimately found a significant positive association of food assistance programmes with a household’s food security status and food security resilience.

Smith and Frankenberger [ 74 ] analysed the effects of resilience capacity in reducing the effect of shocks on household food security using HHS and FAQ (number of months of inadequate household food access) as measures of food security. The results of their fixed effect panel data model showed that resilience capacity enhancing attributes such as household assets, human capital, social capital, information access, women empowerment, diversity of livelihood, safety nets, and market access reduce the negative effect of flooding on household food security.

Which food security indicator is the best?

Although numerous food security indicators have been developed for use in research, there is no agreement on the single ‘best’ food security indicator among scientists or practitioners for measuring, analysing, and monitoring food security [ 9 , 12 ]. The different international agencies also use their own sets of food security indicators (e.g. World Food Programme: FCS, USAID: HFIAS; FAO: POU and FIES; and EIU: GFSI). Figure  9 summarises the most applied food security indicators according to the level of analysis and the food security dimensions that they intend to reflect. The level of analysis ranges from macro (e.g. national) to micro (e.g. individual) levels, and the measured food security dimension from availability to utilisation. An ideal food security indicator should capture all the four food security dimensions at individual level to reflect the 1996 World Food Summit definition of food security. However, most of the available indicators are measures of food access at the household level (Fig.  9 ). Only a few composite and anthropometry indicators can measure food utilisation (besides availability and access) at national and individual levels, respectively. On the other hand, the stability dimension can be captured by estimating food security indicators over time or as described above in ‘‘ Quantitative characterization of food security dimensions and components ’’ Sect. The three composite indicators GFSI [ 26 ], Suite of Food Security Index [ 29 ], and PCI [ 12 ] can allow to directly measure the stability dimension of food security while also capturing the other three food security dimensions at national level.

Summary of the retrieved indicators according to the level of analysis and food security dimensions

In general, there exist an inherent trade-off when choosing one indicator over another type of indicator because the various classes of food security indicators reflect different aspects of food security [ 96 ] such as dimensions, components, levels of analysis (e.g. national vs individual), and data requirement (subjective vs objective; recall period of 1 year vs 24 h). Therefore, most of the commonly used indicators can be considered as mutually complementary than substitutes for one another. The subjective experience-based indicators, for example, measure a household’s experience of anxiety/worry/hunger arising from lack of food access, whereas the objective dietary diversity-based indicators measure a household’s access to diverse food, reflecting a household’s caloric intake and diet quality. Household dietary diversity-based and caloric adequacy indicators also complement each other because sufficient calorie might be achieved with low food quality (without diversified diet), whereas a diverse diet might not be enough to meet a household’s caloric requirement. Noting this complementarity, Bolarinwa et al. [ 76 ] classified households into three categories of food insecurity (food secure, partially food insecure, and completely food insecure) by integrating two indicators: HDDS and per capita food expenditure (where the food expenditure reflects caloric adequacy).

Data requirements of food security measurement

The most critical challenge of a comprehensive food security measurement and analysis is generating reliable data consistently for estimating complementary food security indicators (at the individual level) [ 13 ]. Measuring food security with a high frequency consistently over time (e.g. quarterly instead of annually) at the individual level by applying a set of complementary indicators (e.g. calorie/nutrient adequacy and anthropometry measures) can help us better analyse and monitor food security (Fig.  10 ). A national level food security measurement at a point in time (e.g. using POU) is less informative for decision-making compared with measuring food security every year (or ideally in real-time) at the household level (e.g. using calorie adequacy). Integrating food consumption and anthropometry information in regular national household living standard surveys can also be crucial to eliminating the limitations of current measurement approaches, especially because nutrition, food consumption, health, and income are interrelated [ 13 ].

High frequency food security measurement for better food security analysis.

De Haen et al. [ 13 ] rightly remind us that to improve the reliability and accuracy of a nation’s food security measurement and analysis, ‘the focus should be on generating more timely, comprehensive, and consistent household surveys that cover food consumption and anthropometry, [which] allow much better assessment of the prevalence of food insecurity and undernutrition, as well as of trends and driving forces.’ That is, first, generating data from a nationally representative sample through comprehensive household surveys allows us to estimate a set of complementary indicators reflecting the different aspects of food security measurement (dimensions, components, outcomes, behavioural responses, coping mechanisms) (Fig.  10 ). Second, comprehensive surveys help measure both the prevalence of food insecurity and its drivers/determinants. Third, it is critical to generate these data consistently over time so that the progress towards food security can be monitored, drivers can be analysed over time, and food insecurity can be detected well in advance. This approach could address the UN Scientific Group’s criticism [ 11 ] that ‘existing early warning systems lack indicators to adequately monitor degradation of food systems.’ Fourth, the data allow us to analyse and evaluate the effects of programmes and interventions (over time) at different levels (individual, household, and national). It also opens opportunities to conduct development research in food, nutrition, health, and poverty [ 13 ].

In summary, we suggest the following points in the light of the above discussions for a comprehensive food security measurement:

Food security should be measured at the individual (or at least at household) level by applying a set of complementary food security indicators to capture the availability, access, and utilisation dimensions of food security. Combining anthropometry measures with other objective food security indicators (e.g. calorie adequacy or dietary diversity indicators) will further allow us to capture these three dimensions.

The fourth dimension of food security, i.e. the stability dimension, can be captured by producing the estimates of the complementary food security indicators over time or in real time. A repeated high frequency food security measurement (if possible by using near real-time data) is thus preferable, as it can also help to identify the onset of food insecurity in time, to evaluate interventions/programs, and to monitor food security progresses.

The behavioural aspects of food insecurity and the cultural acceptability of food can be measured by using one of the experience-based measures. For example, FAO’s FIES can be applied to estimate the prevalence and severity of food insecurity at individual level. Because the FIES has been applied in more than 100 countries, countries can compare their respective food security states with each other.

The use of experience-based indicators (e.g. FIES) allows conducting rapid food security assessments as the data collection is easier compared to the objective food security indicators (e.g. calorie adequacy).

Integrating food consumption (intake, expenditure, and diet diversity) and anthropometry information in regular national household living standard surveys enables us to collect complete and consistent data for estimating complementary food security indicators in food security analyses.

Study limitations and future research

In this study, we identified and characterized the most commonly applied food security indicators in the literature with respect to the dimensions and components covered, methods and models of measurement, level of analysis, data requirements and sources, intended purpose of application, and strengths and weaknesses. Subsequently, we analysed data on food security measurement from 78 peer-reviewed articles, and suggested the estimation of complementary food security indicators consistently over time for conducting a comprehensive analysis by taking all the four food security dimensions and components into account. In order to select the set of these complementary food security indicators that would be applicable to a specific context (e.g. country or region), we recommend to conduct a Delphi study by involving food security experts, policy-makers and other relevant stakeholders. In addition, we limited the literature search to two databases (Scopus and WoS) and included only peer-reviewed articles in this study. Therefore, we suggest to extend this study by broadening the literature type by including the grey literature (e.g. reports, book chapters and conference proceedings) and by searching from other databases, which reduce the publication bias. Moreover, we followed the 1996 World Food Summit definition of food security [ 5 ], which provided the foundation for the four food security dimensions ( availability , access , utilisation , and stability ). Accordingly, in this study, we organised the literature review on food security measurement over these four dimensions. However, food system researchers have recently noted the need to update the definition of food security in reference to sustainable food systems, for example, by including new food security dimensions [ 102 – 104 ]. Clapp et al. [ 103 ], for example, proposed the inclusion of two extra dimensions ( sustainability and agency ) to improve the framework of food security analyses. The inclusion of these two extra dimensions guarantees that every human being has access to healthy and nutritious food, not only now but also in the future. In this regard, sustainability can be considered as a pre-requisite for long-term food security [ 103 , 104 ]. Therefore, we recommend future research to operationalize literature reviews according to the six food security dimensions (i.e. availability , access , utilisation , stability , sustainability and agency ). Furthermore, most existing studies about food security measurement in the literature are based on the 1996 World Food Summit definition of food security [ 5 ]. Food security analyses based on this definition narrows the scope of the food security concept, and do not support system level analysis by considering other components of the food system. For example, food security is a subset (component) of the Food Systems Approach, which takes food environments, food supply chains, individual factors, external food system drivers, consumer behaviour, and food system outcomes (e.g. food security and health outcomes) into account [ 105 – 108 ]. Therefore, given the increasing attention to the Food Systems Approach and system level analyses in the literature, the Food Systems Approach can be used as a framework for operationalising future literature reviews on food security.

We critically reviewed numerous food security indicators and methodologies published in scientific articles since the last decade using the SLR methodology. We reviewed, analysed, and summarised the results of 78 articles on food security measurement. We found that the household-level calorie adequacy measure was the most frequently used indicator in the literature as a sole measure of food security. Dietary diversity indicators (HDDS, WDDS, IDDS, and FCS) and experience-based indicators (HFSSM, FIES, HFIAS, HHS, ELCSA) were almost equally in use and popular. In terms of the food security dimensions, food utilisation (13%) and stability (18%) were seldom captured. Caccavale and Giuffrida [ 12 ], Izraelov and Silber [ 7 ], and Upton et al. [ 96 ] are the only studies that measured food security by considering all four dimensions. We also found that the majority of the studies that applied calorie adequacy and dietary diversity-based indicators employed secondary data whereas most of the studies that applied experience-based indicators employed primary data, suggesting the convenience/simplicity of collecting data for experience-based indicators than dietary-based indicators. The use of experience-based indicators allows conducting rapid food security assessments whereas the use of complementary indicators is required for food security monitoring over time. We conclude that the use of complementary food security indicators, instead a single indicator, better capture the different food security dimensions and components,this approach is also beneficial for analyses at different levels. The results of this study, specifically the analysis on data requirements for food security measurement, can be used by food security stakeholders such as governments, practitioners and academics for briefs, teaching, as well as policy-related interventions and evaluations.

