literature review on food insecurity in kenya

• 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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Conceptualization, IM, BA and BS; methodology, IM, BA and BS; formal analysis, IM, BA and BS; investigation IM and BS; data curation, BA; writing—original draft preparation, IM, BA and BS; writing—review and editing, IM, BA and BS; visualization, BA; supervision, IM and BS; project administration, IM and BS; funding acquisition, IM and BS All authors have read and agreed to the published version of the manuscript. All authors read and approved the final manuscript.

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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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Shocks, socio-economic status, and food security across Kenya: policy implications for achieving the Zero Hunger goal

Emily Mutea 1,2 , Md Sarwar Hossain 5,3 , Ali Ahmed 4 and Chinwe Ifejika Speranza 1

Published 7 September 2022 • © 2022 The Author(s). Published by IOP Publishing Ltd Environmental Research Letters , Volume 17 , Number 9 Citation Emily Mutea et al 2022 Environ. Res. Lett. 17 094028 DOI 10.1088/1748-9326/ac8be8

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1 Institute of Geography, University of Bern, 3012 Bern, Switzerland

2 Centre for Training and Integrated Research in ASAL Development (CETRAD), Nanyuki, Kenya

3 Environmental Science and Sustainability, School of Interdisciplinary Studies, University of Glasgow, Dumfries, United Kingdom

4 Initiative for Climate Change and Health, International Centre for Diarrhoeal Disease Research Bangladesh, Dhaka, Bangladesh

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• Received 5 February 2021
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This study assessed the association between shocks, socio-economic factors, and household food security across Kenya, and provided policy implications for achieving the Zero Hunger goal at national and local levels in Kenya. We analysed the Kenya Integrated Household Budget Survey 2015–16 data for 24 000 households by employing regression models. Our multiple findings show that: (a) half of the surveyed population across Kenya were food insecure; (b) large disparities in food security status exist across the country; (c) demographics (e.g. gender, urban areas), and other socio-economic aspects (e.g. education, income, remittances), positively influence food security; and (d) social and economic shocks negatively influence food security. In summary, the food security status in Kenya is not satisfactory. Our findings suggest that, in general, achieving the sustainable development goals (SDGs) Zero Hunger goal by 2030 will likely remain challenging for Kenya. Ultimately, a redoubling of efforts is required to achieve SDG 10 (reducing inequality) to ensure no one is left behind. Further, the findings could be useful in the formulation and implementation of national and regional policies for achieving the Zero Hunger goal by 2030 in Kenya.

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1. Introduction

For decades, one of the most popular global goals of human society has been to reduce persistent food insecurity. Actions included the declaration of food security as a basic human right in 1948, the World Food summit of 1996, the Millennium Development Goals of 2001, and the 2015 sustainable development goals (SDGs). Despite these remarkable initiatives, the status of food security in various world regions is far from satisfactory. For example, the second SDG on Zero Hunger is behind track and will only be achieved with substantial additional efforts (United Nations Department For Economic And Social Affairs 2019 ). By definition, food insecurity is limited physical, economic, or social access to food, while hunger is the uneasy or painful sensation caused by insufficient consumption of food (Jones et al 2013 , FAO 2019 ). The Food and Agriculture Organisation (FAO) of the United Nations ( 2019 ) frames hunger as chronic undernourishment.

According to the most recent report by the FAO, one in ten people are food insecure. More than two billion people globally are experiencing moderate or severe food insecurity, and at least 690 million people are still hungry (Davis et al 2020 , FAO, IFAD, UNICEF, WFP & WHO 2020 ). While food security across the world is slowly improving, sub-Saharan Africa is the only region in the world where food insecurity has risen since 2014. More than one-quarter of the population in Eastern and Middle Africa is food insecure (Coughlan de Perez et al 2019 , FAO 2020 ).

Among these Eastern African countries, Kenya is one of the most food insecure; it has made slow progress in achieving its millennium development goal targets, and its progress in achieving the SDGs (in particular the Zero Hunger goal) lags behind expected achievements (FAO, IFAD, UNICEF, WFP & WHO 2020 , Musyoka et al 2020 ). Food insecurity in Kenya affects 2.6 million people, with significant differences between counties and regions (KNBS 2018a ). In general, more than half of the population in Kenya is suffering from moderate to severe food insecurity. Kenyan arid and semi-arid lands, urban slums, and rural households have high food and nutrition insecurity compared to the national averages (FAO, IFAD, UNICEF, WFP & WHO 2020 ). Kenya was ranked 86 out of 113 countries for food insecurity by the global food security index in 2017 (Government of Kenya—GoK 2018 ). Despite several national and international initiatives, Kenya still is in the level of serious hunger with a rank 84th out of the 107 countries globally in 2020 (GHI 2020 ).

Achieving the Zero Hunger goal by 2030 will be highly challenging due to the future impacts of climate change (Stevens and Madani 2016 , Niles and Brown 2017 ), spatial distribution of the food insecure population (Hossain et al 2016 ), and social and economic shocks at household, local, and national levels (FAO, IFAD, UNICEF, WFP & WHO 2020 , Ingram 2020 ). Understanding the association between food security and socio-economic characteristics is necessary to understand the way multiple factors influence food security across different scales (FAO 2013 , Ingram 2020 ).

Shocks are additional threats to achieving household food security (DFID 2003 , Ifejika Speranza et al 2008 , Alinovi et al 2010 ). In general, shocks are events that can cause significant reduction of wellbeing such as income loss and food insecurity (Marques 2003 ), and typically sudden disturbing events (e.g. floods, epidemics or rapid rise in food prices), with often unpredictable and traumatic impacts such as collapse of livelihoods and economies. Further, shocks can be sudden social changes (e.g. the death of a household member) (Berend 2007 , Kozel et al 2008 ) which also increase vulnerability and threats to food security (DFID 2003 ). Socio-economic factors, conflicts or climate trigger shocks such as a food crisis due to sudden rise in food prices and increased income inequality (FAO 2019 ). Economic, social, and environmental shocks prolong and exacerbate the severity of acute food insecurity (Conklin et al 2018 , Cottrell et al 2019 ). This is because they reduce households' ability to maintain food security. If ignored, these shocks may have unpleasant effects on food insecurity in all its forms.

The FAO ( 2019 ) notes that shocks disproportionately challenge food security in places where inequalities in the distribution of socio-economic factors and other resources are profound. One way to overcome this problem is to understand better the impacts of such disparities in order to prioritise actions and implement tailored strategies depending on available resources (Hong et al 2019 ). There is thus a need to monitor all SDGs at regional and sub-regional levels to identify ways to reduce inequalities, an aspect addressed in SDG 10. In particular, reducing inequality within countries helps to ensure the progress of SDGs, leaving no one behind. Ultimately, it is important to understand the spatial pattern of food insecurity and recognise the drivers associated with the food insecure population using reliable data sets. This will help to monitor variability in food insecurity and its drivers and thus provide scientific knowledge for long-term planning to achieve Zero Hunger through geographically and socially targeted interventions.

Previous studies on food security in Kenya mostly focused on demand and access to food (Koir et al 2020 ), household vulnerability to food security shocks (Musyoka et al 2020 ), impacts of drought on food security and gender perspective (Huho and Mugalavai 2010 , Kassie et al 2014 ) and basics of food consumption and poverty status (KNBS 2018b ). However, it has not yet been explored how household socio-economic characteristics in the context of combined social, environmental, and economic shocks influences household food security across Kenya. Most studies are based on case studies (e.g. Ulrich et al 2012 , Mutea et al 2019 ) of food security making it difficult to gain an overview of food security at the county and national levels. Yet, data collected for national overviews such as the Kenya Integrated Household Budget Survey 2015–16 (KIHBS), can fill this gap of gaining a national and county level overview of food security and complement insights gained from case studies. Thus, we analyse the spatial heterogeneity of food security and the associated drivers (socio-economic factors and shocks) using the 2015–16 KIHBS collected across Kenya in order to provide policy insights for achieving the Zero Hunger goal in the methods section, we explain the 2015–16 KIHBS datasets and data analysis (logistic regression) including how we define food security. Next, we explain the results focusing on food security across Kenya, and the association with shocks and socio-economic characteristics, before discussing the progress of food security and policy implications for achieving the Zero Hunger goal in Kenya. This novel study highlights the usefulness of national-level datasets for understanding food security in Kenya and could be useful in the formulation and implementation of national and regional policies for achieving the Zero Hunger goal by 2030 in Kenya and other similar East African countries.

2.1. Data and variables

The KIHBS 2015–16 data is a household survey that collects information from the Kenyan population in order to guide national development policy decisions (KNBS 2018a ). The KIHBS questionnaire, designed by experts, is a set of modules that are administered to collect information on household characteristics, housing conditions, education, general health characteristics, nutrition, household income and credits, household transfers, information and communication technology, domestic tourism, shocks to household welfare, and access to justice. From these key variables, we chose our outcome and predictor variables for food insecurity.

The survey was conducted by the Kenya National Bureau of Statistics from September 2015 to August 2016. Three-stage sampling was followed in order to determine sample size independently for each of the 47 Counties of Kenya, resulting in a planned national sample of 24 000 households. However, due to missing values, the total sample consists of 21 773 households. The samples are representative at the national level, the county level ( n = 47), and the local level (urban or rural place of residence). We limited our analysis to KIHBS 2015–16, as the previous dataset KIHBS 2005–2006 is not consistent with the current dataset of KIHBS 2015–16, which has been improved in terms of indicators and data collection. For example, the number of indicators for shocks and food items are higher in KIHBS 2015–16 due to the inclusion of new variables. Some other variables such as remittances have been recently included in KIHBS 2015–16. In addition, some of the variables such as dead and stolen livestock are divided into two shocks in KIHBS 2015–16. Therefore, considering these points, we limited our approach to the cross-sectional analysis of the KIHBS 2015–16.

2.2. Data analysis

The outcome variable was household food security. We measured this variable using indicators proposed by the International Food Policy Research Institute (Smith and Subandoro 2007 , Szabo et al 2015 ). The approach considered two key indicators of food security: the percentage of total household expenditure on food and the total daily calorie availability at the household level. The survey did not explicitly assess food security using these indicators, therefore, we combined variables in the dataset to compute the aforementioned food security indicators.

The share of total household expenditure (as a proxy of income) spent on food is an indicator of household food security because it is widely documented that the poorer and more vulnerable a household, the larger the share of household income spent on food. A rise in food prices results in a higher share of total household expenditure spent on food, which constrains poorer households' resources. These force poor households to spend more on basic staples, reduce the quality of their diets, or even reduce the quantity consumed of the least expensive foods, while also reducing non-food expenditures that may be equally needed such as on health and education (Lele et at 2016 ). This indicator uses the monetary value of household consumption disaggregated into food and non-food items. Thus, the share of household food expenditure is equal to the percentage of expenditure on food divided by total expenditure (Smith et al 2014 ). A household was categorised as food insecure if more than 75% of its total expenditure went on food items (Smith and Subandoro 2007 ).

In the calorie-based food security analysis, a household was classified as food secure if daily calorie requirements were higher than total reported energy intake per capita. We made a final categorisation based on the combination of the above two variables; a household was categorised as food secure if at least one of the above conditions was met. This study used two key categories of predictor variables: household socio-economic characteristics and shocks to household welfare that comprised 19 and 22 independent variables, respectively (table 1 ). On one hand, socio-economic characteristics comprise factors such as education, income and social support that influence households' wellbeing. On the other hand, shocks are sudden events such as death of household head that make households vulnerable.

Table 1.  Key categories of predictor variables used in regression modelling.

We performed logistic regression modelling in order to test the main predictor variables influencing household food security. Before running the regression modelling, polychoric correlation was used as a test for independence and multicollinearity. In polychoric correlation, variables are redundant if the correlation is higher than 0.70 (Aletras et al 2010 ), As a result, we dropped the following variables: marital status, source of domestic water, electricity connection, source of cooking and lighting energy, number of livestock, and large rise in food and farm input prices.

Given that the outcome variable was dichotomous, we applied a series of logistic regression models with food security as the outcome variable in all the models to check the robustness of the final regression model. Model 1 examined the relationship between the outcome variable (food security) and 18 predictor (independent) variables defining the shocks to household welfare. The second model included the socio-economic characteristics in addition to the model 1 predictor variables (shocks). The third model included the outcome variable while the predictor variables were shocks and socio-economic characteristics excluding household remittances. The fourth model represented (Model 2 and assests) the relationship between the outcome variable and shocks, socio-economic characteristics, and assets as the predictor variables. The adjusted regression model with predicted variables was specified as follows:

3.1. Food security across Kenya

The description of the studied households' characteristics is presented in the supplementary file. Overall, 52% of households were food secure. This classification was based on a combination of calorie deficiency and food expenditure indicators as explained in the data analysis section. Of the 52% food secure households, 70% and 30% were male and female-headed households, respectively and, 51%, 12% and, 37% of households were food secure respectively in rural, peri-urban and urban areas. The prevalence of food security was similar between households that did not practice agriculture and those involved in agriculture (50% and 50% respectively).

