Analysis of the Progress Status of SDG 2 Indicators - Zero Hunger in WAEMU Member Countries in 2021
Kamkeu ulrich 20230263; Oubian abdel Kader 20210067; Ouedraogo sandra 20210300; Guinko zeinabou 20240810;
2025-04-09
ABSTRACT
Hunger remains a major issue in the WAEMU member countries, despite efforts made to achieve food self-sufficiency. In 2021, these countries continue to face challenges in ensuring food security for their populations. Although global trends in eliminating hunger, food insecurity, and malnutrition showed signs of improvement over the past two decades, this trend slowed significantly, particularly in Sub-Saharan Africa since 2015. The analysis of the implementation of SDG 2 indicators in 2021 highlights the progress of these goals, while emphasizing that factors such as insecurity, climate crises, and epidemics are significant barriers to achieving the “Zero Hunger” target. In 2021, countries like Ivory Coast and Senegal display relatively advanced food security levels, thanks to strong agricultural systems and geographical and security advantages. Conversely, countries like Burkina Faso, Mali, and Niger face particularly critical situations, marked by armed conflicts and arid climates that severely hinder their development. Lastly, despite favorable geographical and climatic conditions for agriculture, countries such as Benin, Guinea-Bissau, and Togo face alarming hunger situations, mainly due to unequal distribution of resources. Thus, in 2021, sustained efforts are still required to improve food security in these countries by strengthening agricultural capacities, facilitating access to resources, and developing effective public policies to eradicate hunger.
INTRODUCTION
Over the past few decades, the global number of hungry people, as measured by the prevalence of undernourishment, experienced a steady decline until 2015, when this trend reversed. By 2015, nearly 690 million people, or 8.9% of the world’s population, were affected by hunger, a rise of 10 million in one year and nearly 60 million in five years (UN, 2023). Despite the global efforts to combat hunger and achieve food security, this growing trend of food insecurity presents a significant challenge in meeting the United Nations’ Sustainable Development Goal (SDG) 2—Zero Hunger—by 2030. To eliminate hunger by 2030, urgent and coordinated actions are required to address persistent inequalities, transform global food systems, invest in sustainable agriculture, and mitigate the impacts of conflict and pandemics on food security and nutrition. In light of these challenges, the SDGs, established to address hunger and food insecurity globally, are crucial. SDG 2 specifically targets the elimination of hunger, ensuring food security, improving nutrition, and promoting sustainable agriculture, all while aiming to guarantee regular access to quality food for all and increase agricultural productivity, especially for small-scale farmers. However, as Jean-François Riffaud, Director General of Action Against Hunger, aptly stated, “Hunger feeds on inequality and vulnerability.” This emphasizes the need to address the root causes of hunger and malnutrition, particularly to ensure equitable access to adequate food for all. Tackling hunger and malnutrition is not only essential for improving health and well-being but also for fostering sustainable development in WAEMU member countries and beyond. Our study will focus on the analysis of the progress status of SDG 2 indicators in WAEMU member countries in 2021. The goal is to evaluate the extent of implementation of SDG 2 in these countries, providing a clear picture of where they stand in achieving food security. Specifically, the study will:
Conduct an inventory of SDG 2 indicators in the WAEMU region. Review the current state of food insecurity in these countries. Examine the factors influencing the achievement of SDG 2 indicators. Provide a comparative analysis of the progress made in different WAEMU countries.
The research will explore the following assumptions:
The global trajectory toward achieving Zero Hunger by 2030 remains off track, with alarming rates of hunger and food insecurity.
Achieving SDG 2 requires considering the unique challenges faced by each country, particularly those in resource-poor regions like sub-Saharan Africa. Insecurity and conflicts significantly hamper agricultural policies, further exacerbating food insecurity.
The primary questions guiding this study are: What are the primary causes of hunger in WAEMU countries? What are the consequences of hunger in these countries? How has the implementation of SDG 2 indicators affected the situation in WAEMU member countries?