Availability of data and materials

All data are available within the paper.

Detailed discussion on this issue can be found in ''Which food security indicator is the best?'' Sect.

In Scopus, since the research field ‘Agricultural and Biological Sciences’ domain is very broad, we excluded studies in the areas of biology, chemistry, ecology, environment, forestry, aquaculture, and plant/crop sciences during the literature search (via “AND NOT”).

In line with this, our final food security measurement dataset does not contain articles from 2010 Additional file 1 .

The call to the special issue can be retrieved from the journal’s website: https://www.sciencedirect.com/journal/global-food-security/special-issue/10F642R6J6K .

This confirms the lack of due attention given to the standardization and harmonisation of food security measurement prior to 2010, and the lack of consensus among researchers, practitioners, or governments on the indicators and methodologies that should be applied for measuring and monitoring food security.

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Acknowledgements

We are grateful to Maha AlDhaheri for the support at the initial stage of the literature searching and screening processes.

This study was funded by the Ministry of Education of the United Arab Emirates through the Collaborative Research Program Grant 2019, under the Resilient Agrifood Dynamism through evidence-based policies project [Grant Number: 1733833].

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Additional file 1:.

 Data and list of articles used in the systematic literature review on food security measurement (N = 78).

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Table S1 . Summary of the publications that applied dietary diversity score indicators. Table S2 . Summary of the publications that used Food Consumption Score (FCS). Table S3 . Summary of the publications that used HFIAS and HHS. Table S4 . Summary of the publications that used HFSSM and ELCSA. Table S5 . Summary of the publications that used FIES.

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Manikas, I., Ali, B.M. & Sundarakani, B. A systematic literature review of indicators measuring food security. Agric & Food Secur 12 , 10 (2023). https://doi.org/10.1186/s40066-023-00415-7

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• Food insecurity
• Measurement

Agriculture & Food Security

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A systematic literature review of indicators measuring food security

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• 1 Faculty of Business, University of Wollongong in Dubai, Knowledge Park, 20183 Dubai, United Arab Emirates.
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• DOI: 10.1186/s40066-023-00415-7

Measurement is critical for assessing and monitoring food security. Yet, it is difficult to comprehend which food security dimensions, components, and levels the numerous available indicators reflect. We thus conducted a systematic literature review to analyse the scientific evidence on these indicators to comprehend the food security dimensions and components covered, intended purpose, level of analysis, data requirements, and recent developments and concepts applied in food security measurement. Data analysis of 78 articles shows that the household-level calorie adequacy indicator is the most frequently used (22%) as a sole measure of food security. The dietary diversity-based (44%) and experience-based (40%) indicators also find frequent use. The food utilisation (13%) and stability (18%) dimensions were seldom captured when measuring food security, and only three of the retrieved publications measured food security by considering all the four food security dimensions. The majority of the studies that applied calorie adequacy and dietary diversity-based indicators employed secondary data whereas most of the studies that applied experience-based indicators employed primary data, suggesting the convenience of collecting data for experience-based indicators than dietary-based indicators. We confirm that the estimation of complementary food security indicators consistently over time can help capture the different food security dimensions and components, and experience-based indicators are more suitable for rapid food security assessments. We suggest practitioners to integrate food consumption and anthropometry data in regular household living standard surveys for more comprehensive food security analysis. The results of this study can be used by food security stakeholders such as governments, practitioners and academics for briefs, teaching, as well as policy-related interventions and evaluations.

Supplementary information: The online version contains supplementary material available at 10.1186/s40066-023-00415-7.

Keywords: Data; Food insecurity; Index; Indicators; Measurement; Scale.

© The Author(s) 2023.

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• Published: 15 August 2022

A systematic review of the impact of food security governance measures as simulated in modelling studies

• Aleid Sunniva Teeuwen 1 ,
• Markus A. Meyer 1 , 2 ,
• Yue Dou   ORCID: orcid.org/0000-0001-6320-5482 1 &
• Andrew Nelson   ORCID: orcid.org/0000-0002-7249-3778 1  

Nature Food volume  3 ,  pages 619–630 ( 2022 ) Cite this article

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• Agriculture
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To effectively address food security, we need tools that assess governance measures (for example, strategic storage reserves, cash transfers or trade regulations) ex ante. Simulation models can estimate the impact of such measures via scenarios with differently governed food systems. On the basis of a systematic review of 110 simulation studies published over 2000–2021, we examined how food security governance has been represented, and identified needs for future simulation model development. We found that studies commonly used agent-based, system dynamics, and computable general equilibrium models; tended to be production, trade or consumption centric; assessed the impact of a wide variety of mostly treasure- or authority-based measures; and applied diverse food security indicators, mostly of access or availability. We also identified blind spots (for example, simulation of nodal measures) and proposed how to address these blind spots (for example, telecoupling) and to make food security governance simulation studies fit for meta-analyses (for example, harmonizing food security indicators for comparison).

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Global food security, the access to sufficient, safe and nutritious food, has declined slowly but steadily since 2015 1 . Recently, this decline has accelerated owing to the coronavirus disease 2019 (COVID-19) pandemic and measures taken to mitigate its impact on human health. Millions of people were pushed into immediate hunger, and in the medium to long term, existing inequalities are expected to exacerbate further 2 . To address food security challenges, we cannot rely exclusively on market mechanisms to deliver sufficient quantities of nutritious food at affordable prices. This was illustrated all too well by the world food price crises of 2007–2008 and 2010, which were largely a result of speculative activity in global commodity markets 3 and increasing demands for grains as feed and fuel 4 . We need intervention in the form of governance 5 : public (for example, government), private (for example, food retailers) or communal (for example, farmer cooperatives) entities that implement measures to improve food security 6 .

To govern effectively, we need to measure the impact of governance implementation ex post or simulate the impact of governance implementation ex ante. The literature on ex-post assessments of food security governance is too limited to provide empirically founded guidance with regard to choice of governance measures to improve food security 6 , 7 . The literature on ex-ante assessment, however, is vast and growing. Ex-ante assessments are made using simulation models that compare scenarios where food systems are governed differently to projected business as usual scenarios 8 . Food security governance simulation studies have the following characteristics: they use simulation models (1) to assess the impact of governance measures ex ante (2) on food security (3) within a food value chain context (4).

There are many types of simulation models, including agent-based, system dynamic, optimization and equilibrium models (for full list, see Supplementary Note 1 ), which have different capabilities, making them fit for different governance cases and contexts 9 . Models may be coupled to overcome weaknesses associated with certain model types 10 . Governance measures are the tools that governing entities use to affect society 11 . These can be categorized into: nodality (information dissemination), authority (laws and regulation), treasure (financial incentives) or organization (capacity building, punishment and crisis management) 11 . Within these categories, measures vary in how socially and spatially targeted they are (Extended Data Table 1 ). Social and spatial targeting is increasingly adopted around the world in the hope of maximizing impact with limited funds 12 , 13 . Socially targeted measures, such as social protection policies 14 , 15 , 16 , can reach vulnerable groups more effectively 13 . Spatially targeted measures can reach geographically vulnerable groups that, for example, live in drought-prone regions 17 , 18 , 19 or food deserts 20 , 21 , 22 more effectively 23 . In addition, spatially targeted governance can, through more efficient use of natural resources, contribute to enhanced food production 24 , 25 , 26 . Assessment of socially and spatially targeted measures does, however, require socially and spatially disaggregated models and data such as household surveys and gridded data, respectively. Most simulation models are not spatially disaggregated by default, and some model types, such as equilibrium models, traditionally rely on socially aggregated data such as trade balance sheets 27 .

Impacts of governance on food security are assessed with indicators of food availability, access, utilization or stability (for indicators, see Supplementary Dataset 1 ). Impacts may occur within or outside the jurisdictions within which measures are implemented. A trade moratorium on grains from Russia and Ukraine may, for instance, reduce grain prices in Russia and Ukraine, but increase prices globally, especially in net importing countries 28 . As both the COVID-19 pandemic and the war in Ukraine have illustrated, capturing such spillover effects is increasingly important in our highly interconnected world 29 . Simulated changes in food security occur within food value chain contexts, which differ from setting to setting. In subsistence settings, they may consist of production and consumption echelons only, but more often than not, middle echelons also play an important role through distribution, processing, storage and packaging, trade and wholesale, and/or retail of food 30 . Spurred on by widespread urbanization and associated dietary changes, these middle echelons have grown rapidly and continue doing so throughout the Majority World (that is, low-income countries) 30 . Rapid urbanization without accompanied development of road infrastructure may, for instance, leave urban consumers unable to obtain or afford produce from rural areas 31 . Despite this, the middle echelons tend to be missing in both development initiatives 32 and simulation studies 33 .