Regarding our food security indicators, calorie deficiency was the major cause of food insecurity, affecting 84% of households. The mean calorie intake per adult equivalent was 2828 ± 12 calories. Moreover, 58% of the households spent over 75% of their income on food, making them food insecure. Surprisingly, rural households spent on average 79% of their income on food, whereas for urban households this figure was 62%.

However, as shown in figure 1 , there were variations across the country, with less than 50% of households found to be food insecure in almost half of the counties. Based on our analysis, households across Kenya were divided into four clusters according to food security status (10%–30%, 31%–50%, 51%–70% and 71%–90%) across the 47 counties. This aimed to simplify the food security status across counties by allowing a quick glance on counties that have similarities in terms of food security status across the country, useable for future interventions. Cluster one contained four counties (7% of the total—Nairobi, Mombasa, Machakos, and Kiambu), with over 70% of households being food secure. Most of these counties are in the central region, with one in the coastal region. Cluster two comprised 21 counties (45% of the total), where over half of households were food secure. Cluster three comprised of 20 counties (42%), where more than 50% of households were food insecure. The fourth cluster contained two counties from the north-eastern arid region (Wajir and Mandera), with 85% and 75% of households living in food insecurity respectively. Surprisingly, in Garissa County, which is also in the north-eastern arid region, 59% of households were food secure.

Figure 1.  Food security status across the 47 counties of Kenya. See SI table 2 for county wise food security data.

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3.2. Association of food security with shocks and socioeconomic variables

The results of regression modelling between household food security and the predictor variables (see table 2 ) are shown in table 3 . These results are based on Model 4. Regression analysis showed that household demographics (e.g. gender of household head, household size), socio-economic characteristics (e.g. remittances, household income, farming, cooking appliances, television), and four types of shock (death of livestock, death of household head, death of a working household member, jail term for household head) significantly influenced household food security (table 3 ).

Table 2.  Regression results with household food security as the outcome variable (*0.05 **0.01 ***0.001) OR: odds ratio, CI: 95% confidence interval and P > |z| : significant.

Table 3.  Regression results for the four models with household food security as the outcome variable (*0.05 **0.01 ***0.001).

Among the socio-economic variables, household income and remittances were the strongest predictors of household food security across the 47 counties. For instance, the OR (95% CI) of becoming food secure were 1.54 (1.42–1.68) from receiving remittances compared to those that did not receive remittances. The odds of becoming food secure from receiving remittances were higher in urban areas (1.86, p = 0.00) compared to rural (1.47, p = 0.00) and peri urban (1.52, p = 0.00) areas. The odds of food security for households increased along with household income.

Households headed by a woman were 21% (95% CI: 1.12–1.32) more likely to be food secure than male-headed households. Households with secondary education had 0.88 times the odds of households with no education for food security. The odds of food security were higher for households with primary and university education than for those with no education, but these results were not significant. In comparison to households living in rural areas, households in urban areas had higher odds (0.89, p = 0.04) of food security (95% CI: 0.78–0.99).

However, in terms of the age of the household head, an increase in age was only associated with a very slight increase in household food security: an OR of 1.01. The OR of food security for families that owned a television were 0.62 (95%: CI 0.55–0.69) and statistically significant ( p = 0.00). The odds of food security for households not engaged in agriculture were 20% higher than for households in agriculture.

The regression model showed that only four shocks out of 19 (breadwinner jailed, death of household head, death of a working household member, death of livestock) were found to have a significant influence on food security. Death of livestock was found to have severely and significant influence on food security in rural areas (0.81, p = 0.00) compared to urban (0.79, p = 0.23) and peri-urban (1.01, p = 0.92) areas of Kenya. The majority (53%) of counties had encountered all four shocks; 34% had been hit by three (death of household head, death of a working household member, and death of livestock); and 13% had experienced two types of shocks (death of livestock and household head). All four significant shocks were social and economic in nature and had a negative impact on household food security. Interestingly, no environmental shock had a significant effect on household food security.

The odd ratios of food security after death of livestock were 21% less than cases where no livestock had died. This result was statistically significant at p -value 0.00. The most affected region was the Rift valley, followed by Eastern and Nyanza. Similarly, households that experienced death of household head ( p = 0.00, 95% CI: 0.58–0.91) or working household member ( p = 0.03, 95% CI: 0.45–0.96) were ∼40% less food secure than households who had not experienced these shocks.

Regression Model 1 shows that nine shocks were statistically significant (table 4); in contrast, in Model 4, only four shocks remain statistically significant considering socio-economic variables. Regression Models 2, 3, and 4 showed that socio-economic variables were a strong predictor of food security. Furthermore, social and economic shocks had a stronger influence on food security than environmental shocks. Considering the lowest values of AIC and BIC from regression results, we argue that Model 4 performed best among the four models.

4. Discussion

4.1. progress and drivers of food security.

This study assessed food security status at the national level and across the 47 counties of Kenya. Additionally, we assessed the socio-economic aspects and shocks affecting household food security. Our findings show that half of the households across Kenya were food insecure. Out of the 47 counties, 25 counties were within national food security levels, while the rest were below the national average. However, our results also indicate differences in food security levels across the 47 counties in Kenya.

This study reveals a positive association between food security and socio-economic variables such as gender of household head, family size, remittance, and income. These results are in line with those of previous studies (Babatunde and Qaim 2010 , Szabo et al 2015 , Mutea et al 2019 , Paul et al 2019 ).

Our analysis also revealed a negative significant association between household food security and socio-economic characteristics (e.g. ownership of a television set) and shocks (e.g. death of livestock, death of a working household member, death of household head).

We found that all the shocks were spread more or less equally across the 47 counties, with the most common being death of livestock. This implies that for those households owning livestock, death of livestock and by extension ownership of livestock are significant drivers of food security. Livestock keeping (e.g. sheep, goat, dairy cows and poultry) in urban areas makes important contributions to the livelihoods of urban livestock keepers (Roessler et al 2016 , Alarcon et al 2017 , Pablo et al 2017 , Crump et al 2019 ). Urban livestock keeping is a source of food security due to provision of essential micronutrients to avoid malnutrition and can release pressure on poor households (that spend 60%–80% of income in food) (Alarcon et al 2017 ). Rearing livestock enables smallholders to have improved livelihoods and to avoid food insecurity through income generation and can be used as a coping strategy during times of need (Nabarro and Wannous 2014 ).

Kenya has addressed the issue of food security in its Vision 2030 plan and the present government's 'big four' agenda. These initiatives emphasize investing in agriculture, with the aim of transforming agriculture from subsistence to productive commercial farming as a pathway to food security (GoK 2007 , 2018 ). However, our findings reveal that households not involved in agriculture are 20% more likely to be food secure. There are two possible explanations for this result.

First, most of Kenya is semi-arid and its agricultural production is challenged by climate variability and climate change, use of outdated technology, poor infrastructure (especially roads linking farmers to markets), soil degradation, regions with low cropping potential, diseases and pests, lack of fallows, and nutrient amendments (Foeken and Owuor 2008 , Thornton and Herrero 2016 , KARI 2019 ). These problems result in little or no harvest, leading to food shortage and hence food insecurity.

Surprisingly, no significant impacts on food security were found from environmental shocks such as droughts, floods, pests, and diseases, which are usually related to climate variability. This could be due to the cross-sectional datasets of KIHBS, collected from September 2015 to August 2016. Longitudinal datasets are often a prerequisite for analysing the social impacts of climate change (Geffersa and van den Berg 2015 , Bahruid et al 2019 ). As droughts and floods are widespread in Kenya, they are systemic factors that can affect all inhabitants hence socio-economic characteristics becomes a differentiating and important factor in face of such system-wide exposures. This may be the reason for the non-significant association between food security and environmental shocks such as drought as the result show a non-significant possibility of 10% less food security for households experiencing drought and flood. In addition, households are also adapting to diversified livelihoods, resulting in less dependency on agriculture, where resources are becoming increasingly scarce (Babatunde and Qaim 2010 , Menike and Arachchi 2016 ). In response to coping with drought, households mostly depend on livestock when adapting to climatic change (Ifejika Speranza 2010 ). Often environmental shocks (e.g. diseases, drought, floods etc) trigger livestock diseases, which may lead to livestock death, so environmental shocks can be the underlying drivers of livestock loss, which then directly affects food security.

In addition, we found that female-headed households were more likely to be food secure than male-headed households. There were no major variations across the counties in terms of gender of household head, with over 60% of households being male-headed in most counties. A possible explanation for this outcome is that women play a decisive role in dietary diversity at the household level. Other scholars have also found a significant association between the availability of a diverse diet at household level and women's participation in decision-making (Amugsi et al 2016 ). Women are also more involved in a variety of food system activities such as production and processing food, which are key in food availability and utilisation. However, such households are more often reported to be less endowed with necessary resources such as land and finances compared to male-headed households, which makes them vulnerable to food insecurity (Kassie et al 2014 ).

4.2. Policy implications for achieving the Zero Hunger goal in Kenya

The results suggest (figure 2 ) that given current conditions, achieving the Zero Hunger goal by 2030 is achievable for very few counties (e.g. those with 60%–70% population food secure) in Kenya; the rest (less than 40% of population food insecure) will likely continue to be food insecure for a long time if no additional efforts are put in place. These findings suggest that, in general, achieving Zero Hunger by 2030 will likely remain challenging for Kenya. This is because of the huge variations and disparities existing across the country. There are four counties that could certainly meet this goal, even before 2030. Twenty one further counties, with some effort, could feasibly be food secure by 2030. However, it is highly unlikely that the remaining 22 counties will be 100% food secure by that time. Considering the results, that social and -economic shocks had a stronger influence on food security than environmental shocks, holds implications for achieving the zero-hunger goal.

Figure 2.  Progress of the Zero Hunger goal to achieve food security across counties (A > 50%, B < 50% food secure) in Kenya. See SI table 2 for county-wise food security data.

First, there is a need for actions to improve system-wide resilience to environmental shocks. While these shocks seem not to have significant impacts at the inter-household level, they condition the agricultural production system for all households through influencing natural production conditions. Measures are thus needed to reduce the sensitivity of crop and livestock production systems to environmental shocks. Kenya is guided by several strategic documents towards the achievement of food security: nationally by Vision 2030 and the 'big four' agenda (GoK 2007 ); its national adaptation plan and drought management strategies to end drought emergencies (GoK 2016 ), regionally by the African Union (AU) Malabo Declaration (AU 2014 ); and globally by the United Nations (UN) post-2015 goals (UN 2019 ). The effectiveness of such initiatives thus needs to be monitored to ascertain to what extent they address the systemic vulnerability underpinning food insecurity in Kenya.

Second, our results show that attaining food security for all involves more than just producing more food, even though increasing agricultural production is a big part of the solution to eradicating hunger. The results highlight the need to also address disparities in socio-economic characteristics. It is important that governments comprehensively combine sustainable agricultural investments with cross-sectoral developments (e.g. appropriate technology, market infrastructure) to improve agricultural production and to diversify and increase income levels. This approach has worked well in Ghana, leading to agricultural development (Adolph and Grieg-Gran 2013 ). Elsewhere, in Malawi and Bangladesh, subsidies have been effective in reducing food insecurity and contributing to environmental sustainability (Hossain et al 2016 ), hence such an option is worth exploring for Kenya.

Moreover, to ensure no one is left behind along the Zero Hunger goal pathway, it is essential to redouble efforts towards addressing the challenges that affect the most food insecure counties in terms of socio-economic characteristics. On the more challenging side, access to quality education is essential, as educated households are food secure. Our results suggest that households with secondary and other types (primary and university) of education are significant and non-significant respectively, but have a positive influence on food security in Kenya. Therefore, achieving other SDGs such as quality education (SDG 4) is necessary to end hunger across Kenya.