Problem Statement:
What progress have WAEMU member countries made in achieving the SDG 2 targets in 2021, and what are the main obstacles limiting the effectiveness of the implementation of these indicators?
This study will be divided into four key sections: a literature review, methodology, results and discussions, and concluding with proposed solutions to the issues identified.
A. LITERARY REVIEW
I. Some definitions
Agricultural Support: Agricultural support refers to the set of policies, measures, and financial resources aimed at improving agricultural production, productivity, and the profitability of agricultural enterprises. This may include subsidies, loans, infrastructure, advisory services, agricultural insurance, and investments in agricultural research and development. Source: FAO - Food and Agriculture Organization of the United Nations
Access to Food: Access to food refers to the ability of individuals to obtain an adequate amount of food regularly and at affordable prices. This depends on food availability, individual income, and economic and social policies. Source: FAO - Food and Agriculture Organization of the United Nations
SDG 2 Indicators: The indicators of Sustainable Development Goal (SDG) 2, titled “Zero Hunger,” measure the progress made by countries in eradicating hunger, ensuring food security, improving nutrition, and promoting sustainable agriculture. These indicators include measures of malnutrition prevalence, access to essential nutrients, and the resilience of food systems. Source: United Nations - Sustainable Development Goals (SDGs)
Food Security: Food security exists when all people, at all times, have physical, social, and economic access to sufficient, safe, and nutritious food that meets their dietary needs and preferences for an active and healthy life. This includes availability, accessibility, stability, and proper utilization of food. Source: FAO - Food and Agriculture Organization of the United Nations
Malnutrition : Malnutrition refers to a condition resulting from an inadequate diet, either excessive or insufficient, leading to negative health outcomes. Malnutrition can include undernutrition (deficit in calories and nutrients), overnutrition (excess nutrients, such as obesity), and micronutrient deficiencies (such as vitamins and essential minerals). Source: World Health Organization (WHO)
Zero Hunger: The “Zero Hunger” initiative (SDG 2) aims to eliminate hunger and malnutrition by 2030. This includes the goal of ensuring that all people have access to sufficient, healthy, and nutritious food, promoting sustainable agriculture, and enhancing the resilience of populations to food crises. Source: United Nations - Sustainable Development Goals (SDGs).
II. The origins of world hunger
2. Climate Change and Its Impact on Food Security
Climate change, such as droughts, floods, and instability in agricultural seasons, severely affects food production in many regions of the world. These phenomena alter agricultural yields, thus reducing food supply, particularly in poor and vulnerable countries. Source: IPCC - Intergovernmental Panel on Climate Change
3. Armed Conflicts and Forced Displacement:Drivers of Hunger Worldwide
Armed conflicts are significant drivers of hunger, as they destroy agricultural infrastructures, disrupt food supply chains, and force millions of people to flee their homes. Conflict-displaced people are often deprived of access to sufficient food. Source: World Food Programme (WFP)
4. Poverty and Limited Access to Food Resources
Poverty is one of the main causes of hunger. Families living below the poverty line cannot afford to buy quality food or access essential resources such as clean water and the infrastructure needed to grow their own food. Source: World Bank
5. Inefficient Agricultural Systems and Lack of Adequate Infrastructure
Many developing countries face inefficient agricultural systems, often outdated or poorly suited to local climatic conditions and needs. The lack of storage, transport, and distribution infrastructure contributes to food loss and reduced food production. Source: FAO - Food and Agriculture Organization
III. Global hunger situation
The UEMOA (West African Economic and Monetary Union) region, comprising Benin, Burkina Faso, Côte d Ivoire, Guinea-Bissau, Mali, Niger, Senegal, and Togo, faces a challenging hunger situation, with significant disparities in food security. The region is particularly vulnerable to the effects of climate change, economic crises, and conflicts, all of which exacerbate food insecurity.
Hunger and Food Insecurity: According to the FAO and WFP, the UEMOA countries experience high levels of food insecurity, with millions of people suffering from malnutrition and limited access to sufficient food.
Malnutrition Rates: The Global Hunger Index (GHI) indicates that the majority of countries in the UEMOA region are classified as having serious or alarming levels of hunger and malnutrition, especially with regards to child malnutrition (stunting and wasting).