Previous reviews have discussed subsets of above-mentioned characteristics (for example, how to bridge micro–macro scales in food security models 10 , 34 ) or pointed out the shortcomings of the ex-post literature on food security governance 6 , 7 . Yet, no studies have systematically reviewed food security governance simulation studies, providing no community-wide understanding of common modelling practices, potential blind spots and promising developments. In this Article, to facilitate targeted and relevant future model development, we aim to summarize and critically appraise existing food security governance simulation studies, which, in contrast to ex-post studies, have not been systematically reviewed. In particular, we ask how food security governance is simulated. This question, in turn, is broken down into four subquestions: (1) Which modelling approaches are used? (2) How do simulation studies represent the food system, and to what extent are they (capable of) capturing up- and downstream value chain dynamics? (3) Which governance measures are simulated, and are simulation models (capable of) assessing socially and spatially targeted governance measures? And lastly: (4) How is food security measured, and to what extent are models (capable of) assessing spillover effects?

A total of 1,953 potentially relevant studies were identified through a database search in Scopus and Web of Science (for full list, see Supplementary Dataset 2 ). Among these, 110 remained after title and abstract screening, full-text reading and qualitative content analysis (Table 1 ). The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) flow diagram (Fig. 1 ) illustrates the selection process of the simulation studies for systematic review (for more information, see Supplementary Note 2 , Supplementary Figs. 1–5 and Supplementary Dataset 3 ).

Search terms were compiled on the basis of related systematic reviews, and studies were identified through a systematic search and checked against exclusion criteria.

Modelling approaches

Food security governance was simulated by means of: cellular automata (CA, n  = 6), agent-based models (ABM, n  = 25), system dynamics models (SDM, n  = 19), optimization models ( n  = 11), partial equilibrium (PE, n  = 11) and computable general equilibrium (CGE, n  = 24) models, micro-simulation models ( n  = 7), econometric models ( n  = 15) and/or other mathematical models ( n  = 13) (Fig. 2 ). Thematically, equilibrium and micro-simulation models were mostly economic, while ABM, SDM, optimization and econometric models were more diverse, often covering both the bio-physical and/or the social domain in addition to the economic (Fig. 2a and Extended Data Fig. 1 ).

a – d , Number of studies using models with different modelling domains ( a ), spatial scales ( b ), simulated regions ( c ) and value chain echelons ( d ). Sizes of numbers and bubbles are proportional to number of studies. If a study covers multiple domains, is multi-scale, simulates or spans multiple regions, or covers multiple value chain echelons, it is counted multiple times.

Source data

While most studies simulated governance using stand-alone models, a minority used coupled models ( n  = 21). Coupling to specialist, typically bio-physical, mathematical models to simulate, for example, crop growth 18 , 35 , 36 , hydrology 37 or transportation 20 occurred among ABM, SDM, optimization, CGE and PE studies. Further, CGE models were frequently coupled to micro-simulation models to scale down findings, enabling the assessment of governance impacts on different socio-economic groups, for example 31 , 38 , 39 (Table 2 ).

The spatial scale of simulation studies varied from village to global (Fig. 2b ). With the notable exceptions of ABM, CA, and CGE and PE studies, most studies had a national scale. ABMs and CA tended to have subnational scales, simulating governance interventions within provincial or municipal jurisdictions. CGE and PE studies, contrastingly, were often global (Fig. 2b ). In terms of geographic focus, there were no obvious patterns distinguishing model types (Fig. 2c ).

Overall, two out of the three most food-insecure regions globally, East Asia and the Pacific ( n  = 43) and Sub-Saharan Africa ( n  = 40), were the regions covered most by the studies reviewed. South Asia ( n  = 21), however, received little attention considering the region’s high population and food insecurity status (Fig. 2c ). Considering individual countries, China ( n  = 30) and the United States ( n  = 26) dominated. Here, studies reflected national policy, with models covering the United States typically aiming to improve access to healthy food in ‘food deserts’, poor city districts, through various pricing, safety net or city planning policies 14 , 20 , 22 , and models covering China typically aiming to obtain or maintain food self-sufficiency through, for instance, land protection schemes 40 , 41 , 42 .

Value chain coverage

No study covered all food value chain echelons, and only two studies 43 , 44 covered all value chain echelons but one: retail. Most studies covered food production only, consumption only, or a combination of production, consumption and trade (Fig. 3 ). Food distribution, processing or storage, and retail were rarely simulated. Value chain coverage by CA, ABMs, SDMs and optimization models was overwhelmingly production centric (Fig. 2d ). CGE and PE models, contrastingly, were centred around the trade echelon, often combined with production and/or consumption echelons.

Row numbers and bubble sizes refer to the number of studies that cover the specific combinations of value chain echelons. Column numbers refer to the total number of studies that cover each specific value chain echelon.

Studies that covered the distribution, processing, storage and retail echelons came in three fashions. Firstly, some ABM studies assessed policies that aimed to improve urban food access by transporting food to the consumers 22 , or the consumers to the food 20 , 21 , or by relocating or creating new supermarkets 20 , 21 . Secondly, some optimization and SDM studies simulated measures that aimed to improve the availability and stability of (perishable) foods by, for instance, setting up a bottom-up cooperative for dairy distribution, processing and retail 45 , increasing the shelf life of bread 46 or meat 47 at retail, using import quotas to strengthen the domestic supply chains 48 , or improving sourcing strategies of public distribution programmes 49 . Lastly, some CGE and PE model studies simulated measures that aimed to reduce poverty and improve food access through investment in transportation 31 , 50 , processing 51 or marketing 50 , 51 infrastructure.

Governance measures

The governance measures assessed most through food security governance simulation modelling were treasure or authority based (Table 3 ). Among treasure-based measures, bearer-directed payments ( n  = 31) dominated, and among authority-based measures, standard constraints dominated ( n  = 38). Frequently simulated authoritarian measures were land protection 24 , 40 , 52 , tax policies 53 , 54 , 55 and trade regulations such as tariffs 15 , 54 , 56 , quotas 28 , 55 , 57 or bans 28 , 43 , 58 . These measures have in common that they affect or are meant to affect the wider public (first row, Table 3 ). Infrequent or absent authoritarian measures were conditional tokens 59 and enablements 16 , and certificates and directed constraints, respectively, which are customized for individuals (second and third row, Table 3 ). Treasure-based measures were more frequently targeted towards groups or individuals than authority-based measures. Social protection policies such as conditional cash transfers 14 , 15 , 16 or food stamps, for example 60 , 61 , 62 , were specifically targeted towards the poorest, most vulnerable citizens.

Organization and nodality were simulated much less than treasure and authority. Among organization-based measures, at-large treatment (typically, big infrastructure projects such as construction of dams 63 , 64 , 65 or irrigation canals 17 , 65 , 66 , roads 31 , 50 or market facilities 20 , 21 , 50 ) was simulated most. Additionally, some studies assessed the impact of governance measures aiming to improve food access through better distribution systems 39 , 57 , 67 . The most common nodal measures assessed were group-targeted messages. These were usually policies through which the government educated 68 , 69 , 70 or tried to convince farmers of the benefit of certain products 70 , 71 or practices 72 , 73 , 74 (Table 3 ).

Studies’ choices of governance measures sometimes coincided with choices of model types. At-large trade policies relying on authority such as import tariffs on food commodities were, for instance, almost exclusively simulated by CGE 15 , 54 , 56 and PE models 55 , 70 . Contrastingly, infrastructure projects and other organizational governance measures were simulated by SDM 66 , 75 , 76 , ABM 20 , 63 , 65 and optimization 49 , 50 , 64 models. Nodal measures were mostly simulated by ABMs and SDMs 74 , 77 , 78 , as well. Treasure-based measures such as subsidies on farm inputs were, however, simulated by the full range of model types. Socially targeted governance measures, too, were simulated by the full range of simulation models (Fig. 4a and Extended Data Fig. 2a ). Spatially targeted governance measures were rarer than socially targeted governance measures, but were also simulated by most model types (Fig. 4b and Extended Data Fig. 2b ).

a , b , Dark-green bubbles show the number of studies that simulated socially ( a ) or spatially ( b ) targeted governance measures with socially ( a ) or spatially ( b ) disaggregated data. Turquoise bubbles show the number of studies that simulated untargeted measures with or without socially ( a ) or spatially ( b ) disaggregated data. Pink bubbles show the number of studies that simulated socially ( a ) or spatially ( b ) targeted governance measures without using socially ( a ) or spatially ( b ) disaggregated data. Bubble sizes and numbers are proportional to number of studies.