This study was limited to a cross-sectional (snapshot of a single moment in time) analysis, with the aim of ascertaining policy implications for achieving the Zero Hunger goal by assessing the status and drivers of food security at both national level and administrative unit (county) level. An analysis of qualitative data and a longitudinal study (repeated observations) considering seasonality of shocks may offer a deeper contextual understanding of the impacts of environmental shocks on food security, its complexities, and its subjectivity. Further studies that extend and collect social and ecological datasets may also offer an understanding of the interactive relationships between the Zero Hunger goal and other goals, which would help to set meaningful targets and achieve these targets comprehensively. However, the result of our study will be useful for assessing how Kenya has progressed in terms of the Zero Hunger goal and for guiding national and regional policies aimed at progressing towards achieving the SDGs in Kenya and other similar East African countries by 2030.

5. Conclusion

Food security analysis across Kenya can provide important information about achieving the Zero Hunger goal; it can also be useful for decision-makers at global, national, and local levels. In this research, analysis of KIHBs datasets has shown that demographics (e.g. gender of household head, family size,) and other socio-economic characteristics (e.g. income, remittances, education) are positively associated with food security and that social and economic shocks (e.g. death of household head or livestock) are negatively associated with food security across Kenya. In general, food security status both at national and county levels is not satisfactory. It is unlikely that Kenya will be able to achieve the Zero Hunger goal by 2030, considering current food security levels, social (e.g. poverty, inequality) and environmental (e.g. climate, land degradation) challenges, and the ambitious targets set out by the SDG for Zero Hunger goal. These findings highlight the usefulness of regular (e.g. every 5 yr) collections of data sets at national-level for understanding food security, and can complement insights from household food security surveys, considering the larger efforts needed for case studies at household and local levels.

Acknowledgments

The main author received support from the Swiss Government Excellence Scholarships for Foreign Scholars and Artists: ESKAS 2017.0930. The authors are also thankful to the Kenya national bureau of statistics for making the data available. M S H acknowledges Marie Skłodowska-Curie Grant Agreement No. 796994, under the European Union's Horizon 2020 research and innovation programme. CIS acknowledges the Swiss Programme for Global Issues on Development (r4d programme) funded by the Swiss Agency for Development and Cooperation and the Swiss National Science Foundation [Grant number 400540_152033].

Data availability statement

The datasets used and/or analysed during the current study are available from the Kenya National Bureau of Statistics. The data that support the findings of this study are not openly available due to the copyright of Kenya National Bureau of Statistics.

No new data were created or analysed in this study.

Author contributions

E M and M S H conceptualized the idea for this manuscript and the data analysis plan. E M and A H analyzed the data. E M contributed to the writing of first draft manuscript, the analysis and interpretation of the data with help from M S H. M S H, A H, and C S, participated in the writing of the manuscript. All authors read and approved the final manuscript.

Conflict of interest

The authors declare no competing interests.

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Methodology, case studies, conclusion and recommendations, a review of the impact of climate change on water security and livelihoods in semiarid africa: cases from kenya, malawi, and ghana.

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Dinko Hanaan Dinko , Ibrahim Bahati; A Review of the Impact of Climate Change on Water Security and Livelihoods in Semiarid Africa: Cases From Kenya, Malawi, and Ghana. Journal of Climate Resilience and Justice 2023; 1 107–118. doi: https://doi.org/10.1162/crcj_a_00002

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Within semiarid Africa, precipitation is the most important hydrological variable upon which livelihoods are carved since it determines the cycle of rainfall and water security needed for agriculture. However, research shows that climate change has largely altered that. This article critically reviews the extensive literature on climate-water-livelihoods in semiarid sub-Saharan Africa, highlighting the common threads that underlie them. By comparing three cases in three different regions (Ghana for West Africa, Kenya for East Africa, and Malawi for Southern Africa), this article provides a basis for cross-comparison and a framework for understanding the impact of climate change on water security and livelihoods in semiarid Africa. A cross-country, cross-region comparison of the impact of climate change on water security is essential for long-term and medium-term preparedness for adaptation to climate-induced water insecurity. Crucially, this calls for a renewed focus on the synergies between climate change and social, ecological, political, and economic factors, which have often been ignored in the water insecurity and climate change discourse on semiarid areas.

Water resources in Africa’s semiarid regions have come under pressure over the last 4 decades, to warnings of reaching near “dangerous levels of water stress” ( World Bank, 2022 ). Due to climate change, water insecurity in Africa and beyond has brought an existential debate about water ethics in terms of use, access rights, and sustainability ( Groenfeldt, 2019 ). Water insecurity includes elements of water scarcity where the water demand exceeds water availability and lack of access to safe water supplies ( Matchawe et al., 2022 ). Swelling urban growth, environmental degradation, and anthropogenic pollution continue to limit access for large populations in the region ( Kahn, 2009 ). With livelihoods in semiarid Africa carved around rainfed agriculture, the impact of climate change and variability on food and income security remains uncertain, putting the discourse of water insecurity into the greater hydropolitics of water ( Hellberg, 2018 ). For example, Kankam-Yeboah et al. (2013) have projected a 50% decrease in streamflow in the Volta Basin by 2050. Similarly, Barron et al. (2015) have discussed how agricultural water management interventions for smallholders in the Volta and Limpopo basins could be best utilized to build resilience against climate change. The impact of climate change on floods and droughts in terms of vulnerability and disaster risk reduction in the northern savannah has been explored ( Armah et al., 2010 ; Douxchamps et al., 2014 ; Poussin et al., 2015 ). While these are important in the climate-water-livelihoods discourse in semiarid Africa, they tended to be basin-specific ( Abubakari et al., 2017 ; Kankam-Yeboah et al., 2013 ; Mahe et al., 2013 ; Niasse, 2005 ; Oyebande, 2013 ), model-oriented ( Faramarzi et al., 2013 ; Muller, 2009 ; Nyadzi et al., 2018 ; Roudier et al., 2014 ; Thomas & Nigam, 2018 ), or subregion focused ( Barry et al., 2018 ; Callo-Concha et al., 2013 ; Oyebande, 2013 ; Paeth et al., 2008 ; Yaro & Hesselberg, 2016 ).

Transcending these gaps, this article provides a rapid review of the climate-water-livelihoods literature in semiarid sub-Saharan Africa, highlighting the common thread that underlies them. The article first looks at individual case analyses of how Ghana, Kenya, and Malawi are dealing with climate-water-insecurity, followed by cross-comparison in understanding the impact of climate in semiarid Africa. The article aims to highlight how climate change and water insecurity are urgencies of both nation-states and regions, calling for short and long-term adaptation and preparedness to climate-induced water insecurity.

Furthermore, water security issues need to be tied to the greater debate on the political economy of tackling climate change ( Fritz et al., 2021 ), including adaptation ( Sovacool & Linnér, 2016 ), framing, and knowledge dissemination ( Armstrong et al., 2018 ), and understanding the relationship between climate change and capital accumulation ( Xie & Cheng, 2021 ). Developing countries, including those in semiarid Africa, have been the least contributors to climate change, yet still operate in the confines of the managerial climate change policy approach from the Global North ( Arnall et al., 2014 ), including talks of “just transitions” ( Newell & Mulvaney, 2013 ) to green economies. The political economy of climate change here deals with nuances between the social and political processes on how water insecurity has affected livelihoods and created urgencies of disaster preparedness in semiarid Africa.

We conducted a rapid review of the literature on climate change and water security in the three countries. Unlike a systematic review, a rapid review does not require a double review of each paper. Additionally, a rapid review limits analysis to only the papers from the queried results database. Although it is less systematic, rapid reviews provide well-timed and data-informed contextualized summaries of the literature for policymakers to address evolving issues quickly ( Kerr et al., 2022 ; Khangura et al., 2012 ; Sharpe et al., 2017 ), while shaping ongoing scholarly discourse. We conducted a systematic search in Web of Science using Boolean operatives and keywords as shown in the Supporting Information . Using the search criteria, 1,150 papers were refined further to journal articles, reviews, and book chapters. This process generated 1,030 papers (see the Supporting Information for details). The 1,030 papers were screened for contextual relevance, subject relevance, and credibility (see Figure 1 ). We used two main criteria to determine the credibility of papers. First, papers were deemed credible if methods, data, and conclusions logically flow into each other. Second, the papers were deemed credible if they were not published in journals on Beall’s List ( Beall, 2022 ).

Summary of refined Web of Science database search. *Some papers are cross-listed across disciplines. Generated from Web of Science at Clarivate Query.

After screening for relevance, 154 papers were meticulously reviewed, and 84 ended up being used in the article. The 84 papers were then categorized into the three case studies and read immersively to allow key themes of differences and similarities to emerge. In addition, eight grey literature sources from government and the World Bank were included in this article to provide relevant contextual data for the three countries (see Table 1 ). We included all studies from 1990 to 2021 that addressed the relationship between climate change, water security, and impacts on livelihoods in the three countries. Papers that did not explicitly examine the intersections of climate change impacts on water insecurity and livelihoods were excluded.

Summary of Web of Science Searches and the Number of Papers Reviewed

The following subsections show how climate change has altered the cycle of rainfall and caused water insecurity in semiarid Africa. It presents literature by case analysis in the three countries, highlighting the trajectory of climate change evidence, projections on water insecurity, and regional implications on livelihoods.

Kenya Case Study

In Kenya, agriculture remains the main driver of economic growth and employs more than half of the labor force, which is reliant on the availability of water. The importance of agriculture is reflected in the fact that in 2017, agriculture contributed to 65% of merchandise exports ( Wankuru et al., 2019 ). With 80% of the landmass being semiarid and less than 2% of arable land under irrigation ( Mogaka et al., 2006 ), Kenya’s economy is particularly vulnerable to climate change and variability. Compared to neighboring Tanzania and Uganda with 2,940 and 2,696 cubic meters of water per capita per year, respectively, Kenya has just about 1,700 cubic meters per capita per year ( Wankuru et al., 2019 ). This makes Kenya a water-scarce country under the United Nations (UN) water classification system ( UN-Water, 2013 ). The hydrology of Kenya is largely governed by the rainfall regime as there are very few transboundary rivers in Kenya. It is also determined by the movement of the Intertropical Convergence Zone (ITCZ), which produces two rainfall seasons and two dry seasons. The ITCZ has been disrupted largely by climate change. This is acknowledged by the government of Kenya, which asserts that the country is generally experiencing a warmer temperature trend over the past 5 decades ( GoK, 2013 ). In addition, Nicholson (2016) reports a decreasing rainfall over the semiarid areas in Kenya since the 1970s. Nicholson (2014) further demonstrates that during the 2008–2011 drought in the Horn of Africa, rainfall in northern Kenya was 50–70% below normal seasonal rainfall the decade earlier.

Additionally, drought in Kenya is often driven by La Niña. With multiple consecutive years of droughts, a result of poor rains and dry spells over the past decade, there has been little to no recovery among affected households whose livelihoods are determined by the rhythm of the climate. This puts pressure on existing water resources and thus brews competition for access, control, and use rights to water bodies. In a region characterized by instability and uncontrolled arms circulation, such contestations have often resulted in violent armed conflicts ( Dinko, 2022 ). In semiarid northern Kenya, Witsenburg and Adano (2009) have argued that rainfall does not just determine water availability, but it determines pasture, crop yields, and milk availability. As the water gets scarcer during drought seasons, pressure on shallow wells increases, and the propensity of people to fight for access similarly escalates. Beyond tensions in social relations, droughts have a significant impact on food and livestock production. For instance, the 1990/2000 drought resulted in a decline of one million tons in maize production ( GoK, 2013 ). Such steep declines in a major food staple such as maize have had a knock-on effect on the prices of food, leading to nationwide food insecurity protests recently in July 2022 ( “About 3.5 million Kenyans Facing Food Insecurity—WHO,” 2022 ). Like the food crop sector, livestock production has suffered significant losses in drought years ( Barrios et al., 2010 ; Hope et al., 2012 ; Mogaka et al., 2006 ; Sutherland et al., 1991 ).