Impact of Conflicts and Displacement: Countries like Burkina Faso and Mali have been significantly impacted by conflicts, displacing large numbers of people and disrupting local food production. In 2022, these countries faced an increasing number of displaced persons, further worsening food security Today, the FAO estimates that 783 million people go hungry every day. According to the same source, if current trends continue, the number of undernourished people will exceed 600 million by 2030.
IV. The consequences of hunger
Hunger or food insecurity translates into a deterioration in the quality of the diet. It increases the risk of malnutrition, which can lead to undernutrition or, conversely, to overweight and obesity, on the increase in all regions of the world (Flourens, 2023). In children, hunger is the main cause of stunted growth and cognitive dysfunction. Undernourishment significantly affects physical capacity, cognitive development and learning, resulting in lower productivity. Beyond the suffering of individuals and families, it reduces the profitability of socio-economic investments (FAO, 2023)
V. Hunger in the SDGs
The beginning of the 21st century marked a decisive turning point when the international community once again recognized that a large proportion of the world s population lived in abject poverty and were deprived of basic human rights. This gave rise to the Millennium Development Goals (MDGs) in 2000, divided into eight (08) objectives. The first of these goals was to “eradicate extreme poverty and hunger”.
Sustainable development is a set of decisions that improves living conditions in the present without endangering resources for future generations. On September 25, 2015, alongside the UN General Assembly, 193 world leaders committed to 17 global goals by 2030 to help end extreme poverty, fight inequality and injustice, and address climate disruption (fiche_thematique_odd.pdf, s. d.). The effective implementation of these objectives cannot be achieved without a minimum level of well-being in terms of food security, because as they say in Africa “an empty stomach has no ears”.
1. SDG 2 targets
Target 2.1: concerns universal access to safe, nutritious and adequate food. The aim is to eliminate hunger and guarantee access for all, particularly the poor and people in vulnerable situations, including infants, to safe, nutritious and adequate food throughout the year.
Target 2.2l: aims to put an end to all forms of malnutrition. This means putting an end to all forms of malnutrition, in particular by reaching internationally agreed targets for stunting and wasting in children under 5 by 2025, and meeting the nutritional needs of adolescent girls, pregnant and lactating women, and the elderly.
Target 2.3: aims to double the productivity and income of small-scale food producers. This will involve doubling the agricultural productivity and income of small-scale food producers, particularly women, indigenous peoples, family farmers, pastoralists and fishermen, notably through secure and equal access to land, other productive resources and inputs, knowledge and financial services; markets and opportunities for value-added and off-farm employment.
Target 2.4: relates to sustainable food production and resilient agricultural practices. This will involve ensuring sustainable food production systems and implementing resilient agricultural practices that increase productivity and production, contribute to the maintenance of ecosystems, enhance resilience to climate change, extreme weather, drought, floods and other disasters, and progressively improve land and soil quality.
Target 2.5: aims to Maintain genetic diversity in food production. This will involve maintaining the genetic diversity of seeds, crops, farmed and domesticated animals and their associated wild species, notably through well-managed and diversified seed and plant banks at national, regional and international levels, and promoting access to equitable and fair resources. The equitable sharing of benefits arising from the use of genetic resources and associated traditional knowledge, as agreed internationally.
Target 2.A: relates to investment in rural infrastructure, agricultural research, technology and genebanks. The aim is to increase investment, notably through enhanced international cooperation, in rural infrastructure, agricultural research and extension services, technology development and plant and livestock genebanks, in order to boost the agricultural production capacity of developing countries, particularly the least developed.
Target 2.B: aims to prevent agricultural trade restrictions, market distortions and export subsidies. This will involve correcting and preventing trade restrictions and distortions on world agricultural markets, notably through the parallel elimination of all forms of agricultural export subsidies and all export measures with equivalent effect, in line with the mandate of the Doha Development Round.