To be able to assess the impact of socially and/or spatially targeted governance measures, models need to be socially and/or spatially disaggregated. We found that most studies ( n  = 65) were able to identify food-insecure socio-economic groups within the wider study population, and most of these studies also simulated governance measures that targeted these people directly ( n  = 41; Fig. 4a ). Most of these were treasure based. Socially targeted governance measures relying on organization, and especially nodality and authority, were much rarer (Table 3 ). Most studies did not use spatially disaggregated data ( n  = 68), rendering them unable to identify vulnerable geographic groups (for example, inhabitants of drought-prone regions; Fig. 4b ). Among the spatially disaggregated studies, few ( n  = 14) explored the potential benefits of spatially targeted measures that could potentially reach these vulnerable groups 17 , 18 , 19 or enhance production through more efficient natural resource management 17 , 25 , 26 (Table 3 and Fig. 4b ). Further, our review showed that some studies simulated targeted measures with models calibrated using socially ( n  = 14) and/or spatially ( n  = 12) aggregated data only (Fig. 4 ). Bazzana et. al. 65 , for instance, assessed the impact of the construction of hydro-electric dams, electric grids and water canals, and payments as compensation to those who lost resources because of these infrastructure projects. Despite the spatially targeted nature of these governance measures, the assessment was made without using any spatial data, without reporting the spatial extent or resolution of the model and without providing a spatially disaggregated visualization of the impact of the governance measures.

Governance impacts

Governance impacts on food security were most frequently assessed using indicators for food access (44%) or availability (27%). Few studies assessed the impact of governance on food utilization ( n  = 39), and even fewer on the nutritional qualities of crops ( n  = 1) 68 and diets ( n  = 3) 37 , 72 , 79 . The number of studies that assessed the impact of governance on stability was also low ( n  = 29), but as many studies (also) provided indicator values over time ( n  = 41); in total, more than half of the studies assessed stability. However, most models had too low temporal resolution (≥annual) to account for seasonal differences. In total, 123 different food security indicators were used (Supplementary Dataset 1 ). The majority of these (60%) were unique to single studies. The price of food commodities ( n  = 38) and income of citizens ( n  = 37) were most frequently used to assess the impact of governance on food access. The production of food ( n  = 33) and the area used to grow crops or keep livestock ( n  = 26) were most frequently used to assess the impact of governance on food availability. The consumption ( n  = 21) or purchase ( n  = 6) of food were most frequently used to assess the impact of governance on food utilization. Lastly, self-sufficiency ( n  = 8) and stocks ( n  = 8) of food crops were most frequently used to assess the impact of governance on stability. Only a minority of the studies ( n  = 23) captured changes in the distribution of food security within a population through indicators such as poverty incidence ( n  = 13), or income inequality ( n  = 9), though a few additional studies gave insight into inequality ( n  = 8), for example, by providing the incomes for different socio-economic groups 31 , 80 , 81 . Most indicators were assessed only within the jurisdiction where the governance measures were implemented ( n  = 90), that is, potential spillover effects were seldom captured. When the impacts were assessed globally ( n  = 14), or locally outside a jurisdiction ( n  = 12), this was done by PE 28 , 37 , 55 or CGE 54 , 82 , 83 simulation studies.

A wide variety of governance measures have been simulated in the reviewed studies, though authority- and treasure-based measures were much more frequent than organization-based, and especially nodality-based, measures. Concerning food security governance, there has not been a comprehensive overview of governance measures implemented in practice. Country responses to the 2008–2009 food price crisis were, however, mapped by Demeke et. al. 13 . In line with this systematic review, they found that a lot of authority- and treasure-based at-large trade policies were implemented. They also registered that many countries implemented treasure-based food price regulation policies and social protection measures such as cash and food transfers to vulnerable people, and subsidy schemes aiming to increase food production. However, they did not register any nodality or organizational measures. This might be because, in contrast to the implemented authority- and treasure-based measures, they do not provide immediate help in times of crises. Nodality and organizational measures typically require longer time horizons, as they often—though not always—require actors to adopt new knowledge 54 , 70 , 73 or norms 20 , 60 , 84 , or require the construction of large-scale infrastructure 24 , 66 , 76 or logistic networks 21 , 49 , 85 .

As research and practice have thus far focused on treasure- and authority-based measures, the potential of many organizational, and especially nodal, measures remains unexplored. In the field of public health, nodal measures such as mass radio campaigns have been shown to be highly cost-effective, mostly owing to the fact that they are very cheap compared with other health interventions 86 . In the field of food security governance, we found only one realistically simulated example of nodal governance implementation and its effectiveness. This study, performed by Williams et al. 18 , found the impact of sharing climate forecasts with farmers (a nodal measure) to be similar to the impact of providing 20% of farmers with jobs (an organizational measure). However, they expected the nodal measure to be much cheaper and easier to realize 18 . The rarity of simulated nodal measures could be due to the fact that this requires models with high degrees of social disaggregation, and the ability to simulate interacting agents, which requires approaches such as ABMs. As increased cost-effectiveness in food security governance could speed up the alleviation of food security, we see the development of such models as an important research priority.

Shifting the focus from spatially untargeted at-large measures based on treasure and authority towards spatially and socially targeted measures based on organization, authority and nodality would be helpful to expand our knowledge base, potentially increase the cost-effectiveness of food security governance and support the decision-making process of policymakers already interested in implementing such governance measures but who often fail to do so effectively 12 , 13 . This can be achieved through the use of spatial models such as ABMs 20 , 63 , 87 or CA 25 , 40 , 52 , or by disaggregating traditionally aspatial models such as CGE or optimization models by either parameterizing them differently for different geographic regions 39 , 88 or by coupling them to spatial models 73 . In terms of the assessment of the impact of governance on food security, simulation models tend to assess governance impacts on availability, access and extra-seasonal stability, but rarely intra-seasonal stability, nutrition and social inequality. As these aspects are prerequisites to achieving food security 5 , 89 , and their assessment is technically feasible 18 , 67 , 68 , we call researchers to include them in future studies.

Steering away from unwanted, negative spillover effects, that is, reducing food security within a region at the expense of food security outside that region, is another prerequisite to achieving net food security improvement. To avoid spillover effects, studies need to assess the impacts of governance measures not only within the jurisdiction where they are implemented, but also in regions connected to this jurisdiction 29 . However, only a small minority of studies did this, all using CGE or PE models. Further, only three of the studies capturing spillover effects used socially disaggregated data 70 , 90 , 91 , and only one reported the impacts of governance implementation in a socially disaggregated way 90 . This illustrates that the ability to assess and target governance in a socially disaggregated way and the ability to assess potential spillover effects are rarely combined. Yet, this is crucial for achieving social justice outcomes 5 . To combine these two abilities, we need to bridge the gap between macro- and micro-scale models 10 , 92 , by downscaling macro-models or by upscaling 10 , 34 or by telecoupling 29 micro-models.

Scaling down is a technical development that has opened up for equilibrium models through coupling with micro-simulation models 39 , 88 . Scaling up micro-scale models is less common than scaling down macro-scale models 93 , and none of the models eligible for this review did this. Nevertheless, this is technically feasible and has been illustrated by Niamir et al. 94 , who simulated the energy consumption choices of households with an ABM and upscaled their impact on total EU-26 energy use with a CGE model. Telecoupling, which accounts for socioeconomic–environmental interactions between distant places 29 , has been illustrated in various food system simulation models. Dou et. al. 95 , for instance, used a telecoupled ABM to assess the impact of increased demand for soybeans in China on land use and farmers’ welfare in Brazil. However, none of our identified food security governance simulation studies use telecoupling.

Lastly, we found that simulation models typically either have a macro-economic focus and simulate the inter-linked impact of governance on food production and consumption through trade, or focus solely on either the production or consumption end of the value chain (Fig. 3 ). The governance measures simulated reflect this trend: measures attempting to improve transportation 20 , 22 , 49 and retail 20 , 47 , 67 , and especially storage 57 , 62 , 96 and processing 45 , 51 , were rare. The neglect of the value chain echelons between production and consumption is in line with previous reviews—which have termed them the ‘missing’ 32 or ‘hidden’ 30 middle—except that the ‘productivist paradigm’ 7 , 33 was less striking. We observed a stronger consumption focus. This may be due to a production bias present in previous reviews 7 , which we also observed when compiling the search string used for this review (Supplementary Note 2 ). Though not all value chain echelons are relevant for all food systems and commodities, it is important for the field of governance simulation to advance the ability to simulate the role of the missing middle in up- and downstream value chain dynamics. This can be done with any type of simulation model, but requires hardly accessible data on missing middle value chain actors, especially in the Majority World 97 .

Analyses of the quantitative impacts of governance measures on food security are important to inform decision makers about the effectiveness of different governance measures and, through this, speed up the alleviation of food insecurity. To facilitate such analyses, we explored which studies and governance measures within the reviewed pool of literature could be compared as they used the same food security indicators, measured with the same spatial and temporal precision, and were implemented either inside a jurisdiction, outside it or globally. Using these criteria for comparability, we found 26 groups with three to six studies, exploring up to 13 governance measures, that could potentially be compared in meta-analyses. Multiple comparisons could be made between ABM, SDM and optimization modelling studies, SDM and PE modelling studies, or CGE and PE modelling studies. The first group of studies assessed the impacts of nodal, treasure-based and organizational measures on land use and farm income. The second two groups assessed the impacts of treasure-based and authority-based measures on the consumption, import, production and price of cereals. For the full list of comparable simulation studies, their simulated governance measures and their impacts on food security, see Supplementary Dataset 4 . Owing to the absence of reporting guidelines within the field, researchers may need to request additional information from the authors of the studies.