Water insecurity resulting from climatic change and variability does not just manifest in droughts but also in floods. While floods may not be as frequent as droughts in Kenya’s semiarid regions, their devastating impact cuts across key sectors of the economy. The flooding regime in Kenya is often associated with the onset of the El Niño warming effect on the tropical pacific region ( Barrios et al., 2010 ; Dunning et al., 2018 ; Gebrechorkos et al., 2019 ; Otieno & Anyah, 2013 ; Nicholson, 2014 ). Unlike droughts whose onset is slow, and whose response could be planned, flash floods are often sudden, and in semiarid Kenya where there is little investment in climate science, floods can be devastating. Opere (2013 , p. 13) reports that the 1997–1998 floods in Kenya “caused some US$151.4 million in public and private property damage” and several hundreds of lives lost. Aside from damage to life and infrastructure, floods also pose a significant threat to public health. Mogaka et al. (2006) show that after the 2003 floods, there was a 60% rise in waterborne diseases and a 32% increase in malaria cases. Wakeford (2017) notes that food and health security are not the only casualties of droughts in Kenya. With 35% of its energy needs dependent on hydroelectricity, the ramifications of droughts reverberate beyond food and ecosystem security to the entire economy ( Karekezi et al., 2009 ; Wakeford, 2017 ). In 2018, the Sondu Miriu Hydroelectric Power Station with an installed generation capacity of 80 megawatts could only generate 10 megawatts ( Gebrechorkos et al., 2019 ). Such a sharp reduction in generating capacity limits economic growth, which in turn has chain effects on well-being and human development in the long run.

Malawi Case Study

In Malawi, climate change poses a significant threat to the economic growth and livelihoods of poor and vulnerable populations. The vulnerability of Malawi to climate change emanates from the fact that agriculture, which supports the livelihoods of 80% of Malawians, is rainfed ( Arndt et al., 2019 ). In addition, Malawi’s industrial front is predominantly agrarian, hence the entire economy is immensely vulnerable to the forces of climatic change. Malawi ranks 171 out of 189 on the league of wealth and poverty nations with a Human Development Index (HDI) of 0.477 ( African Development Bank, 2018 ). Although its HDI increased by 40% between 1990 and 2017, more than half of the population (50.7%) live below the poverty line, while a quarter (25%) are chronically poor ( United Nations Development Programme [UNDP], 2021 ). With more than 90% of the population dependent on rainfed agriculture, climate extremes as manifested in droughts and floods could significantly erode yields and consequently food security. Joshua et al. (2016) indicate that over 15% of Malawians were affected by the 2012/2013 flooding, translating into 2.31 million people in need of food and associated aid while 176 people were killed and a quarter of a million people were displaced.

With climate change expected to increase the frequency of weather extremes, the other climatic threat (besides floods) Malawi is expected to witness is droughts. Observed temperatures over Malawi in the past 50 years indicate an increasing trend of about 0.21°C per decade ( Msowoya et al., 2016 ; Vizy et al., 2015 ). Nicholson et al. (2014) report a 1°C increase in temperature between 1960 and 2006. While there is a clear trend in temperature increases, the rainfall trend is less clear. Mughogho (2014) , for instance, finds that farmers perceived a decreasing amount of rainfall with increasing within-season variability. Ngongondo et al. (2011) similarly report that increases in evaporation losses between 1971 and 2000 have led to a decreased runoff. When taken together, increased temperature and declining rainfall mean that Malawi has experienced less than the usual amount of water. This projection toward a hotter and drier climate is not limited to Malawi, but rather stretched to the whole of the Southern African region as per Intergovernmental Panel on Climate Change ( IPCC, 2013 ), noting a likely increase of 5°C by the end of the century. This is similar to what Mariotti et al. (2013) suggest, that Malawi and other countries with a single rainy season will experience a delay in the onset of rains and when rains start, long dry spells will likely be common. In the context of Malawi, where the population growth rate is about 3% ( African Development Bank, 2018 ), this could mean food insecurity and pressure on water resources in the face of a burgeoning population. For instance, Asfaw et al. (2015) suggest that maize production, the predominant food crop accounting for 70% of cropped land in Malawi, has been erratic due to a combination of climate change and other nonclimatic factors, including low technology uptake.

Finally, the food insecurity and poverty situation outlined above essentially highlights water availability or lack thereof (as manifested in floods and droughts) and its impact on agriculture output. De Wit and Stankiewicz (2006) contend that increasing temperature and a concomitant decline in rainfall could lead to a 10% drop in river flow in the Zambezi basin, which covers much of Malawi. This will have a direct impact on water availability for drinking, agricultural use, and hydroelectric power generation in Malawi. Similarly, Kumambala (2010) finds that water levels in Lake Malawi will decline due to increasing droughts and evaporative loss from warmer temperatures. With 92% of Malawians having access to water mainly through surface water sources, which are rain-dependent, changes in precipitation could increase the water insecurity situation.

Ghana Case Study

Surface water is crucial to agriculture and power generation in Ghana’s semiarid region. Climate studies have increasingly indicated rainfall, the source of water upon which surface water sources depend, is decreasing in semiarid Ghana. For instance, Nicholson et al. (2000) reports a reduction of 15 to 40% in rainfall over 30 years (1968–1997) across semiarid West Africa. These findings are consistent with assertions by Owusu and Waylen (2009) that the total amount of rainfall in northern Ghana has declined since the 1960s. The Government of Ghana’s assessment of climate change further acknowledges that Ghana has experienced about a 1°C rise in temperature and a 20% overall reduction in rainfall since 1980 ( U.S. Environmental Protection Agency [EPA], 2000 ). The above findings have been contested by Antwi-Agyei et al. (2017) and Appiah (2019) who observe an improvement in rainfall in recent years, albeit that the recovery has been in the southern forested areas of Ghana. While these contending findings are useful for academic debates, they both use mean annual temperature and rainfall, which may not be relevant because they fail to show within-season variability. In semiarid Ghana, what is important is rainy season variability. It is the unpredictability of seasonal variations that have serious implications on crop production and water insecurity issues. In other words, farmers’ experiences of climate are not in annual averages, but crucially the distribution of rainfall during the rainy season, which has implications on staple crops and water security outcomes for households.

Generally, an overwhelming majority of local climate models in semiarid Ghana point to drying trends, where semiarid areas such as Ghana will get drier, while the wet tropical forest regions will get wetter. A key proponent of the drying thesis is reported by Amadou et al. (2018) , who projects that the mean daily temperature over Ghana will increase by between 2.5°C and 3.2°C, while rainfall is expected to decline by 9 to 27% by the end of the century. This scenario is consistent with observations that rainfall has generally declined over the last 50 years in West Africa due to the long-term general southward shift of the migration of the ITCZ ( Dickinson et al., 2017 ).

Changes in rainfall translate into food and water security challenges. In Ghana’s semiarid region, there is growing evidence that the impacts of climate change will significantly alter the water security cycle with debilitating consequences on food security and poverty reduction and undermine adaptive capacity ( Dinko et al., 2019 ; Nyantakyi-Frimpong & Bezner-Kerr, 2015 ; Yaro, 2013 ). According to Ghana’s Third National Communication Report to the UNFCCC , observed historical minimum temperatures have increased by 2% in the south (rainforest, coastal agroecological, deciduous, and transition zones) and 37% in the north (Guinea and Sudan savannah zones) ( Amlalo & Oppong-Boadi, 2015 ). When taken together as a geospatial unit, the average rate of climate change may present modest changes in Ghana. However, this picture is misleading as it masks wide spatial variation of observed and projected climatic changes.

Like observed and projected temperature changes, rainfall decline is greater in the Sudan Savannah than in any other agroecological zone. Because agriculture is almost exclusively rainfed coupled with limited diversification of livelihood options, the decline in rainfall has the potential to offset large-scale multiple shocks to the Ghanaian economy. The combined ramifications for national security could be dire.

Linking climate change with ongoing demographic and agricultural land expansion in semiarid Ghana highlights the scope and nature of future vulnerability to climatic shocks and stress. Grazing land and livestock production (which is predominant in semiarid Ghana) are vulnerable to climate change for three plausible reasons. First, decreasing precipitation and increasing evaporation due to rising temperatures in semiarid regions could potentially reduce the primary productivity of grazing land and accompanying livestock carrying capacity. Second, prolonged droughts could directly lead to the loss of herds. The third reason is a loss of biomass. Repeated and prolonged drought could decimate the capacity of soil to regenerate sufficient biomass to sustain growing livestock. This may leave the soil unable to recover even during wetter periods.

Beyond climate impact on agriculture, the effect of a changing climate on water bodies in semiarid areas presents a significant threat to livelihood security. Studies by Alcamo et al. (2003) , Ojo et al. (2004) , and Riede et al. (2016) forecast that by the year 2050, rainfall in West Africa will decline by 10%, prompting major water shortages. They further reason that the 10% decrease in precipitation would translate into a 17%–20% reduction in runoff, while semiarid regions such as semiarid Ghana may experience a reduction of 50%–30%, respectively, in the surface drainage. With a population growth above 2.7% ( Bongaarts & Casterline, 2013 ; Yansaneh, 2005 ), competition and pressure on water resources could double within this same period in the Sudan Savannah. This could lead to a decline in agricultural production and significantly affect food inflation, thus affecting food availability, access, and stability. The northern savannah belt faces an even more serious dilemma. The region is already experiencing a decline in soil fertility, declining yields, and environmental desertification. Declining precipitation could exacerbate these stresses and throw poverty reduction efforts out of gear.

Comparative Analysis of the Three Countries and Key Takeaways

This literature review examined the intersections of climate change and water insecurity in semiarid Africa using Kenya, Malawi, and Ghana as case studies. In three cases, there is growing evidence that climate change has negatively impacted water security, and the trend is projected to continue. The predominance of rainfed agriculture coupled with the fact that agriculture remains the largest single employer in all three countries particularly make them sensitive to climate change and variability. The sector accounts for roughly 40% of employment in Kenya and Ghana and about 80% of employment in Malawi ( Wankuru et al., 2019 ). Intersecting with high dependence on rainfed agriculture is low human development, which explains the low autonomous and institutional adaptive capacities.

While the above shows similarities among the three cases, there exist some differences that must be highlighted. Generally, while Kenya and Ghana are expected to endure increasing temperatures and a simultaneous decline in rainfall, Malawi is expected to receive a modest increase in rainfall overall with associated floods. Malawi, however, is expected to endure the greatest temperature increase of all three cases, as Table 2 shows.

Summary of the Nature of Climatic Changes in the Three Case Studies and Implications on Water Security

Table 2 shows both the observed and projected changes of climate change in Kenya, Malawi, and Ghana from the likelihood of turning into extreme events and the most likely impact it will cause on water security and livelihoods. In all three countries, we observe that there has been an increase in temperatures by 1°C from the 1980s to the late 2000s. However, by the end of the century, there is a projected increase in temperature of 3.2°C for Ghana, 4.5°C for Kenya, and 6°C for Malawi, leaving Kenya and Malawi more susceptible to intense droughts and floods, heatwaves, and severe droughts than Ghana. Also, Kenya and Malawi will experience more water stress in terms of evaporation losses and unpredictable rainfalls, aggravating food production and livelihoods more than Ghana. The FAO AQUASTAT (2022) data in Figure 2 further shows that from 1995 to 2019, the percent of people in Kenya who have become water stressed has increased from 14.8% to 33.2%, followed by Malawi (12.7% to 17.5%) and Ghana, which has moved from 3.7% to 6.3% of people who are water stressed.

Percentage of water-stressed people in Ghana, Kenya, and Malawi from 1990 to 2019. Compiled from FAO AQUASTAT (2022) .

In semiarid Kenya, climate-induced water insecurity has led to violent armed conflicts over water resources. Prolonged droughts have plunged millions of people into hunger necessitating a declaration of a humanitarian crisis over the Horn of Africa 1 . Violent conflicts over water resources in semiarid Kenya have thrust to the fore the role of water insecurity in exacerbating existing societal tensions. It also shows how already fragile societies can further disintegrate under the threat of climate-induced water insecurity. In comparison to Ghana and Malawi, climate-induced water insecurity has not led to violent armed conflicts, albeit anecdotal evidence suggests there are growing contestations in semiarid Ghana for access to and control over water for dry season farming and rearing of animals.

This article reviewed the literature on the nexus of climate change and water security in semiarid Africa, focusing on three cases from Kenya, Malawi, and Ghana. It has highlighted the nature and extent of climatic changes and how these changes intersect with water security in semiarid Africa. Generally, while climate change is driving water insecurity in semiarid Africa, the literature confirms that the preexisting socioeconomic conditions have exacerbated their vulnerability. Through a comparative analysis of the three countries, the review of the literature shows that there are synergies between climate change and social, ecological, political, and economic factors that have often been ignored in the water insecurity and climate change discourse in semiarid areas. There is an urgent need to examine the contestations arising from multiple and competing uses of surface water and how policy engagements can bring fair regulation of access outcomes. Again, with climate-induced water insecurity likely to increase, sufficient knowledge is needed to understand how internal functions of language, culture, and politics continue to determine who gets access rights to water. Sufficient knowledge is also necessary to understand how differences in social inequalities are reproduced and the ways societies are coping in times of water insecurity crises.