Target 2.C: aims to guarantee stable food markets and rapid access to information. This will involve adopting measures to ensure the smooth functioning of markets for food products and their derivatives, and facilitating timely access to market information, including on food reserves, in order to help limit extreme volatility in food prices.
2. SDG 2 indicators
There are thirteen (12) indicators, listed as follows:
Prevalence of undernourishment Prevalence of food insecurity Prevalence of stunting in children Prevalence of child malnutrition (wasting or overweight) Production per labor unit Income of small-scale food producers; Sustainable food production Genetic resources in conservation facilities ; Local breeds threatened with extinction; Agricultural orientation index ; Official flows to agriculture; Export subsidies for agricultural products Food price anomalies
V. Study area
For the purposes of our study, we focused on the WAEMU member countries: Benin, Burkina Faso, Ivory Coast, Guinea-Bissau, Mali, Niger, Senegal, and Togo. This selection was made to assess hunger indicators in these nations, many of which are highly impacted by food insecurity.
B.MATERIAL AND METHODOLOGY
I. Materials
The database from the website Our World in Data, which offers an extensive collection of data sorted by category and period, served as the primary source for data extraction.
For data processing and summarization, RStudio software was employed to analyze the data and generate an overall summary for interpretation.
To collect field data, we used the Kobotoolbox, a robust tool for information and data collection. This platform facilitated the creation of questionnaires, which were essential for gathering information that contributed to the data used throughout the document. Specifically, we developed two survey forms, targeting:
Research Questionnaire : Mothers of children under 5 years old to collect data on child nutrition; Households to gather demographic information; Families to determine whether they have received or are receiving financial aid from the government or NGOs; Mothers to assess their knowledge of nutritional health.
Interview guide: Government officials Ministers, secretaries-general, or directors in the ministries of agriculture, food security, health, rural development, and economy; International organizations: Organizations that support efforts to reduce hunger in the region; Non-governmental organizations (NGOs): Local or international NGOs working in the fields of food security, rural development, and nutrition.
Agricultural cooperatives: Those directly involved in agricultural and food policies in the region. For managing the bibliographic review and writing the document, Word and Zotero were used to handle citations and word processing. Finally, the maps used in the study were created using QGIS software.
II. Methodology
In this study, we had the freedom to choose the study area. We selected the WAEMU member countries for their symbolic unity and complementarity in cultural and social matters, as well as their shared currency, the CFA franc (F CFA). These countries, both Sahelian and coastal, aim to complement each other in their development efforts. Our focus was to highlight the progress made by this group of countries in achieving the 2030 SDGs, with particular emphasis on SDG 2: “ZERO HUNGER”.
The data search was carried out using the official data site Our World in Data, which provides thematic data across various regions globally. The data we collected pertain to the countries in our study area for the year 2021, as this was the year when all relevant data for these countries were updated and made available.
The data was processed using R and RStudio software to draw conclusions that could either support or challenge the hypothesis presented. Various R packages were used to facilitate analysis: the factominer package was employed for Principal Component Analysis (PCA), the factoshiny package for clarifying the various individuals, and the Factoextra package for generating the Biplot.
We then created maps using QGIS software to visually present and interpret our findings, specifying the variables according to the different countries. After reviewing numerous scientific documents on our topic, we ensured all bibliographic information was properly cited in the writing. For data collection, we developed a simulation questionnaire, as field data collection was not required for this study.
For the analysis, we focused exclusively on quantitative variables, given the nature of the study’s research question. This question necessitated the consideration of thirteen (12) variables, which represent SDG indicators. Since data was unavailable for some variables, these were excluded from the study. However, to enhance the relevance of the data interpretation, additional variables were incorporated into this study.
C. PRESENTATION OF DATA USED
I. Definition of the variables used
The vision of SDG 2 to 2030 is to eliminate hunger, achieve food security, improve nutrition and promote sustainable agriculture. The United Nations (UN) has defined 8 targets and 13 indicators for SDG 2 (ONU, 2023) The variables used are :
Population (POP) : this is the latest census results for the various countries, taking into account the growth rate.