We note that our findings are subject to limitations. Firstly, studies were identified using a search string compiled from existing reviews on simulation modelling, governance, food security and food systems. Biases present in these reviews may be only partly addressed through the compiled search string used in our research. We noticed, for instance, that we initially lacked terms for the consumption echelon, which we therefore supplemented. Other biases may have gone unnoticed. Secondly, we did not include specific governance programmes or food security indicators in our search string. Studies that assess the impact of specific governance measures (for example, the Indian public food distribution programme 49 ) on specific food security indicators (for example, dietary income differential 67 ) without framing their studies or outcomes in the context of governance and food security might have been overlooked. Thirdly, we did not include grey literature, but the grey literature we did identify did not contain any approaches or topics that were not covered by the peer-reviewed studies (Supplementary Note 2 ). Lastly, our findings, resulting from a qualitative content analysis, involved interpretation.

Nevertheless, by this systematic review, we connected the fragmented landscapes of studies that simulate food security governance implementation and provided a comprehensive overview of the state of the field, mapping dominant modelling approaches (ABMs, SDMs and CGE models), governance measures (treasure- and authority-based) and value chain echelons (production, trade and consumption). To be able to generate useful knowledge for the community (for example, for meta-analyses similar to those in public health 98 or ecology 99 ), however, future research could benefit from harmonization of food security indicators. We also identified blind spots regarding studies’ choices of governance measures, food security indicators and value chain coverage. We recommend the development of ABMs that simulate the implementation of nodal governance measures (for example, provision of seasonal weather forecasts for farmers 18 ); socially and spatially disaggregated models that consider socially vulnerable groups and bio-physical heterogeneity and simulate the implementation of socially and spatially targeted governance measures 21 , 22 , 65 ; macro–micro coupling or telecoupling to capture potential spillover effects (for example, trade moratorium on grains from Russia and Ukraine 28 ); and a re-orientation moving beyond availability and access-focused production, trade and consumption studies, towards utilization-, nutrition- and social justice-focused value chain studies.

Studies sought for review

Food security governance simulation studies sought for this review meet four criteria: they use simulation models (1) to assess the impact of governance measures ex ante (2) on food security (3) within a food system context (4). A paper was considered a simulation study if it used a model that simulates alternative scenarios with future projections or alternative (future) realities. Models that simulated alternative historical realities (≤1990) and empirical models that assessed the impact of governance that was already implemented were excluded. Additionally, our scope was limited to peer-reviewed English-language journal papers published after 2000. Governance was approached from a tool-kit perspective, looking at the tools that governance entities may use to affect society 11 . We distinguished four main categories of governance tools: nodality (information dissemination), authority (laws and regulation), treasure (financial incentives) and organization (capacity building, punishment and crisis management) 11 . Any study that described a governance measure that was described clearly enough to be categorized in this way is eligible for this review (Table 3 and Extended Data Table 1 ).

With regard to food security, we considered any study that assessed the impact of governance on an indicator for any of the four dimensions of food security: availability, access, utilization or stability (for full list of indicators, see Extended Data Table 1). Indicators related to food safety and overconsumption, though important aspects of the wider field of food governance, were beyond the scope of this review, and have been studied by others 100 , 101 , 102 . Food systems could potentially encompass all aspects of our lives, but for this review we confined them to the three elements identified by the High-Level Panel of Experts (2017) (ref. 103 ): (1) food value chains (including food production, storage, distribution, processing, packaging and retail), (2) food environments (the physical, economic, political or socio-cultural context within which people interact with their food) and (3) consumer behaviour. Aquatic food production, consumer behaviour outside of food environments, and hunting, gathering or fishing were beyond the scope of this review.

Search term selection

To identify food security governance simulation studies, we collected search terms from existing systematic literature reviews on the topics of (1) food systems, (2) food security, (3) simulation modelling and (4) governance (for list of reviews, see Supplementary Table 1 ). Different combinations of search terms were tested on Web of Science and Scopus. For each combination, we went through the first 40 studies. If fewer than two studies were eligible on the basis of abstract and title scanning, the term responsible for those results was dropped (for search terms, see Supplementary Table 2 ). To test the sensitivity of the selected search terms, the collection of identified studies was compared with the collection of studies identified for the review by Utomo et al. 33 . Terms used in studies from their review that met the inclusion criteria of our review were added. The final search string used to identify food security governance simulation studies on 21 April 2021 was:

((‘food system’ OR agricultur* OR farm* OR ((food OR agricultural) AND (‘value chain*’ OR ‘supply chain*’ OR value chain* OR supply-chain*)) OR agri-food OR ‘food processing’ OR ‘food production’ OR agri-business OR ‘food transfer*’ OR livestock OR pasture OR ‘food consumption’ OR ‘food purchase’) AND

(nutrition* OR diet* OR ‘food secur*’ OR ‘food insecur*’ OR ‘food access*’ OR ‘food availab*’ OR ‘food demand’ OR ‘food supply’ OR ‘food sovereign*’ OR ‘food sufficien*’ OR ‘food insufficien*’ OR ‘food utili[sz]ation’ OR hunger OR malnutrition OR poverty OR livelihood) AND

((agent-based AND model*) OR (multi-agent) OR (individual-based AND model*) OR (simulation AND model*)) AND

(govern* OR stewardship OR regime OR politic* OR polic* OR accountability OR incentiv*)).

Screening process

A total of 1,953 potentially relevant studies were identified through a database search in Scopus and Web of Science (for full list, see Extended Dataset 2). In addition to 624 duplicate studies, a total of 154 studies were excluded before screening as they did not meet the criteria: English-language peer-reviewed journal papers published after 2000 (for screening criteria, see Supplementary Figs 1 – 5 ). The remaining 1,175 studies were screened, first on the basis of titles, abstracts and keywords, then, if deemed eligible, on the basis of full text. The first round of screening was performed in Ryyan QCRI, a webtool that helps track and expedite the screening process 104 . The second round was performed in ATLAS.ti v9.1.7 (ref. 105 ). The PRISMA flow diagram (Fig. 1 ) illustrates the process of the selection of simulation studies for the systematic review.

Data extraction and analysis

Eligible studies were imported to ATLAS.ti for a directed, qualitative content analysis 106 where descriptive codes were used to extract text fragments containing information about the simulation approaches (for the ATLAS.ti project, see Data Repository 107 ). Coded information relating to characteristics 1–5 is specified in Table 1 . The text fragments, along with the descriptive codes attached to them, and meta-data concerning the document they were extracted from, were exported to Excel (for raw data, see Data Repository 107 ), then imported to R v4.1.0 for data processing and analysis. For each of the reported results, the number of studies in which (a combination of) codes occurred in was counted. If a study contained multiple code categories per variable (for example, multiple model domains), each code category was counted.

Reporting summary

Further information on research design is available in the Nature Research Reporting Summary linked to this article.

Data availability

The datasets generated during the current study are available in the FoodSecGovSim2 Code Repository on github ( https://github.com/ateeuw/FoodSecGovSim2 ) and on the Data Repository on Dataverse ( https://doi.org/10.7910/DVN/Q9WXC2 ) 107 . Source data are provided with this paper.

Code availability

The code used to generate the results described in this review can be found in the FoodSecGovSim2 Code Repository on github ( https://github.com/ateeuw/FoodSecGovSim2 ).

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Acknowledgements

This research was made possible thanks to the funding of the 4TU.HTSF DeSIRE programme of the four universities of technology in the Netherlands and the National Science Foundation-China (grant agreement 42001228). The authors thank Y. Georgiadou for helping us define and categorize governance measures.

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Conceptualization: A.S.T., M.A.M. and A.N. Formal analysis: A.S.T. Methodology: A.S.T. and M.A.M. Supervision: M.A.M., Y.D. and A.N. Original draft: A.S.T. Review and editing: A.S.T., M.A.M., Y.D. and A.N.

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Extended data

Extended data fig. 1.

Domains of food security governance simulation models.

Extended Data Fig. 2

Social and spatial targeting of governance measures by different simulation models.

Supplementary information

Supplementary information.

Supplementary Notes 1 and 2, Supplementary Tables 1 and 2, Supplementary Figs. 1–5 and supplementary references.

Reporting Summary

Supplementary data 1.

This table contains all the indicators used by the reviewed body of food security governance simulation modelling studies to assess the impact of governance on food security. The indicators are arranged according to the number of studies that use them, and the food security dimension (access, availability, utilization and stability) they belong to.

Supplementary Data 2

This dataset contains: (1) a list of all the screened studies and information on the databases we found them on, their eligibility and, if applicable, their reasons for rejection; (2) the ineligibility criteria; and (3) a simplified list of all eligible studies. More information can be found in the Dataverse repository (data availability statement).

Supplementary Data 3

This dataset contains information on (1) the PRISMA 2020 statement, comprising a 27-item checklist addressing the introduction, methods, results and discussion sections of a systematic review report, and (2) the 12-item PRISMA 2020 extension for abstracts.

Supplementary Data 4

Analyses of the quantitative impacts of governance measures on food security are important to inform decision makers about the effectiveness of different governance measures and, through this, speed up the alleviation of food insecurity. To facilitate such analyses, we explored which studies and governance measures within the reviewed pool of literature could be compared as they used the same food security indicators, measured with the same spatial and temporal precision, and were implemented either inside a jurisdiction, outside it or globally. Using these criteria for comparability, we found 26 groups with three to six studies, exploring up to 13 governance measures, that could potentially be compared in meta-analyses.