This review of the literature highlights the need for capacity building to achieve adaptation and mitigation processes that equip different stakeholders (including nation-states, businesses, and local people) in building sustainable and climate-resilient water systems. Smallholder farmers should be empowered to anticipate and respond robustly to climate change–induced water insecurity without losing their basic access to water for household and agricultural needs ( Adger, 2006 ; Cutter et al., 2003 ; Dixon & Stringer, 2015 ). This can be achieved through agroecology ( Woodgate, 2016 ) and a participatory approach where key issues of land rights, labor, gender, and food security are part of the programming ( Bahati et al., 2022 ) as agrarian change ensues in larger parts of semiarid Africa.

Finally, while climate change may be increasing the severity of natural hazards, the impact is exacerbated by social, ecological, political, and economic factors ( Yaro et al., 2015 ). The vulnerability of the three countries as shown in this article is simultaneously embedded in the broader socioeconomic challenges that are faced. Climatic changes will increasingly lead to more water stress and an increase in temperature. This means that the ability of people in the three countries to adapt and respond robustly to climate extremes such as droughts and floods is a function of idiosyncratic and wider forces, including the state of the national economy and the nature of economic activities. Thus, the vulnerability of the three countries should not just be viewed from the changes in the climatic variables (i.e., temperature and precipitation) but from the fact that they are largely rainfed agrarian economies, albeit with growing diversification in the case of Ghana and Kenya. In essence, the impact of climate-induced water insecurity is filtered through other nonclimatic factors, including demographic dynamics, the nature of livelihood pursuits, water policies, and other pertinent socioeconomic drivers. Building resilient local systems that use both Indigenous and modern methods of farming, water preservation, and conservation to combat climate-induced water insecurity should be given priority since water insecurity can easily accelerate social conflict in semiarid areas.

The Horn of Africa consists of Somalia, Djibouti, Ethiopia, Eritrea, and Kenya. Eastern Uganda is sometimes added.

Author notes

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Climate-smart agriculture: adoption, impacts, and implications for sustainable development

• Original Article
• Open access
• Published: 29 April 2024
• Volume 29 , article number  44 , ( 2024 )

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• Wanglin Ma   ORCID: orcid.org/0000-0001-7847-8459 1 &
• Dil Bahadur Rahut   ORCID: orcid.org/0000-0002-7505-5271 2  

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The 19 papers included in this special issue examined the factors influencing the adoption of climate-smart agriculture (CSA) practices among smallholder farmers and estimated the impacts of CSA adoption on farm production, income, and well-being. Key findings from this special issue include: (1) the variables, including age, gender, education, risk perception and preferences, access to credit, farm size, production conditions, off-farm income, and labour allocation, have a mixed (either positive or negative) influence on the adoption of CSA practices; (2) the variables, including labour endowment, land tenure security, access to extension services, agricultural training, membership in farmers’ organizations, support from non-governmental organizations, climate conditions, and access to information consistently have a positive impact on CSA adoption; (3) diverse forms of capital (physical, social, human, financial, natural, and institutional), social responsibility awareness, and digital advisory services can effectively promote CSA adoption; (4) the establishment of climate-smart villages and civil-society organizations enhances CSA adoption by improving their access to credit; (5) CSA adoption contributes to improved farm resilience to climate change and mitigation of greenhouse gas emissions; (6) CSA adoption leads to higher crop yields, increased farm income, and greater economic diversification; (7) integrating CSA technologies into traditional agricultural practices not only boosts economic viability but also contributes to environmental sustainability and health benefits; and (8) there is a critical need for international collaboration in transferring technology for CSA. Overall, the findings of this special issue highlight that through targeted interventions and collaborative efforts, CSA can play a pivotal role in achieving food security, poverty alleviation, and climate resilience in farming communities worldwide and contribute to the achievements of the United Nations Sustainable Development Goals.

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1 Introduction

Climate change reduces agricultural productivity and leads to greater instability in crop production, disrupting the global food supply and resulting in food and nutritional insecurity. In particular, climate change adversely affects food production through water shortages, pest outbreaks, and soil degradation, leading to significant crop yield losses and posing significant challenges to global food security (Kang et al. 2009 ; Läderach et al. 2017 ; Arora 2019 ; Zizinga et al. 2022 ; Mirón et al. 2023 ). United Nations reported that the human population will reach 9.7 billion by 2050. In response, food-calorie production will have to expand by 70% to meet the food demand of the growing population (United Nations 2021 ). Hence, it is imperative to advocate for robust mitigation strategies that counteract the negative impacts of climate change and enhance the flexibility and speed of response in smallholder farming systems.

A transformation of the agricultural sector towards climate-resilient practices can help tackle food security and climate change challenges successfully. Climate-smart agriculture (CSA) is an approach that guides farmers’ actions to transform agrifood systems towards building the agricultural sector’s resilience to climate change based on three pillars: increasing farm productivity and incomes, enhancing the resilience of livelihoods and ecosystems, and reducing and removing greenhouse gas emissions from the atmosphere (FAO 2013 ). Promoting the adoption of CSA practices is crucial to improve smallholder farmers’ capacity to adapt to climate change, mitigate its impact, and help achieve the United Nations Sustainable Development Goals.

Realizing the benefits of adopting CSA, governments in different countries and international organizations such as the Consultative Group on International Agricultural Research (CGIAR), the Food and Agriculture Organisation (FAO) of the United Nations, and non-governmental organizations (NGOs) have made great efforts to scale up and out the CSA. For example, climate-smart villages in India (Alam and Sikka 2019 ; Hariharan et al. 2020 ) and civil society organizations in Africa, Asia, and Latin America (Waters-Bayer et al. 2015 ; Brown 2016 ) have been developed to reduce information costs and barriers and bridge the gap in finance access to promote farmers’ adoption of sustainable agricultural practices, including CSA. Besides, agricultural training programs have been used to enhance farmers’ knowledge of CSA and their adoption of the technology in Ghana (Zakaria et al. 2020 ; Martey et al. 2021 ).

As a result, smallholder farmers worldwide have adopted various CSA practices and technologies (e.g., integrated crop systems, drop diversification, inter-cropping, improved pest, water, and nutrient management, improved grassland management, reduced tillage and use of diverse varieties and breeds, restoring degraded lands, and improved the efficiency of input use) to reach the objectives of CSA (Kpadonou et al. 2017 ; Zakaria et al. 2020 ; Khatri-Chhetri et al. 2020 ; Aryal et al. 2020a ; Waaswa et al. 2022 ; Vatsa et al. 2023 ). In the Indian context, technologies such as laser land levelling and the happy seeder have been promoted widely for their potential in climate change adaptation and mitigation, offering benefits in terms of farm profitability, emission reduction, and water and land productivity (Aryal et al. 2020b ; Keil et al. 2021 ). In some African countries such as Tanzania and Kenya, climate-smart feeding practices in the livestock sector have been suggested to tackle challenges in feed quality and availability exacerbated by climate change, aiming to improve livestock productivity and resilience (García de Jalón et al. 2017 ; Shikuku et al. 2017 ; Radeny et al. 2022 ).

Several studies have investigated the factors influencing farmers’ decisions to adopt CSA practices. They have focused on, for example, farmers’ characteristics (e.g., age, gender, and education), farm-level characteristics (e.g., farm size, land fertility, and land tenure security), socioeconomic factors (e.g., economic conditions), institutional factors (e.g., development programs, membership in farmers’ organizations, and access to agricultural training), climate conditions, and access to information (Aryal et al. 2018 ; Tran et al. 2020 ; Zakaria et al. 2020 ; Kangogo et al. 2021 ; Diro et al. 2022 ; Kifle et al. 2022 ; Belay et al. 2023 ; Zhou et al. 2023 ). For example, Aryal et al. ( 2018 ) found that household characteristics (e.g., general caste, education, and migration status), plot characteristics (e.g., tenure of plot, plot size, and soil fertility), distance to market, and major climate risks are major factors determining farmers’ adoption of multiple CSA practices in India. Tran et al. ( 2020 ) reported that age, gender, number of family workers, climate-related factors, farm characteristics, distance to markets, access to climate information, confidence in the know-how of extension workers, membership in social/agricultural groups, and attitude toward risk are the major factors affecting rice farmers’ decisions to adopt CSA technologies in Vietnam. Diro et al.’s ( 2022 ) analysis revealed that coffee growers’ decisions to adopt CSA practices are determined by their education, extension (access to extension services and participation on field days), and ownership of communication devices, specifically radio in Ethiopia. Zhou et al. 2023 ) found that cooperative membership significantly increases the adoption of climate-smart agricultural practices among banana-producing farmers in China. These studies provide significant insights regarding the factors influencing farmers’ decisions regarding CSA adoption.

A growing body of studies have also estimated the effects of CSA adoption. They have found that CSA practices enhance food security and dietary diversity by increasing crop yields and rural incomes (Amadu et al. 2020 ; Akter et al. 2022 ; Santalucia 2023 ; Tabe-Ojong et al. 2023 ; Vatsa et al. 2023 ; Omotoso and Omotayo 2024 ). For example, Akter et al. ( 2022 ) found that adoption of CSA practices was positively associated with rice, wheat, and maize yields and household income, contributing to household food security in Bangladesh. By estimating data from rice farmers in China, Vatsa et al. ( 2023 ) reported that intensifying the adoption of climate-smart agricultural practices improved rice yield by 94 kg/mu and contributed to food security. Santalucia ( 2023 ) and Omotoso and Omotayo ( 2024 ) found that adoption of CSA practices (improved maize varieties and maize-legume intercropping) increases household dietary diversity and food security among smallholders in Tanzania and Nigeria, respectively.

Agriculture is crucial in climate change, accounting for roughly 20% of worldwide greenhouse gas (GHG) emissions. Additionally, it is responsible for approximately 45% of the global emissions of methane, a potent gas that significantly contributes to heat absorption in the atmosphere. CSA adoption improves farm resilience to climate variability (e.g., Makate et al. 2019 ; Jamil et al. 2021 ) and mitigates greenhouse gas emissions (Israel et al. 2020 ; McNunn et al. 2020 ). For example, Makate et al. ( 2019 ) for southern Africa and Jamil et al. ( 2021 ) for Pakistan found that promoting CSA innovations is crucial for boosting farmers’ resilience to climate change. McNunn et al. ( 2020 ) reported that CSA adoption significantly reduces greenhouse gas emissions from agriculture by increasing soil organic carbon stocks and decreasing nitrous oxide emissions.

Although a growing number of studies have enriched our understanding of the determinants and impacts of ICT adoption, it should be emphasized that no one-size-fits-all approach exists for CSA technology adoption due to geographical and environmental variability. The definitions of CSA should also be advanced to better adapt to changing climate and regional production conditions. Clearly, despite the extensive research on CSA, several gaps remain. First, there is a lack of comprehensive studies that consolidate findings across different geographical regions to inform policymaking effectively. The calls for studies on literature review and meta-analysis to synthesize the findings of the existing studies to make our understanding generalized. Second, although the literature on determinants of CSA adoption is becoming rich, there is a lack of understanding of how CSA adoption is influenced by different forms of capital, social responsibility awareness of farmers’ cultivating family farms, and digital advisory services. Third, there is a lack of understanding of how climate-smart villages and civil society organizations address farmers’ financial constraints and encourage them to adopt modern sustainable agricultural practices, including CSA practices. Fourth, very few studies have explored how CSA adoption influences the benefit–cost ratio of farm production, factor demand, and input substitution. Fifth, no previous studies have reported the progress of research on CSA. Addressing these gaps is crucial for designing and implementing effective policies and programs that support the widespread adoption of CSA practices, thereby contributing to sustainable agricultural development and climate resilience.

We address the research gaps mentioned above and extend the findings in previous studies by organizing a Special Issue on “Climate-Smart Agriculture: Adoption, Impacts, and Implications for Sustainable Development” in the Mitigation and Adaptation Strategies for Global Change (MASGC) journal. We aim to collect high-quality theoretical and applied research papers discussing CSA and seek to comprehensively understand the associations between CSA and sustainable rural and agricultural development. To achieve this goal, we aim to find answers to these questions: What are the CSA practices and technologies (either single or multiple) that are currently adopted in smallholder farming systems? What are the key barriers, challenges, and drivers of promoting CSA practices? What are the impacts of adopting these practices? Answers to these questions will help devise appropriate solutions for promoting sustainable agricultural production and rural development. They will also provide insights for policymakers to design appropriate policy instruments to develop agricultural practices and technologies and promote them to sustainably enhance the farm sector’s resilience to climate change and increase productivity.