Share of undernourished population(PSFS): this is the proportion of individuals in a population who experienced moderate or severe levels of food insecurity during the reference period. Moderate food insecurity is generally associated with the inability to eat a healthy, balanced diet on a regular basis. Severe food insecurity generally implies a reduction in food intake and therefore more severe forms of undernutrition, including hunger.
The proportion of stunted children(MCSS): the proportion of children under 5 years of age who are stunted, meaning that their height/age ratio is at least 2 standards deviations below the median of the World Health Organization (WHO) child growth standards. Stunting is a consequence of severe malnutrition (WHO, 2023).
The proportion of emaciated children (MCW): Both wasting and overweight are defined as malnutrition. A child is defined as “emaciated” if his or her weight/height ratio is more than 2 standard deviations below the median of WHO child growth standards (https://ourworldindata.org/sdgs/zero-hunger, 2023).
Proportion of overweight children(MCO): Both wasting and overweight are defined as malnutrition. A child is defined as “overweight” if his or her weight/height ratio is more than 2 standard deviations above the median of WHO child growth standards (https://ourworldindata.org/sdgs/zero-hunger, 2023)
Productivity of small-scale foodproducers (PSP): This indicator is measured as the ratio between annual production and the number of working days in a year.
Agricultural value added per worker (AVW): This indicator is measured as the ratio between annual production and the number of working days in a year.
Average income of small-scale food producers (ASFP): measured in terms of annual income from agricultural production of food and agricultural products. Small food producers are those whose land area, livestock and economic income from agricultural activities are in the bottom 40% of the national distribution of these measures.
The agricultural orientation index for government expenditure (AOI): The agricultural orientation index (AOI) corresponds to the agricultural share of government expenditure, divided by the agricultural share of GDP. An IOA greater than 1 means that the agricultural sector receives a higher share of public spending in relation to its economic value while an IOA of less than 1 reflects a weaker orientation towards agriculture.
Total financial assistance and agricultural flows per recipient (FAFA):
this refers to official development assistance flows to countries and territories that are members of the Development Assistance Committee (DAC) of the Organization for Economic Co-operation and Development, and to multilateral institutions that meet a set of criteria linked to the source of financing, the purpose of the transaction, and the concessional nature of the financing.
The Food Price Anomaly Indicator (FPAI): identifies unusually high market prices, by assessing price growth in a given month over many years, while taking into account the seasonality of agricultural markets and inflation.
Gross domestic product (GDP)
an economic indicator used to measure a country’s wealth production. Gross domestic product (GDP) measures the value of all goods and services produced in a country over the course of a year.
Terrorist attacks (TA) : these are a series of acts of violence (attacks, hostage-taking, etc.) committed by an organization or individual to create a climate of insecurity, to blackmail a government, or to satisfy hatred towards a community, a country or a system.
II. Table of values used in the analysis
The table 1 below shows the data for the variables and individuals used in our study.
setwd("D:/MASTER 2024-2025/2IE/P_RTI/RTI_groupe10_GEAAH")
data = read.csv(file ="ukamkeu.csv", header = TRUE, sep = ";", quote = "\"",
dec = ",", row.names = 1)
data## POP MCSS... MCO... MCW... PSFS... AVW... ASFP... AOI
## BENIN 13410443 30.9 2.1 5.0 10.4 22038386 712.80 0.13
## COTE D'IVOIRE 24641638 20.9 2.6 8.1 9.1 17076805 650.05 0.19
## BURKINA FASO 22147544 22.4 2.0 6.1 15.1 2906019 914.88 0.13
## GUINEE 1370460 28.5 5.4 7.8 9.9 9006822 1073.62 0.08
## MALI 22397430 24.3 1.9 9.3 7.1 14812776 766.85 0.08
## NIGER 24543400 47.1 2.0 9.8 12.5 7099216 301.58 0.03
## SENEGAL 17220783 17.3 3.0 8.1 4.5 28144626 1093.48 0.16
## TOGO 8883093 22.9 2.1 5.7 14.1 13640979 409.75 0.01
## FAFA... FPAI GDP..MILLARDS. TA
## BENIN 75670000 0.84 14.39 1
## COTE D'IVOIRE 210560000 0.89 16.18 161
## BURKINA FASO 205840000 0.65 59.90 1
## GUINEE 10620000 0.11 1.44 0
## MALI 283670000 0.32 17.28 139
## NIGER 189070000 0.30 12.92 49
## SENEGAL 141230000 0.06 23.40 1
## TOGO 27890000 0.99 6.99 0D. RESULTS AND DISCUSSION
I. Analysis of cpa results
The objective of PCA is to provide a synthetic representation of large numerical datasets, mainly through graphical visualizations. This dataset includes 8 individuals and 12 variables, and the analysis of the graphs reveals no outliers.