Source Data Fig. 2

This dataset contains information about which food security governance simulation studies use different model types in combination with different (1) model domains, (2) spatial scales, (3) geographic foci and (4) food value chain echelons.

Source Data Fig. 3

This dataset contains information on which combinations of food value chain echelons are covered by different food security governance simulation studies.

Source Data Fig. 4

This dataset contains information on food security governance studies' use of socially and spatially disaggregated data, and assessment of socially and spatially targeted governance.

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Teeuwen, A.S., Meyer, M.A., Dou, Y. et al. A systematic review of the impact of food security governance measures as simulated in modelling studies. Nat Food 3 , 619–630 (2022). https://doi.org/10.1038/s43016-022-00571-2

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A systematic literature review of indicators measuring food security

Abstract: measurement is critical for assessing and monitoring food security. yet, it is difficult to comprehend which food security dimensions, components, and levels the numerous available indicators reflect. we thus conducted a systematic literature review to analyse the scientific evidence on these indicators to comprehend the food security dimensions and components covered, intended purpose, level of analysis, data requirements, and recent developments and concepts applied in food security measurement. data analysi… show more.

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References 92 publication s, monitoring of relationships between indicators of food security of the states.

The conceptual foundations of monitoring the state’s food security system under the conditions of global turbulence have been deepened. It was determined that the set of indicators of the state of food security used in national practice is imperfect. It is emphasized that the totality of indicators cannot objectively contribute to identifying potential threats to sustainable development and adopting effective management decisions. Attention is focused on the need to consider the impact of other components, including the issues of the accessibility, availability, quality, and safety of food products, as well as their resilience and adaptability. The hypothesis regarding the direct relationship between the degree of compliance of agricultural production in the country with sustainable development principles and the overall level of economic accessibility of food has been proved. The established and mathematically proven direct relationship between the indicators made it possible to emphasize the need to raise the population’s living standards. This is necessary to reduce the negative impact of the production of agricultural products on the environment, which results from the understanding by consumers of the need to satisfy not only basic needs but also to preserve natural resources for future generations. It is mathematically proven that in countries with a high level of economic accessibility of food, the compliance of agricultural production with sustainable development principles has a high level and is strongly correlated with economic factors. In countries with medium and low indicators of economic accessibility of food, such a relationship does not have statistical significance. The conclusions drawn are useful for practical use in conditions of global economic turbulence.

Material insecurity and religiosity: A causal analysis

Some cultural evolutionary models predict that under stressful reductions of well-being, individuals will be more attracted and fastidiously adhere to traditional systems of norms that promote solidarity and cooperation. As religious systems can bolster human relationships with a variety of mechanisms, the material insecurity hypothesis of religion posits that individual religiosity will increase under conditions of material insecurity. The bulk of the literature up to this point has been correlational and cross-national. Here, across 14 field sites, we examine the causal role that educational attainment and food insecurity play in religiosity. We find that years of formal education and food insecurity do not consistently contribute to individual religiosity cross-culturally. We conclude with a discussion of some theoretical and methodological implications. As a general workflow for cross-cultural causal research in the quantitative social sciences, the present work is a modest but necessary first step in reliably estimating causation in the material insecurity hypothesis of religiosity.

Day Scholars Food Insecurity Experience Scale-Survey Module (DSFIES-SM): Psychometric Analysis

Background: School feeding programs’ evaluation requires the measurement of food insecurity, a more objective indicator, within school in low-income countries. The Global Child Nutrition Foundation (GCNF) uses subjective indicators to report school feeding coverage rates across many countries that participate in the global survey of school meal programs all year round. Aim: To test the methodological feasibility of measuring a school food insecurity construct as a direct indicator of the effectiveness and efficiency of school feeding programs. Methods: Two-stage sampling was used during the selection of schools and 128 schoolchildren with a mean ( SD) age of 10.5(1.58) were recruited. Item Response Theory (IRT) and Classical Test Theory (CTT) approaches were utilized during DSFIES-SM development. Psychometric analysis was utilized to assess the psychometric properties of the measure of school food insecurity and to establish the construct-level reliability, convergent and discriminant validity of the DSFIES-SM. Results: DSFIES-SM generated acceptable item-level reliabilities, ranging from .75 to .78. The construct-level reliability of the DSFIES-SM was indicated by Cronbach’s α of .78. Composite reliability was at .77. Fit measures and tests of model fitness for confirmatory factor analysis (CFA) (RMSEA < 0.0001; TLI = 1.06; SRMR = 0.043; CFI = 1.00, p = .85) confirmed that the data fitted the model perfectly. Conclusion: DSFIES-SM consists of twelve questions with dichotomous yes/no responses. DSFIES-SM has good convergent and discriminant validity. Findings suggest that the items compose a statistical scale designed to cover a range of severity of school food insecurity. Future replications to establish other forms of validity across different cultural contexts in low-income countries can be of benefit to the present research.

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Measuring Food Insecurity in India: A Systematic Review of the Current Evidence

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Purpose of Review

India is home to an estimated 200 million malnourished people, suggesting widespread food insecurity. However, variations in the methods used for determining food insecurity status mean there is uncertainty in the data and severity of food insecurity across the country. This systematic review investigated the peer-reviewed literature examining food insecurity in India to identify both the breadth of research being conducted as well as the instruments used and the populations under study.

Recent Findings

Nine databases were searched in March 2020. After excluding articles that did not meet the inclusion criteria, 53 articles were reviewed. The most common tool for measuring food insecurity was the Household Food Insecurity Access Scale (HFIAS), followed by the Household Food Security Survey Module (HFSSM), and the Food Insecurity Experience Scale (FIES). Reported food insecurity ranged from 8.7 to 99% depending on the measurement tool and population under investigation. This study found variations in methods for the assessment of food insecurity in India and the reliance on cross-sectional studies.

Based on the findings of this review and the size and diversity of the Indian population, there is an opportunity for the development and implementation of an Indian-specific food security measure to allow researchers to collect better data on food insecurity. Considering India’s widespread malnutrition and high prevalence of food insecurity, the development of such a tool will go part of way in addressing nutrition-related public health in India.

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A systematic literature review of indicators measuring food security

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Introduction

Food insecurity has been identified as a “pressing public health concern” in India [ 1 •]. At the household level, food security exists when all members, at all times, have access to enough food for an active, healthy life [ 2 ••]. Individuals who are food secure do not live with hunger or fear starvation. Across urban settings, the prevalence of food insecurity has been found to range from 51 to 77%, yet over 70% of India’s population resides rurally, where data concerning food insecurity is limited [ 3 ].

The concept of food security consists of six main dimensions: availability, access, utilization, stability, agency, and sustainability. The first three dimensions are interlinked and hierarchical. Food availability is concerned with ensuring that sufficient quantities of food of appropriate quality are supplied through domestic production or imports (including food aid). Access to food is necessary but not sufficient for access. Access is concerned with ensuring adequate resources, or entitlements, are available for the acquisition of appropriate foods for a nutritious diet. Access is necessary but not sufficient for utilization. Utilization is concerned with the ability of an individual to access an adequate diet, clean water, sanitation, and health care to reach a state of nutritional well-being. The three other concepts have become increasingly accepted as important, as risks such as climatic fluctuations, conflict, job loss, and epidemic disease can disrupt any one of the first three factors. Stability refers to the constancy of the first three dimensions. Agency is recognized as the capacity of individuals or groups to make their own food decisions, including about what they eat, what and how they produce food, and how that food is distributed within food systems and governance. Finally, sustainability refers to the long-term ability of food systems to provide food security and nutrition in a way that does not compromise the economic, social, and environmental bases that generate food security and nutrition for future generations [ 4 ••].

Two hundred million people living in India are estimated to be malnourished [ 5 •]. Poverty, a lack of clean drinking water, and poor sanitation have been identified as common factors contributing to malnutrition in India [ 1 •]. Yet to date, despite high rates of malnutrition pointing toward widespread food insecurity [ 6 ], the link between food insecurity and malnutrition in India has seldom been explored. Of the limited data available, associations have been found between household food insecurity and child stunting, wasting, and being underweight [ 7 ], highlighting the urgency of food insecurity as a public health priority.

Considering the high rates of child stunting, wasting, and overall malnutrition in India, exploring past and emerging research which has both assessed and addressed food insecurity is a crucial step in better understanding nutrition-related health at the population level. Currently, to the best of our knowledge, there is no published systematic review which has explored household food insecurity in India. To understand the factors that contribute to food insecurity at the household level, the related health and nutrition outcomes, and to conceptualize potential strategies which target food insecurity in India, a systematic review of published research undertaken to date which has focused on food insecurity in India is urgently needed. This review seeks to (1) systematically investigate the peer-reviewed literature that purports to investigate food insecurity in India, (2) identify the breadth of research being conducted in India, including the instruments used and the populations under study, and (3) provide an overview of the severity of food insecurity in India as presented by these studies.