Finally, 19 papers were selected after a rigorous peer-review process and published in this special issue. We collected 10 papers investigating the determinants of CSA adoption. Among them, four papers investigated the determinants of CSA adoption among smallholders by reviewing and summarizing the findings in the literature and conducting a meta-analysis. Three papers explored the role of social-economic factors on ICT adoption, including capital, social responsibility awareness, and digital advisory services. Besides, three papers examined the associations between external development interventions, including climate-smart villages and civil-society initiatives, and CSA adoption. We collected eight papers exploring the impacts of CSA adoption. Among them, one paper conducted a comprehensive literature review to summarize the impacts of CSA adoption on crop yields, farm income, and environmental sustainability. Six papers estimated the impacts of CSA adoption on crop yields and farm income, and one paper focused on the impact of CSA adoption on factor demand and input substitution. The last paper included in this special issue delved into the advancements in technological innovation for agricultural adaptation within the context of climate-smart agriculture.

The structure of this paper is as follows: Section  2 summarizes the papers received in this special issue. Section  3 introduces the international conference that was purposely organized for the special issue. Section  4 summarizes the key findings of the 19 papers published in the special issue, followed by a summary of their policy implications, presented in Section  5 . The final section provides a brief conclusion.

2 Summary of received manuscripts

The special issue received 77 submissions, with the contributing authors hailing from 22 countries, as illustrated in Fig.  1 . This diversity highlights the global interest and wide-ranging contributions to the issue. Notably, over half of these submissions (53.2%) originated from corresponding authors in India and China, with 29 and 12 manuscripts, respectively. New Zealand authors contributed six manuscripts, while their Australian counterparts submitted four. Following closely, authors from the United Kingdom and Kenya each submitted three manuscripts. Authors from Thailand, Pakistan, Japan, and Germany submitted two manuscripts each. The remaining 12 manuscripts came from authors in Vietnam, Uzbekistan, the Philippines, Nigeria, the Netherlands, Malaysia, Italy, Indonesia, Ghana, Ethiopia, Brazil, and Bangladesh.

Distributions of 77 received manuscripts by corresponding authors' countries

Among the 77 received manuscripts, 30 were desk-rejected by the guest editors because they did not meet the aims and scope of the special issue, and the remaining 47, considered candidate papers for the special issue, were sent for external review. The decision on each manuscript was made based on review reports of 2–4 experts in this field. The guest editors also read and commented on each manuscript before they made decisions.

3 ADBI virtual international conference

3.1 selected presentations.

The guest editors from Lincoln University (New Zealand) and the Asian Development Bank Institute (ADBI) (Tokyo, Japan) organized a virtual international conference on the special issue theme “ Climate-Smart Agriculture: Adoption, Impacts, and Implications for Sustainable Development ”. The conference was organized on 10–11 October 2023 and was supported by the ADBI. Footnote 1 As previously noted, the guest editors curated a selection of 47 manuscripts from the pool of 77 submissions, identifying them as potential candidates for inclusion in the special issue, and sent them out for external review. Given the logistical constraints of orchestrating a two-day conference, the guest editors ultimately extended invitations to 20 corresponding authors. These authors were invited to present their work at the virtual international conference.

Figure  2 illustrates the native countries of the presenters, showing that the presenters were from 10 different countries. Most of the presenters were from India, accounting for 40% of the presenters. This is followed by China, where the four presenters were originally from. The conference presentations and discussions proved immensely beneficial, fostering knowledge exchange among presenters, discussants, and participants. It significantly allowed presenters to refine their manuscripts, leveraging the constructive feedback from discussants and fellow attendees.

Distributions of selected presentations by corresponding authors' countries

3.2 Keynote speeches

The guest editors invited two keynote speakers to present at the two-day conference. They were Prof. Edward B. Barbier from the Colorado State University in the United States Footnote 2 and Prof. Tatsuyoshi Saijo from Kyoto University of Advanced Science in Japan. Footnote 3

Prof. Edward Barbier gave a speech, “ A Policy Strategy for Climate-Smart Agriculture for Sustainable Rural Development ”, on 10th October 2023. He outlined a strategic approach for integrating CSA into sustainable rural development, particularly within emerging markets and developing economies. He emphasized the necessity of CSA and nature-based solutions (NbS) to tackle food security, climate change, and rural poverty simultaneously. Highlighting the substantial investment needs and the significant role of international and domestic financing, Prof. Barbier advocated reducing harmful subsidies in agriculture, forestry, fishing, and fossil fuel consumption to redirect funds toward CSA and NbS investments. He also proposed the implementation of a tropical carbon tax as an innovative financing mechanism. By focusing on recycling environmentally harmful subsidies and leveraging additional funding through public and private investments, Prof. Barbier’s strategy aims to foster a “win–win” scenario for climate action and sustainable development, underscoring the urgency of adopting comprehensive policies to mobilize the necessary resources for these critical investments.

Prof. Tatsuyoshi Saijo, gave his speech, “ Future Design ”, on 11th October 2023. He explored the significant impact of the Haber–Bosch process on human civilization and the environment. Prof. Saijo identifies this process, which synthetically fixed nitrogen from the atmosphere to create ammonia for fertilizers and other products, as the greatest invention from the twentieth century to the present, fundamentally transforming the world’s food production and enabling the global population and industrial activities to expand dramatically. He also discussed the environmental costs of this technological advancement, including increased greenhouse gas emissions, pollution, and contribution to climate change. Prof. Saijo then introduced the concept of “Future Design” as a method to envision and implement sustainable social systems that consider the well-being of future generations. He presented various experiments and case studies from Japan and beyond, showing how incorporating perspectives of imaginary future generations into decision-making processes can lead to more sustainable choices. By doing so, Prof. Saijo suggested that humanity can address the “Intergenerational Sustainability Dilemma” and potentially avoid the ecological overshoot and collapse faced by past civilizations like Easter Island. He called for a redesign of social systems to activate “futurability”, where individuals derive happiness from decisions that benefit future generations, ultimately aiming to ensure the long-term survival of humankind amidst environmental challenges.

4 Summary of published articles

As a result of a rigorous double-anonymized reviewing process, the special issue accepted 19 articles for publication. These studies have investigated the determinants and impacts of CSA adoption. Table 1 in the Appendix summarises the CSA technologies and practices considered in each paper. Below, we summarize the key findings of the contributions based on their research themes.

4.1 Determinants of CSA adoption among smallholders

4.1.1 influencing factors of csa adoption from literature review.

Investigating the factors influencing farmers’ adoption of CSA practices through a literature review helps offer a comprehensive understanding of the multifaceted determinants of CSA adoption. Investigating the factors influencing farmers’ adoption of CSA practices through a literature review helps provide a comprehensive understanding of the determinants of CSA adoption. Such analyses help identify consistent trends and divergences in how different variables influence farmers’ CSA adoption decisions. In this special issue, we collected four papers that reviewed the literature and synthesized the factors influencing farmers’ decisions to adopt CSA.

Li, Ma and Zhu’s paper, “ A systematic literature review of factors influencing the adoption of climate-smart agricultural practices ”, conducted a systematic review of the literature on the adoption of CSA, summarizing the definitions of CSA practices and the factors that influence farmers’ decisions to adopt these practices. The authors reviewed 190 studies published between 2013 and 2023. They broadly defined CSA practices as “agricultural production-related and unrelated practices that can help adapt to climate change and increase agricultural outputs”. Narrowly, they defined CSA practices as “agricultural production-related practices that can effectively adapt agriculture to climate change and reinforce agricultural production capacity”. The review identified that many factors, including age, gender, education, risk perception, preferences, access to credit, farm size, production conditions, off-farm income, and labour allocation, have a mixed (positive or negative) influence on the adoption of CSA practices. Variables such as labour endowment, land tenure security, access to extension services, agricultural training, membership in farmers’ organizations, support from non-governmental organizations (NGOs), climate conditions, and access to information were consistently found to positively influence CSA practice adoption.

Thottadi and Singh’s paper, “ Climate-smart agriculture (CSA) adaptation, adaptation determinants and extension services synergies: A systematic review ””, reviewed 45 articles published between 2011 and 2022 to explore different CAS practices adopted by farmers and the factors determining their adoption. They found that CSA practices adopted by farmers can be categorized into five groups. These included resilient technologies (e.g., early maturing varieties, drought-resistant varieties, and winter ploughing), management strategies (e.g., nutrient management, water management, and pest management), conservation technologies (e.g., vermicomposting and residue management, drip and sprinkler irrigation, and soil conservation), diversification of income security (e.g., mixed farming, livestock, and crop diversification), and risk mitigation strategies (e.g., contingent planning, adjusting plant dates, and crop insurance). They also found that farmers’ decisions to adopt CSA practices are mainly determined by individual characteristics (age, gender, and education), socioeconomic factors (income and wealth), institutional factors (social group, access to credit, crop insurance, distance, land tenure, and rights), behavioural factors (climate perception, farmers’ perception on CSA, Bookkeeping), and factor endowments (family labour, machinery, and land size). The authors emphasized that extension services improved CSA adaptation by reducing information asymmetry.

Naveen, Datta, Behera and Rahut’s paper, “ Climate-Smart Agriculture in South Asia: Exploring Practices, Determinants, and Contribution to Sustainable Development Goals ”, offered a comprehensive systematic review of 78 research papers on CSA practice adoption in South Asia. Their objective was to assess the current implementation of CSA practices and to identify the factors that influence farmers’ decisions to adopt these practices. They identified various CSA practices widely adopted in South Asia, including climate-resilient seeds, zero tillage, water conservation, rescheduling of planting, crop diversification, soil conservation and water harvesting, and agroforestry. They also identified several key factors that collectively drive farmers’ adoption of CSA practices. These included socioeconomic factors (age, education, livestock ownership, size of land holdings, and market access), institutional factors (access to information and communication technology, availability of credit, input subsidies, agricultural training and demonstrations, direct cash transfers, and crop insurance), and climatic factors (notably rising temperatures, floods, droughts, reduced rainfall, and delayed rainfall).

Wang, Wang and Fu’s paper, “ Can social networks facilitate smallholders’ decisions to adopt Climate-smart Agriculture technologies? A three-level meta-analysis ”, explored the influence of social networks on the adoption of CSA technologies by smallholder farmers through a detailed three-level meta-analysis. This analysis encompassed 26 empirical studies, incorporating 150 effect sizes. The authors reported a modest overall effect size of 0.065 between social networks and the decision-making process for CSA technology adoption, with an 85.21% variance observed among the sample effect sizes. They found that over half (55.17%) of this variance was attributed to the differences in outcomes within each study, highlighting the impact of diverse social network types explored across the studies as significant contributors. They did not identify publication bias in this field. Among the three types of social networks (official-advising network, peer-advising network, and kinship and friendship network), kinship and friendship networks are the most effective in facilitating smallholders’ decisions to adopt climate-smart agriculture technologies.

4.1.2 Socioeconomic factors influencing CSA adoption

We collected three papers highlighting the diverse forms of capital, social responsibility awareness, and effectiveness of digital advisory services in promoting CSA in India, China and Ghana. These studies showcase how digital tools can significantly increase the adoption of CSA technologies, how social responsibility can motivate CSA practices and the importance of various forms of capital in CSA strategy adoption.

Sandilya and Goswami’s paper, “ Effect of different forms of capital on the adoption of multiple climate-smart agriculture strategies by smallholder farmers in Assam, India ”, delved into the determinants behind the adoption of CSA strategies by smallholder farmers in Nagaon district, India, a region notably prone to climate adversities. The authors focused on six types of capital: physical, social, human, financial, natural, and institutional. They considered four CSA practices: alternate land use systems, integrated nutrient management, site-specific nutrient management, and crop diversification. Their analyses encompassed a dual approach, combining a quantitative analysis via a multivariate probit model with qualitative insights from focus group discussions. They found that agricultural cooperatives and mobile applications, both forms of social capital, play a significant role in facilitating the adoption of CSA. In contrast, the authors also identified certain barriers to CSA adoption, such as the remoteness of farm plots from all-weather roads (a component of physical capital) and a lack of comprehensive climate change advisories (a component of institutional capital). Furthermore, the authors highlighted the beneficial impact of irrigation availability (a component of physical capital) on embracing alternate land use and crop diversification strategies. Additionally, the application of indigenous technical knowledge (a component of human capital) and the provision of government-supplied seeds (a component of institutional capital) were found to influence the adoption of CSA practices distinctly.