1. Inertia distribution
The inertia of the first dimensions indicates the strength of relationships between variables and helps determine the number of dimensions that should be analyzed. The first two dimensions account for 56.24% of the total inertia in the dataset, meaning that 56.24% of the total variability in the individuals (or variables) cloud is explained by this plane. This percentage is relatively high, suggesting that the first plane accurately represents the data’s variability.Table 2 below displays the distribution of variances across the seven axes, providing a comprehensive overview of the information. The graphical representation of this data is shown in Figure 2.
library(FactoMineR)## Warning: package 'FactoMineR' was built under R version 4.4.3library(factoextra)## Warning: package 'factoextra' was built under R version 4.4.3## Loading required package: ggplot2## Warning: package 'ggplot2' was built under R version 4.4.3## Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBapca_1 = PCA(X = data, scale.unit = TRUE, ncp = 5, ind.sup = NULL,
quanti.sup = NULL, quali.sup = NULL, row.w = NULL,
col.w = NULL, graph = TRUE, axes = c(1,2))
fviz_eig(pca_1, addlabels=TRUE, hjust = -0.3) 
ylim(0, 65)## <ScaleContinuousPosition>
## Range:
## Limits: 0 -- 65 library(FactoMineR)
library(factoextra)
library(ggplot2)
library(factoextra)Indeed, the inertia of the first axis is not greater than that obtained from the 0.95 quantile of random distributions (29.18% compared to 39.57%). This suggests that no axis contains significant information. As a result, the analysis will focus on these axes. Therefore, we will rely on the cumulative percentage of the first two axes for interpreting the results. Table 3 provides a summary of the contributions of individuals and their quality of representation in the construction of these first two axes. Table 3 : Contributions of the variables and their quality of representation
fviz_eig(pca_1, addlabels=TRUE, hjust = -0.3)
Therefore, the principal component analysis presented in table reveals that the variables making the largest contributions to the formation of Axis 1 and Axis 2 are as follows:
Table 4 : Variables that contribute most to the creation of Axis 1 and Axis 2
DIM 1 DIM 2 POP ASFP FAFA PSFS TA IOI Source : CPA results
2. Description of axes
fviz_pca_ind(pca_1, col.ind = "contrib", gradient.cols = c("blue" , "green" , "red"), repel = TRUE)
The labeled individuals are those that make the greatest contributions to the construction of the plane. The graph displays the individuals projected based on their correlation with the axes. The stronger the correlation of an individual with an axis, the closer they are to that axis when projected. Specifically:
Dimension 1 opposes individuals such as MALI and COTE D’IVOIRE (to the right of the graph, characterized by a strongly positive coordinate on the axis) to individuals like SENEGAL and GUINEE (to the left of the graph, characterized by a strongly negative coordinate on the axis).
Dimension 2pposes individuals such as SENEGAL and GUINEE (at the top of the graph, characterized by a strongly positive coordinate on the axis) to individuals like TOGO and NIGER (at the bottom of the graph, characterized by a strongly negative coordinate on the axis).
library(ggplot2)
library(factoextra)
fviz_pca_var(pca_1, col.var = "cos2" , gradient.col = c("blue" , "green" , "red"), repel = TRUE )
Figure 4 : Individuals factor map (PCA)
This graph shows the correlation between the initial variables and the principal axes of the PCA (Principal Component Analysis):
The longer and closer a vector is to the circle, the better the variable is represented in the plane (Dim1–Dim2).