A systematic search was undertaken to identify all food security research conducted at the household level in India. The search was conducted in March 2020. Key search terms were based on the FAO [ 8 ] definition of food security: “food access*,” OR “food afford*,” OR “food insecur*,” OR “food poverty*,” OR “food secur*,” OR “food suppl*,” OR “food sufficien*,” OR “food insufficien*,” OR “hung*” AND “household*” OR “house*” AND “India.” Searched databases included Academic Search Complete, CINAHL Complete, Global Health, MEDLINE, Embase, SCOPUS, ProQuest, PsychInfo, and Web of Science. To gain a full collection of articles that reported on research investigating household food security in India, no limits were placed on publication dates. Only peer-reviewed articles published in English were considered; unpublished articles, books, theses, dissertations, and non-peer-reviewed articles were excluded. This review adheres to the PRISMA statement [ 9 , 10 ], see Fig. 1  for a flowchart describing the process of screened included and excluded articles.

Flow chart of articles meeting search criteria, number of articles excluded, and final number of articles meeting inclusion criteria for review

Two authors (FHM and AS) and a research assistant reviewed all articles to identify relevant studies. Articles underwent a three-step review process (see Fig. 1 ). All articles were downloaded into EndNote X7, duplicates were identified and removed, and the article titles, journal titles, year, and author names were then exported to Microsoft Excel 365 to facilitate reviewing. Articles were first screened by title and abstract based on the inclusion and exclusion criteria described above by two authors independently. Any article that clearly did not meet the inclusion criteria was removed at this stage, any that did, or possibly could meet the inclusion criteria on further inspection, were retained. The full text of the remaining articles was obtained, and at least two authors (FHM and AS) or a research assistant independently read all 161 articles that remained at this stage to determine if the article met the inclusion criteria. Any articles at this stage that clearly did not meet the inclusion criteria were removed. Any disagreements on those that were retained were discussed and settled by consensus between the authors.

Articles that discussed food insecurity in general but collected no new data (for example, Gopalan [ 11 ] and Gustafson [ 12 ]) were excluded, as were previously conducted reviews in the region (for example, del Ninno, Dorosh [ 13 ], Harris-Fry, Shrestha [ 14 ]). As this review was primarily interested in studies that purported to measure food insecurity in India, studies that discussed food insecurity, either as the standard measured construct or as a construct created by the authors but termed food insecurity, were included. While there are many non-government organizations and inter-government organizations that work to measure food or nutritional insecurity, the construct of “hunger,” the associated conditions of malnutrition (either with overweight or obesity) or conditions that might indicate malnutrition (including anemia or under-5 mortality), these reports generally do not include a complete description of the method used to collect data if data were collected at the household level and often use the sale or production of crops as a proxy; as such, these reports have been excluded from this review.

Data were extracted from each article by the three authors. Data were extracted into a Microsoft Excel 365 spreadsheet that allowed for the capture of specific information across all included articles. Data extracted at this stage included the following: location; population group; findings; measured food security (Y/N); method for determining food insecurity; and prevalence of food insecurity.

The search identified 1018 articles, of which 395 were duplicates. The titles and abstracts of the remaining 616 articles were read, with 518 articles excluded as they did not refer, either directly or indirectly, to food insecurity research in India, leaving 161 articles for further investigation. The full text of the 161 articles was reviewed; 108 articles were excluded as they did not meet the inclusion criteria. The remaining 53 articles were included in this review.

Most articles ( n  = 48, 90%) were cross-sectional studies; three were longitudinal, with data covering 27 years [ 15 ], 11 years [ 16 ], and 4 years [ 17 ], and one was a randomized controlled trial [ 18 ]. Eight studies employed a mixed methods approach, seven were qualitative, and the remaining 38 were quantitative studies. Participant numbers ranged in size from the smallest study with 10 participants [ 19 ] to population-level studies with over 100,000 participants [ 15 , 20 ]. See the supplementary material for an overview of the studies included.

Most food insecurity research was conducted in the state of West Bengal, where 9 studies were conducted, followed by 6 studies each in Maharashtra and the union territory of Delhi (see Fig. 2 ). India consists of 28 states and 8 union territories; this review found research from 17 states and five union territories, as well as four nationwide studies showing good coverage across the country.

Distribution of studies exploring food insecurity in India

Measuring Food Insecurity

All studies included in this review purported to measure food insecurity directly, with the main aim of the majority ( n  = 45, 85%) of articles to determine the prevalence of food insecurity. These articles employed a range of measurement tools to achieve this aim. The most common way to measure food insecurity was via the Household Food Insecurity Access Scale (HFIAS) which was employed in 17 studies. The second most common method employed to measure food insecurity was via the Household Food Security Survey Module (HFSSM), employed in 13 studies. Other measures of food insecurity include the Food Insecurity Experience Scale (FIES), used in three studies, the Comprehensive Nutrition Survey in Maharashtra used in two studies, and the Radimer/Cornell used in one study. The remaining 17 studies used a proxy measure, either one devised by the authors or by using data from the India National Sample Survey (NSS). See Table 1 for an overview of these measurement tools.

The prevalence of food insecurity in these studies ranged from 8.7 to 99%; 13 studies stated that they measured food insecurity but did not report food insecurity results. The most common way for food insecurity to be measured in India was through employing Household Food Insecurity Access Scale (HFIAS). This experiential scale was designed to be used cross-culturally and consists of nine questions, with frequency questions asked if participants experience the condition. Responses to these questions are scored so that “never” receives a score of 0, “rarely” is scored 1, “sometimes” is scored 2, and “often” is scored 3, so that when summed, the lowest possible score is 0 and the highest is 27. A higher score represents greater food insecurity, with continuous scores typically divided into four categories, representing food-secure and mildly, moderately, and severely food-insecure households according to the scheme recommended by the HFIAS Indicator Guide [ 21 ]. The scale is based on a household’s experience of problems regarding access to food and represents three aspects of food insecurity found to be universal across cultures [ 22 , 23 , 24 ]. This scale measures feelings of uncertainty or anxiety about household food supplies, perceptions that household food is of insufficient quality, and insufficient food intake [ 21 ]. The questions asked in the HFIAS allow households to assign a score along a continuum of severity, from food secure to food insecure. Food insecurity measured via the HFIAS ranged from 77.2% in a population of 250 women who resided in an urban area in South Delhi [ 25 ] to 8.7% in Indian children [ 26 ].

The second most common measurement tool identified in this search is the US Household Food Security Survey Module (HFSSM). This tool was developed to measure whether households have enough food or money to meet basic food needs and what their behavioral and subjective responses to that condition were [ 27 ]. The HFSSM module consists of a set of 18 items, 8 of which are specific to households with children. It captures four types of household food insecurity experiences: (1) uncertainty and worry, (2) inadequate food quality, and insufficient food quantity for (3) adults and (4) children [ 28 ]. It is available in an 18-item and 6-item forms and allows households to be assigned a category of food insecurity: high food security, marginal food insecurity, low food insecurity, and very low food insecurity. In accordance with the method proposed by Coleman-Jensen et al. [ 29 ], food security scores are combined to create one measure for the level of food security for a household. Food security status is determined by the number of food-insecure conditions and behaviors that the household reports. Households are classified as food secure if they report fewer than two food-insecure conditions. They are classified as food insecure if they report three or more food-insecure conditions, or two or more food-insecure conditions if they have children. Food-insecure households are further classified as having either low food security if they report between three and five food-insecure conditions (or three and seven if they have children), or very low food security if they have six or more food-insecurity conditions (eight if they have children). Studies that employed the HFSSM reported food insecurity ranging from 15.4 [ 30 , 31 , 32 ] to over 80% of study participants [ 33 ]. The HFSSM is a commonly used measure of food insecurity and can be used in several valid forms. Studies included in this review used the 4-, 6-, and 18-item versions of the HFSSM.

The Food Insecurity Experience Scale (FIES) module was used by three studies included in this review. The FIES questions refer to the experiences of the individual or household. This scale was created by the Food and Agriculture Organization of the United Nations (FAO) and has been tested for use globally [ 28 ]. The questions focus on self-reported food-related behaviors and experiences associated with increasing difficulties in accessing food due to resource constraints. The FIES allows for the calibration of other measures, including the HFIAS and the HSSM with the FIES against a standard reference scale allowing for comparability of the estimated prevalence rates of food insecurity [ 34 ], as well as a raw score that can be used by authors as a way to create discrete categories of food insecurity severity [ 35 ]. The three studies that employed the FIES all reported food insecurity within a range of 66–77%, despite different population groups, locations, and sample sizes.

One study employed the Radimer/Cornell scale, a widely used and validated scale [ 36 ]. The scale includes ten items that relate to food anxiety and the quantity and quality of food available. The instrument allows for the categorization of households into four categories of food insecurity: food security, household food insecurity, individual food insecurity, and child hunger.

The Comprehensive National Nutrition Survey (CNNS) was used in two studies. It is a state-specific (Maharashtra) nutrition survey with a focus on infants and children under two and their mothers. The CNSM measured household food security using nine questions [ 37 ]. The questions capture experiences of uncertainty or anxiety over food, insufficient quality, insufficient quantity, and reductions in food intake [ 38 ]. Households are categorized as food secure, mildly food insecure, moderately food insecure, or severely food insecure.