Ye, Zhang, Song and Li’s paper, “ Social Responsibility Awareness and Adoption of Climate-smart Agricultural Practices: Evidence from Food-based Family Farms in China ”, examined whether social responsibility awareness (SRA) can be a driver for the adoption of CSA on family farms in China. Using multiple linear regression and hierarchical regression analyses, the authors analyzed data from 637 family farms in five provinces (Zhejiang, Shandong, Henan, Heilongjiang, and Hebei) in China. They found that SRA positively impacted the adoption of CSA practice. Pro-social motivation and impression management motivation partially and completely mediated the relationship between SRA and the adoption of CSA practices.

Asante, Ma, Prah and Temoso’s paper, “ Promoting the adoption of climate-smart agricultural technologies among maize farmers in Ghana: Using digital advisory services ”, investigated the impacts of digital advisory services (DAS) use on CSA technology adoption and estimated data collected from 3,197 maize farmers in China. The authors used a recursive bivariate probit model to address the self-selection bias issues when farmers use DAS. They found that DAS notably increases the propensity to adopt drought-tolerant seeds, zero tillage, and row planting by 4.6%, 4.2%, and 12.4%, respectively. The average treatment effect on the treated indicated that maize farmers who use DAS are significantly more likely to adopt row planting, zero tillage, and drought-tolerant seeds—by 38.8%, 24.9%, and 47.2%, respectively. Gender differences in DAS impact were observed; male farmers showed a higher likelihood of adopting zero tillage and drought-tolerant seeds by 2.5% and 3.6%, respectively, whereas female farmers exhibited a greater influence on the adoption of row planting, with a 2.4% probability compared to 1.5% for males. Additionally, factors such as age, education, household size, membership in farmer-based organizations, farm size, perceived drought stress, perceived pest and disease incidence, and geographic location were significant determinants in the adoption of CSA technologies.

4.1.3 Climate-smart villages and CSA adoption

Climate-Smart Villages (CSVs) play a pivotal role in promoting CSA by significantly improving farmers’ access to savings and credit, and the adoption of improved agricultural practices among smallholder farmers. CSV interventions demonstrate the power of community-based financial initiatives in enabling investments in CSA technologies. In this special issue, we collected two insightful papers investigating the relationship between CSVs and the adoption of CSA practices, focusing on India and Kenya.

Villalba, Joshi, Daum and Venus’s paper, “ Financing Climate-Smart Agriculture: A Case Study from the Indo-Gangetic Plains ”, investigated the adoption and financing of CSA technologies in India, focusing on two capital-intensive technologies: laser land levelers and happy seeders. Conducted in Karnal, Haryana, within the framework of Climate-Smart-Villages, the authors combined data from a household survey of 120 farmers, interviews, and focus group discussions with stakeholders like banks and cooperatives. The authors found that adoption rates are high, with 77% for laser land levelers and 52% for happy seeders, but ownership is low, indicating a preference for renting from Custom-Hiring Centers. Farmers tended to avoid formal banking channels for financing, opting instead for informal sources like family, savings, and money lenders, due to the immediate access to credit and avoidance of bureaucratic hurdles. The authors suggested that institutional innovations and governmental support could streamline credit access for renting CSA technologies, emphasizing the importance of knowledge transfer, capacity building, and the development of digital tools to inform farmers about financing options. This research highlights the critical role of financing mechanisms in promoting CSA technology adoption among smallholder farmers in climate-vulnerable regions.

Asseldonk, Oostendorp, Recha, Gathiaka, Mulwa, Radeny Wattel and Wesenbeeck’s paper, “ Distributional impact of climate‑smart villages on access to savings and credit and adoption of improved climate‑smart agricultural practices in the Nyando Basin, Kenya ”, investigated the impact of CSV interventions in Kenya on smallholder farmers’ access to savings, credit, and adoption of improved livestock breeds as part of CSA practices. The authors employed a linear probability model to estimate a balanced panel of 118 farm households interviewed across 2017, 2019, and 2020. They found that CSV interventions significantly increased the adoption of improved livestock breeds and membership in savings and credit groups, which further facilitated the adoption of these improved breeds. The findings highlighted that community-based savings and loan initiatives effectively enable farmers to invest in CSA practices. Although there was a sustained positive trend in savings and loans group membership, the adoption of improved livestock did not show a similar sustained increase. Moreover, the introduction of improved breeds initially benefited larger livestock owners more. However, credit availability was found to reduce this inequity in ownership among participants, making the distribution of improved livestock more equitable within CSVs compared to non-CSV areas, thus highlighting the potential of CSV interventions to reduce disparities in access to improved CSA practices.

4.1.4 Civil-society initiatives and CSA adoption

Civil society initiatives are critical in promoting CSA by embedding its principles across diverse agricultural development projects. These initiatives enhance mitigation, adaptation, and food security efforts for smallholder farmers, demonstrating the importance of varied implementation strategies to address the challenges of CSA. We collected one paper investigating how civil society-based development projects in Asia and Africa incorporated CSA principles to benefit smallholder farmers and local communities.

Davila, Jacobs, Nadeem, Kelly and Kurimoto’s paper, “ Finding climate smart agriculture in civil-society initiatives ”, scrutinized the role of international civil society and non-government organizations (NGOs) in embedding CSA principles within agricultural development projects aimed at enhancing mitigation, adaptation, and food security. Through a thematic analysis of documentation from six projects selected on the basis that they represented a range of geographical regions (East Africa, South, and Southeast Asia) and initiated since 2009, the authors assessed how development programs incorporate CSA principles to support smallholder farmers under CSA’s major pillars. They found heterogeneous application of CSA principles across the projects, underscoring a diversity in implementation strategies despite vague definitions and focuses of CSA. The projects variedly contributed to greening and forests, knowledge exchange, market development, policy and institutional engagement, nutrition, carbon and climate action, and gender considerations.

4.2 Impacts of CSA adoption

4.2.1 impacts of csa adoption from literature review.

A comprehensive literature review on the impacts of CSA adoption plays an indispensable role in bridging the gap between theoretical knowledge and practical implementation in the agricultural sector. In this special issue, we collected one paper that comprehensively reviewed the literature on the impacts of CSA adoption from the perspective of the triple win of CSA.

Zheng, Ma and He’s paper, “ Climate-smart agricultural practices for enhanced farm productivity, income, resilience, and Greenhouse gas mitigation: A comprehensive review ”, reviewed 107 articles published between 2013–2023 to distill a broad understanding of the impacts of CSA practices. The review categorized the literature into three critical areas of CSA benefits: (a) the sustainable increase of agricultural productivity and incomes; (b) the adaptation and enhancement of resilience among individuals and agrifood systems to climate change; and (c) the reduction or avoidance of greenhouse gas (GHG) emissions where feasible. The authors found that CSA practices significantly improved farm productivity and incomes and boosted technical and resource use efficiency. Moreover, CSA practices strengthened individual resilience through improved food consumption, dietary diversity, and food security while enhancing agrifood systems’ resilience by mitigating production risks and reducing vulnerability. Additionally, CSA adoption was crucial in lowering Greenhouse gas emissions and fostering carbon sequestration in soils and biomass, contributing to improved soil quality.

4.2.2 Impacts on crop yields and farm income

Understanding the impact of CSA adoption on crop yields and income is crucial for improving agricultural resilience and sustainability. In this special issue, we collected three papers highlighting the transformative potential of CSA practices in boosting crop yields, commercialization, and farm income. One paper focuses on India and the other concentrates on Ghana and Kenya.

Tanti, Jena, Timilsina and Rahut’s paper, “ Enhancing crop yields and farm income through climate-smart agricultural practices in Eastern India ”, examined the impact of CSA practices (crop rotation and integrated soil management practices) on crop yields and incomes. The authors used propensity score matching and the two-stage least square model to control self-selection bias and endogeneity and analyzed data collected from 494 farm households in India. They found that adopting CSA practices increases agricultural income and paddy yield. The crucial factor determining the adoption of CSA practices was the income-enhancing potential to transform subsistence farming into a profoundly ingrained farming culture.

Asante, Ma, Prah and Temoso’s paper, “ Farmers’ adoption of multiple climate-smart agricultural technologies in Ghana: Determinants and impacts on maize yields and net farm income ”, investigated the factors influencing maize growers’ decisions to adopt CSA technologies and estimated the impact of adopting CSA technologies on maize yields and net farm income. They considered three CSA technology types: drought-resistant seeds, row planting, and zero tillage. The authors used the multinomial endogenous switching regression model to estimate the treatment effect of CSA technology adoption and analyze data collected from 3,197 smallholder farmers in Ghana. They found that farmer-based organization membership, education, resource constraints such as lack of land, access to markets, and production shocks such as perceived pest and disease stress and drought are the main factors that drive farmers’ decisions to adopt CSA technologies. They also found that integrating any CSA technology or adopting all three CSA technologies greatly enhances maize yields and net farm income. Adopting all three CSA technologies had the largest impact on maize yields, while adopting row planting and zero tillage had the greatest impact on net farm income.

Mburu, Mburu, Nyikal, Mugera and Ndambi’s paper, “ Assessment of Socioeconomic Determinants and Impacts of Climate-Smart Feeding Practices in the Kenyan Dairy Sector ”, assessed the determinants and impacts of adopting climate-smart feeding practices (fodder and feed concentrates) on yield, milk commercialization, and household income. The authors used multinomial endogenous switching regression to account for self-selection bias arising from observable and unobservable factors and estimated data collected from 665 dairy farmers in Kenya. They found that human and social capital, resource endowment, dairy feeding systems, the source of information about feeding practices, and perceived characteristics were the main factors influencing farmers’ adoption of climate-smart feeding practices. They also found that combining climate-smart feed concentrates and fodder significantly increased milk productivity, output, and dairy income. Climate-smart feed concentrates yielded more benefits regarding dairy milk commercialization and household income than climate-smart fodder.

4.2.3 Impacts on crop yields

Estimating the impacts of CSA adoption on crop yields is crucial for enhancing food security, improving farmers’ resilience to climate change, and guiding policy and investment towards sustainable agricultural development. In this special issue, we collected one paper that provided insights into this field.

Singh, Bisaria, Sinha, Patasaraiya and Sreerag’s paper, “ Developing A Composite Weighted Indicator-based Index for Monitoring and Evaluating Climate-Smart Agriculture in India ”, developed a composite index based on a weighted index to calculate the Climate Smart Score (CSS) at the farm level in India and tested the relationship between computed CSS and farm-level productivity. Through an intensive literature review, the authors selected 34 indicators, which were then grouped into five dimensions for calculating CSS. These dimensions encompassed governance (e.g., land ownership, subsidized fertilizer, and subsidized seeds), farm management practices (mulching, zero tillage farming, and inter-cropping and crop diversification), environment management practices (e.g., not converting forested land into agricultural land and Agroforestry/plantation), energy management (e.g., solar water pump and Biogas digester), and awareness and training (e.g., knowledge of climate-related risk and timely access to weather and agro-advisory). They tested the relationship between CSS and farm productivity using data collected from 315 farmers. They found that improved seeds, direct seeding of rice, crop diversification, zero tillage, agroforestry, crop residue management, integrated nutrient management, and training on these practices were the most popular CSA practices the sampled farmers adopted. In addition, there was a positive association between CSS and paddy, wheat, and maize yields. This finding underscores the beneficial impact of CSA practices on enhancing farm productivity.

4.2.4 Impacts on incomes and benefit–cost ratio

Understanding the income effects of CSA adoption is crucial for assessing its impact on household livelihoods, farm profitability, and income diversity. Quantifying income enhancements would contribute to informed decision-making and investment strategies to improve farming communities’ economic well-being. In this special issue, we collected two papers looking into the effects of CSA adoption on income.

Sang, Chen, Hu and Rahut’s paper, “ Economic benefits of climate-smart agricultural practices: Empirical investigations and policy implications ”, investigated the impact of CSA adoption intensity on household income, net farm income, and income diversity. They used the two-stage residual inclusion model to mitigate the endogeneity of CSA adoption intensity and analyzed the 2020 China Rural Revitalization Survey data. They also used the instrumental-variable-based quantile regression model to investigate the heterogeneous impacts of CSA adoption intensity. The authors found that the education level of the household head and geographical location determine farmers’ adoption intensity of CSAs.CSA practices. The higher levels of CSA adoption were positively and significantly associated with higher household income, net farm income, and income diversity. They also found that while the impact of CSA adoption intensity on household income escalates across selected quantiles, its effect on net farm income diminishes over these quantiles. Additionally, the study reveals that CSA adoption intensity notably enhances income diversity at the 20th quantile only.