Variables that are close to each other are positively correlated. Opposite variables (at 180°) are negatively correlated. Perpendicular variables are uncorrelated.
Dim1 (29.18%): the first axis, representing the factor that explains the most variance.
Dim2 (27.06%): the second axis.
Together, these two axes explain 56.24% of the total information. This provides a solid basis for interpretation.
Analysis of the principal axes
Dim1 (horizontal axis – 29.18%): The variables that are strongly projected onto Dim1 are:
FAFA, POP, GDP, MILLARDS: positively correlated. These are indicators of economic and demographic capacity. These variables reflect an axis of economic power, financing, and country size. Countries that are well projected on this axis are likely those with more means to achieve SDG 2 (Sustainable Development Goal 2).
At the opposite end:
MCO is weakly projected but oppositely oriented: this indicates that it is negatively correlated with development (chronic malnutrition). Dim2 (vertical axis – 27.06%):
The variables strongly projected here are:
ASFP, AVW, AOI, MCW, TA, which are related to political action, agricultural public support, technical or direct intervention indicators (allocations, nutritional support).
At the bottom of the axis:
MQSS, PSFS, FPAI are technical or social variables, negatively projected onto Dim2, thus opposed to the previous ones. Dim2 contrasts political/intervention approaches with the everyday social realities.
Correlations and Cross-Interpretations
Groups of strongly correlated variables
GDP, FAFA, MILLARDS, POP → a very coherent group of economic variables. AOI, AVW, ASFP → agricultural programs or public policies.
PSFS, MQSS, FPAI → indicators of real food security or social aid, weakly represented but consistent with each other.
Oppositions to note
The variables GDP & MCO → MCO measures a nutritional or health issue, inversely proportional to development.
The variables FAFA vs MQSS / PSFS → it is possible that agricultural financing is more associated with economically stronger countries, but not necessarily with immediate results on the social field.
II. Analysis of classification results
The classification results in the formation of three distinct groups or classes, which align with the outcomes of the Principal Component Analysis (PCA):
- Group 1: Guinea and Togo
- Group 2: Benin and Senegal
- Group 3: Mali, Niger, Ivory Coast and Burkina Faso
This grouping is based on the specific characteristics of these countries, as defined by the variables included in the analysis. Each group is characterized by particular patterns in relation to the variables, which have been instrumental in forming these clusters.
Cluster 1 consists of individuals such as Guinea and Togo. This group is
distinguished by:
- Low values
for the variable the POP and FAFA variables… (from the most extreme to the least extreme), which stands out as the most notable characteristic of this group.
Cluster 2 includes individuals like Benin and Senegal. This group is marked by:
- High values for the AVW variable, with these being the most defining features of this cluster.
Cluster 3 is composed of individuals like Mali, Niger, Ivory Coast and Burkina Faso. This group is characterized by:
- High values
for the FAFA and POP variables (from the most extreme to the least extreme), which are the strongest contributors to this cluster’s profile. These characteristics distinguish this group from the others, highlighting a focus on productivity and economic output.
The hierarchical tree can be drawn on the factorial map with the individuals colored according to their clusters.
III. Interpretation of results
fviz_pca_biplot(pca_1, repel = TRUE,col.var = "blue",col.ind = "red")
Figure 8: BIplot
- Variables The variables (FAFA, POP, GDP, BILLIONS, etc.) represent indicators associated with SDG 2. The longer and more directed a vector is, the more it contributes to the axis.
Variables in the same direction: positively correlated variables (GDP, BILLIONS, FAFA, POP, etc.).
Opposite variables: negatively correlated variables (MCO vs GDP).
- Individuals
Countries that are close to each other are similar according to the dimensions of PCA. Their position indicates how they project onto the axes defined by the variables.
Ivory Coast, Mali, Burkina Faso: strongly projected on Dim1(+), which may explain their strong performance in variables such as FAFA, GDP, BILLIONS. Togo, Guinea, Benin: positioned to the left on Dim1(−), associated with lower values of economic variables.