The National Sample Survey (NSS) organization conducts nationwide household consumer expenditure surveys at regular intervals in “rounds,” typically 1 year. These surveys are conducted through interviews with a representative sample of households [ 20 ]. This survey includes only one question about household daily access to food [ 39 ], and while it does provide a method for estimating food insecurity in India, it assumes that financial access equates to physical access to available food; as such, this survey is unlikely to be able to comprehensively capture the intensity of household food insecurity in India [ 40 ]. Four studies employed the NSS. Given that these studies did not specifically collect food insecurity data, the use of the NSS has been considered a proxy indicator here as it generally reflects the measurement of food availability or acquisition rather than food insecurity per se.

Other proxy measures were commonly used. The variety of proxy measures included information on calorie intake, purchasing power, the quantity of food consumed, and agricultural productivity. These proxy measures provide only a partial, usually indirect, measure of food insecurity [ 41 ]. There are also challenges with these measures, as the relationship between food and caloric quantity and household food security is unpredictable [ 42 ]. For example, in a study of households in Gujarat, Sujoy [ 43 ] found that around 85% of households are food insecure at some point in a typical year. This study employed a range of measures to explore the experiences of hunger and food insecurity and the strategies employed by these population groups to mitigate hunger. Exploring the food insecurity experiences of farmers in Bihar, Sajjad and Nasreen [ 44 ] found that 75% of households had very low food security. While not using a standard measure, Sajjad and Nasreen [ 44 ] interviewed households alongside interviews with government officials, food production, food costs, and food acquisition to form an index of food security that could be applied at the household level. A study by George and Daga [ 45 ] using calorie consumption as a proxy for food security identified 57% of participants were food insecure, with the suggestion that income and family size play a role in food security among children. Of the 17 studies that employed a proxy measure of food insecurity, 10 provided no indication of the level of food security in their results.

Population Groups Under Investigation

Studies identified in this review included a variety of population groups. Most studies ( n  = 30) focused on food insecurity at the household level; half of these studies employed one of the standard food insecurity measurement tools, while the other half relied on proxy measures.

Fourteen studies focused specifically on young children, and one on teenagers. These studies used a variety of methods to determine food insecurity among this population, with rates of food insecurity shown to range from 8.7 [ 26 ] to 80.3% [ 33 ]; within this range, most studies reported that food insecurity among children was in the range of 40 to 60%. Interestingly, while the study conducted by Humphries [ 26 ] reported lower levels of child food insecurity (8.7%) than the other studies included in this review, other findings of this study were consistent with other research reviewed. Across all studies that explored food insecurity among children and teenagers, findings suggest problematic infant and young child feeding practices, caregiving, and hygiene practices, with many studies reporting impaired growth in children and teenagers due to these practices.

Seven studies focused specifically on the experiences of women or used the experiences of women as an indicator of food insecurity in their households. All of these studies employed one of the standard measures of food insecurity, with food insecurity in these studies ranging from 32 [ 3 ] to 77.9% [ 46 ]. These studies identified a range of health outcomes related to food insecurity and hunger. For example, in a study of mothers of children under the age of 5, Das and Krishna [ 47 ] found that two-thirds of households were food insecure and that younger mothers were more likely to be food insecure, with the children of these mothers more likely to be underweight and stunted. Among mothers in a study by Chyne et al. [ 48 ], those who had low literacy levels, low income, and large family size were more likely to be food insecure, with many of the children of these mothers being vitamin A deficient, anemic, stunted, and/or wasted. This is consistent with the work of Chatterjee et al. [ 49 ] who found that food insecurity among women was associated with low income and a range of socioeconomic measures including education, employment, and relationship status.

Thirteen studies were conducted in slums. Four of these studies were conducted in slums in Delhi, finding that food insecurity among slum populations ranges between 12% among children aged 1–2 years [ 50 ] and 77% in households more broadly [ 25 ]. Three studies were located in slums in Kolkata, all conducted by Maitra and colleagues [ 30 , 31 , 32 ]. These studies found food insecurity to be 15.4%, finding that low income, household composition, and education are all predictors of household food insecurity. The remaining studies were conducted in slums in Jaipur [ 51 ], Mumbai [ 49 ], Varanasi [ 52 ], Vellore [ 53 ], and West Bengal [ 33 , 54 ]. Slums are an important setting for an exploration of food insecurity, especially in India, where 25% of the urban population resides in slums or slum-like settings. People living in slums have been found to have poorer quality of life, are generally lower income, and have lower educational attainment than non-slum-dwelling populations—all factors that are known to contribute to food insecurity [ 49 ].

Five studies explored food insecurity among people with an underlying health condition. Four of these explored food insecurity among people living with HIV/AIDS [ 55 , 56 , 57 , 58 ]. These studies found that food insecurity ranged from 16 to 99% with people who are food insecure and also living with HIV/AIDS more likely to experience depression and a lower quality of life [ 57 ] and that low income [ 58 ] and low education [ 55 ] are contributing factors to food insecurity, while ownership of a pressure cooker was found to be protective against food insecurity [ 56 ]. Finally, one study explored the experiences of food insecurity among people with tuberculosis [ 59 ]. This study found that around 34% of study participants were food insecure, with low income and employment being associated with food insecurity status.

India has seen massive growth and economic change over the past 2 decades; however, this increase in financial wealth has had little impact on food insecurity and population nutrition [ 60 ]. While India has increased production and, overall, the availability of food has increased [ 61 ], these increases have not yet translated into gains for the general population. Overall, India is seeing increasing income inequality which is having a negative impact on health [ 62 ]. As a result of the disconnect between economic growth and positive health outcomes, there has been an increased interest in food insecurity and nutrition in India over the past two decades, resulting in research that seeks to measure food insecurity.

The main finding of this study is the variation in the methods for the assessment of food insecurity prevalence in India and the reliance on cross-sectional studies to elicit food insecurity data. This may be explained by the fact that food security is notoriously difficult to measure. Initial descriptions of food insecurity were conceptualized through the lens of famine [ 63 ], meaning that solutions were often confined to domestic agriculture [ 41 ]. However, in an increasingly globalized world where countries easily sell and buy goods from each other, it is now important to consider food security in a holistic manner, incorporating the whole definition of food insecurity. By considering the six main dimensions of food security: availability, access, utilization, stability, agency, and sustainability, we can better understand the experiences and drivers of food security. However, as this review has found, few studies measure more than one dimension.

Studies included in this review utilized scales that focused on household food access or availability and were assessed through experience-based scales. Experiential food insecurity scales have been used since the 1990s [ 64 ], first used in the USA and later adopted for use in low- and middle-income countries [ 21 , 65 ]. Experiential measures are based on the notion that food insecurity is associated with a set of knowable and predictable characteristics that can be assessed and quantified [ 17 , 21 ]. This assumes that households will attempt to mitigate food insecurity through a generalizable or standard pattern of responses [ 17 , 22 ]. Strategies include reducing expenditure on education expenses [ 66 ], selling assets or seeking increased employment [ 67 ], and skipping meals or limiting the sizes of meals [ 68 ]. Measures of food insecurity that are based on experience seek to capture some of these strategies and actions, and compared to other metrics, such as agriculture production, caloric intake, or anthropometric measures, they enable direct measurement of the prevalence and severity of the extent of household food insecurity, as well as the perception of the quality of their diets [ 31 ].

Given the wide variety of measurement tools used, it is difficult to present a comprehensive understanding of food insecurity in India. What is clear is that some households are experiencing food insecurity but are not hungry, while others are both hungry and food insecure. Finding a way to identify and measure at-risk households and intervene to reduce hunger is essential to closing the economic-income gap in India. However, without a measure that can be used consistently across the country that takes into consideration each of the dimensions of food security and the diversity within the Indian population, this will not be possible.

Limitations

There are some limitations to this review that should also be acknowledged. While every attempt was made to ensure this review was comprehensive, additional articles may have been missed, particularly if articles were written in a language other than English. However, given that this is the first review of its kind, with the inclusion of several databases and broad key terms, the authors are confident that there is little information that is not presented here. The articles presented in this review are largely cross-sectional, and as such, the quality of the studies means that the conclusions drawn by their authors are limited to the study population and are not widely generalizable. The cross-sectional nature of many of the studies limited the potential impact of quality assessment; as such, no quality assessment was conducted. This is a limitation of both this review and the studies included, and in general, a reflection on the rigor with which food security research has been conducted in these settings. Given the variety of approaches taken to measure food insecurity as found in this review, there are challenges in comparing the outcomes of different studies; as such, this review has not sought to present a meta-analysis. If, in the future, there can be some consistency in the use of measurement tools by researchers and agencies, a meta-analysis may be appropriate. The authors do not feel this should invalidate these findings at this time.

An Indian-specific food security measure needs to be urgently developed and implemented so that food insecurity data can more accurately and consistently be collected and contrasted for the purpose of developing suitable responses to food insecurity. Considering India’s widespread malnutrition and high prevalence of food insecurity, future work should prioritize the development of such a tool in addressing nutrition-related public health in India.

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McKay, F.H., Sims, A. & van der Pligt, P. Measuring Food Insecurity in India: A Systematic Review of the Current Evidence. Curr Nutr Rep 12 , 358–367 (2023). https://doi.org/10.1007/s13668-023-00470-3

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Accepted : 09 March 2023

Published : 06 April 2023

Issue Date : June 2023

DOI : https://doi.org/10.1007/s13668-023-00470-3

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