Kandulu, Zuo, Wheeler, Dusingizimana and Chagund’s paper, “ Influence of climate-smart technologies on the success of livestock donation programs for smallholder farmers in Rwanda ”, investigated the economic, environmental, and health benefits of integrating CSA technologies —specifically barns and biogas plants—into livestock donation programs in Rwanda. Employing a stochastic benefit–cost analysis from the perspective of the beneficiaries, the authors assessed the net advantages for households that receive heifers under an enhanced program compared to those under the existing scheme. They found that incorporating CSA technologies not only boosts the economic viability of these programs but also significantly increases the resilience and sustainability of smallholder farming systems. More precisely, households equipped with cows and CSA technologies can attain net benefits up to 3.5 times greater than those provided by the current program, with the benefit–cost ratios reaching up to 5. Furthermore, biogas technology reduces deforestation, mitigating greenhouse gas emissions, and lowering the risk of respiratory illnesses, underscoring the multifaceted advantages of integrating such innovations into livestock donation initiatives.

4.2.5 Impacts on factor demand and input substitution

Estimating the impacts of CSA adoption on factor demand and input substitution is key to optimizing resource use, reducing environmental footprints, and ensuring agricultural sustainability by enabling informed decisions on efficient input use and technology adoption. In this field, we collected one paper that enriched our understanding in this field. Understanding the impacts of CSA adoption on factor demand, input substitution, and financing options is crucial for promoting sustainable farming in diverse contexts. In this special issue, we collected one paper comprehensively discussing how CSA adoption impacted factor demand and input substitution.

Kehinde, Shittu, Awe and Ajayi’s paper, “ Effects of Using Climate-Smart Agricultural Practices on Factor Demand and Input Substitution among Smallholder Rice Farmers in Nigeria ”, examined the impacts of agricultural practices with CSA potential (AP-CSAPs) on the demand of labour and other production factors (seed, pesticides, fertilizers, and mechanization) and input substitution. The AP-CSAPs considered in this research included zero/minimum tillage, rotational cropping, green manuring, organic manuring, residue retention, and agroforestry. The authors employed the seemingly unrelated regression method to estimate data collected from 1,500 smallholder rice farmers in Nigeria. The authors found that labour and fertilizer were not easily substitutable in the Nigerian context; increases in the unit price of labour (wage rate) and fertilizer lead to a greater budget allocation towards these inputs. Conversely, a rise in the cost of mechanization services per hectare significantly reduced labour costs while increasing expenditure on pesticides and mechanization services. They also found that most AP-CSAPs were labour-intensive, except for agroforestry, which is labor-neutral. Organic manure and residue retention notably conserved pesticides, whereas zero/minimum tillage practices increased the use of pesticides and fertilizers. Furthermore, the demand for most production factors, except pesticides, was found to be price inelastic, indicating that price changes do not significantly alter the quantity demanded.

4.3 Progress of research on CSA

Understanding the progress of research on CSA is essential for identifying and leveraging technological innovations—like greenhouse advancements, organic fertilizer products, and biotechnological crop improvements—that support sustainable agricultural adaptation. This knowledge enables the integration of nature-based strategies, informs policy, and underscores the importance of international cooperation in overcoming patent and CSA adoption challenges to ensure global food security amidst climate change. We collected one paper in this field.

Tey, Brindal, Darham and Zainalabidin’s paper, “ Adaptation technologies for climate-smart agriculture: A patent network analysis ”, delved into the advancements in technological innovation for agricultural adaptation within the context of CSA by analyzing global patent databases. The authors found that greenhouse technologies have seen a surge in research and development (R&D) efforts, whereas composting technologies have evolved into innovations in organic fertilizer products. Additionally, biotechnology has been a significant focus, aiming to develop crop traits better suited to changing climate conditions. A notable emergence is seen in resource restoration innovations addressing climate challenges. These technologies offer a range of policy options for climate-smart agriculture, from broad strategies to specific operational techniques, and pave the way for integration with nature-based adaptation strategies. However, the widespread adoption and potential impact of these technologies may be hindered by issues related to patent ownership and the path dependency this creates. Despite commercial interests driving the diffusion of innovation, international cooperation is clearly needed to enhance technology transfer.

5 Summary of key policy implications

The collection of 19 papers in this special issue sheds light on the critical aspects of promoting farmers’ adoption of CSA practices, which eventually help enhance agricultural productivity and resilience, reduce greenhouse gas emissions, improve food security and soil health, offer economic benefits to farmers, and contribute to sustainable development and climate change adaptation. We summarize and discuss the policy implications derived from this special issue from the following four aspects:

5.1 Improving CSA adoption through extension services

Extension services help reduce information asymmetry associated with CSA adoption and increase farmers’ awareness of CSA practices’ benefits, costs, and risks while addressing their specific challenges. Therefore, the government should improve farmers’ access to extension services. These services need to be inclusive and customized to meet the gender-specific needs and the diverse requirements of various farming stakeholders. Additionally, fostering partnerships between small and medium enterprises and agricultural extension agents is crucial for enhancing the local availability of CSA technologies. Government-sponsored extension services should prioritize equipping farmers with essential CSA skills, ensuring they are well-prepared to implement these practices. This structured approach will streamline the adoption process and significantly improve the effectiveness of CSA initiatives.

5.2 Facilitating CSA adoption through farmers’ organizations

Farmers’ organizations, such as village cooperatives, farmer groups, and self-help groups, play a pivotal role in facilitating farmers’ CSA adoption and empowering rural women’s adoption through effective information dissemination and the use of agricultural apps. Therefore, the government should facilitate the establishment and development of farmers’ organizations and encourage farmers to join those organizations as members. In particular, the proven positive impacts of farmer-based organizations (FBOs) highlight the importance of fostering collaborations between governments and FBOs. Supporting farmer cooperatives with government financial and technical aid is essential for catalyzing community-driven climate adaptation efforts. Furthermore, the successful use of DAS in promoting CSA adoption underscores the need for government collaboration with farmer groups to expand DAS utilization. This includes overcoming usage barriers and emphasizing DAS’s reliability as a source of climate-smart information. By establishing and expanding digital hubs and demonstration centres in rural areas, farmers can access and experience DAS technologies firsthand, leading to broader adoption and integration into their CSA practices.

5.3 Enhancing CSA adoption through agricultural training and education

Agricultural training and education are essential in enhancing farmers’ adoption of CSA. To effectively extend the reach of CSA practices, the government should prioritize expanding rural ICT infrastructure investments and establish CSA training centres equipped with ICT tools that target key demographics such as women and older people, aiming to bridge the digital adoption gaps. Further efforts should prioritize awareness and training programs to ensure farmers can access weather and agro-advisory services. These programs should promote the use of ICT-based tools through collaborations with technology providers and include regular CSA training and the establishment of demonstration fields that showcase the tangible benefits of CSA practices.

Education plays a vital role in adopting CAPs, suggesting targeted interventions such as comprehensive technical training to assist farmers with limited educational backgrounds in understanding the value of CAPs, ultimately improving their adoption rates. Establishing robust monitoring mechanisms is crucial to maintaining farmer engagement and success in CSA practices. These mechanisms will facilitate the ongoing adoption and evaluation of CSA practices and help educate farmers on the long-term benefits. Centralizing and disseminating information about financial products and subsidies through various channels, including digital platforms tailored to local languages and contexts, is essential. This approach helps educate farmers on financing options and requirements, supporting the adoption of CSA technologies among smallholder farmers. Lastly, integrating traditional and local knowledge with scientific research and development can effectively tailor CSA initiatives. This integration requires the involvement of a range of stakeholders, including NGOs, to navigate the complexities of CSA and ensure that interventions are effective but also equitable and sustainable. The enhanced capacity of institutions and their extension teams will further support these CSA initiatives.

5.4 Promoting CSA adoption through establishing social networks and innovating strategies

The finding that social networks play a crucial role in promoting the adoption of CSA suggests that implementing reward systems to incentivize current CSA adopters to advocate for climate-smart practices within their social circles could be an effective strategy to promote CSA among farmers. The evidence of a significant link between family farms’ awareness of social responsibility and their adoption of CSA highlights that governments should undertake initiatives, such as employing lectures and pamphlets, to enhance family farm operating farmers’ understanding of social responsibility. The government should consider introducing incentives that foster positive behavioural changes among family farms to cultivate a more profound commitment to social responsibility. The government can also consider integrating social responsibility criteria into the family farm awards and recognition evaluation process. These measures would encourage family farms to align their operations with broader social and environmental goals, promoting CSA practices.

Combining traditional incentives, such as higher wages and access to improved agricultural inputs, with innovative strategies like community-driven development for equipment sharing and integrating moral suasion with Payment for Ecosystem Services would foster farmers’ commitment to CSA practices. The finding that technological evolution plays a vital role in shaping adaptation strategies for CSA highlights the necessity for policy instruments that not only leverage modern technologies but also integrate them with traditional, nature-based adaptation strategies, enhancing their capacity to address specific CSA challenges. Policymakers should consider the region’s unique socioeconomic, environmental, and geographical characteristics when promoting CSA, moving away from a one-size-fits-all approach to ensure the adaptability and relevance of CSA practices across different agricultural landscapes. They should foster an environment that encourages the reporting of all research outcomes to develop evidence-based policies that are informed by a balanced view of CSA’s potential benefits and limitations.

Finally, governance is critical in creating an enabling environment for CSA adoption. Policies should support CSA practices and integrate environmental sustainability to enhance productivity and ecosystem health. Development programs must offer financial incentives, establish well-supported voluntary schemes, provide robust training programs, and ensure the wide dissemination of informational tools. These measures are designed to help farmers integrate CAPs into their operations, improving economic and operational sustainability.

6 Concluding remarks

This special issue has provided a wealth of insights into the adoption and impact of CSA practices across various contexts, underscoring the complexity and multifaceted nature of CSA implementation. The 19 papers in this special issue collectively emphasize the importance of understanding local conditions, farmer characteristics, and broader socioeconomic and institutional factors that influence CSA adoption. They highlight the crucial role of extension services, digital advisory services, social responsibility awareness, and diverse forms of capital in facilitating the adoption of CSA practices. Moreover, the findings stress the positive impact of CSA on farm productivity, income diversification, and resilience to climate change while also pointing out the potential for CSA practices to address broader sustainability goals.

Significantly, the discussions underline the need for policy frameworks that are supportive and adaptive, tailored to specific regional and local contexts to promote CSA adoption effectively. Leveraging social networks, enhancing access to financial products and mechanisms, and integrating technological innovations with traditional agricultural practices are vital strategies for scaling CSA adoption. Furthermore, the discussions advocate for a balanced approach that combines economic incentives with moral persuasion and community engagement to foster sustainable agricultural practices.

These comprehensive insights call for concerted efforts from policymakers, researchers, extension agents, and the agricultural community to foster an enabling environment for CSA. Such an environment would support knowledge exchange, financial accessibility, and the adoption of CSA practices that contribute to the resilience and sustainability of agricultural systems in the face of climate change. As CSA continues to evolve, future research should focus on addressing the gaps identified, exploring innovative financing and technology dissemination models, and assessing the long-term impacts of CSA practices on agricultural sustainability and food security. This special issue lays the groundwork for further exploration and implementation of CSA practices, aiming to achieve resilient, productive, and sustainable agricultural systems worldwide and contribute to the achievements of the United Nations Sustainable Development Goals.

Data availability

No new data were created or analyzed during this study. Data sharing is not applicable to this article.

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Acknowledgements

We want to thank all the authors who have submitted papers for the special issue and the reviewers who reviewed manuscripts on time. We acknowledge the Asian Development Bank Institute (ADBI) for supporting the virtual international conference on “ Climate-smart Agriculture: Adoption, Impacts, and Implications for Sustainable Development ” held on 10-11 October 2023. Special thanks to the invited keynote speakers, Prof. Edward Barbier and Prof. Tatsuyoshi Saijo. Finally, we would like to express our thanks, gratitude, and appreciation to the session chairs (Prof. Anita Wreford, Prof. Jianjun Tang, Prof. Alan Renwick, and Assoc. Prof. Sukanya Das), ADBI supporting team (Panharoth Chhay, Mami Nomoto, Mami Yoshida, and Raja Rajendra Timilsina), and discussants who made substantial contributions to the conference.

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Ma, W., Rahut, D.B. Climate-smart agriculture: adoption, impacts, and implications for sustainable development. Mitig Adapt Strateg Glob Change 29 , 44 (2024). https://doi.org/10.1007/s11027-024-10139-z

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