Senegal: very high on Dim2, strongly standing out (perhaps due to variables like ASFP or AVW).
The goal of SDG 2 includes targets such as food security, nutrition, and the promotion of sustainable agriculture.
Countries like Ivory Coast, Mali, and Burkina Faso appear to be better positioned along the axes of financing, population, or development (possibly reflecting visible efforts toward SDG 2).
Togo, Benin, and Guinea may be further behind in these areas in 2021. Senegal seems to have a very distinct profile, possibly related to specific policies or a unique indicator like ASFP (Agricultural Public Support).
Niger has a strong economic projection on Dim1(+), but could face structural challenges (as suggested by Dim2(−)).
- Dim1 (29.2%) represents an economic and financing gradient: countries that are strongly projected in this direction (e.g., Ivory Coast, Mali) are associated with variables such as GDP, FAFA (Agricultural Financing), and POP (population). This dimension broadly reflects the level of economic resources mobilized to achieve SDG 2.
- Dim2 (27.1%) expresses another form of differentiation, potentially linked to specific agricultural support initiatives or targeted national policies, as indicated by the strong contribution of variables like ASFP, AVW, and AOI.
Ivory Coast, Mali, and Burkina Faso stand out positively on Dim1, indicating a more robust economic dynamic in relation to food security and a certain mobilization of public resources.
Senegal stands out strongly on Dim2, which could indicate the implementation of specific agricultural or nutritional policies, distinguishing it from the other countries despite a moderate position on Dim1.
Togo, Guinea, and Benin, on the other hand, are negatively positioned on Dim1. This suggests a less favorable situation in terms of agricultural financing, production, or achieving SDG 2-related targets.
Niger presents a contrasting profile: relatively strong on the economic axis Dim1(+), but lagging on Dim2, indicating structural challenges or gaps in certain specific policies.
The various figures below present the state of implementation of the SDGs in UEMOA member countries.
IV. DISCUSSION
The analysis of progress towards achieving SDG 2 (Zero Hunger) in WAEMU member countries in 2022 reveals several significant insights.
First and foremost, the level of hunger within the region shows considerable variation from one country to another. Some countries, benefiting from a high GDP and substantial investments in the agricultural sector, have managed to achieve moderate levels of hunger. This suggests that adequate resource allocation, coupled with effective agricultural policies, can play a crucial role in mitigating hunger.
However, other nations, particularly those that are landlocked or experiencing political instability, continue to struggle with severe hunger issues. This highlights the importance of geographical and political factors in addressing food insecurity. Tailored strategies and targeted interventions are needed to ensure food security in these vulnerable regions.
Equally concerning is the observation that, in some countries, despite having geographical advantages, there is an inability to distribute food resources equitably across their territories. This disparity results in significant inequalities, with stark contrasts in nutrition levels, ranging from malnourished children to those suffering from obesity.
It is critical for these countries to revise and improve their resource distribution policies to ensure that every citizen, particularly children, has access to adequate nutrition.
Given these findings, it is evident that more efforts are needed to meet the targets of SDG 2 in WAEMU member countries. The study also highlights the gaps in current knowledge regarding hunger and malnutrition in the region. There is a pressing need for further research to deepen our understanding of the underlying mechanisms of food insecurity and to evaluate the effectiveness of current policies and interventions. Future studies could also examine additional factors affecting food security, such as climate change and migration patterns.
These goals are undeniably ambitious, as eradicating hunger globally presents immense challenges. External factors such as epidemics and armed conflicts further hinder progress in achieving food security, slowing down efforts to overcome hunger.
V. PROPOSED SOLUTION
- Increase investment in agriculture: To foster agricultural growth, it is crucial to enhance investment by providing sufficient financial support to farmers, improving agricultural infrastructure, and promoting sustainable farming practices.
- Improve access to water and irrigation infrastructure: Given the challenges posed by the dry climate in several countries within the study region, it is vital to develop efficient irrigation systems and enhance access to water for agriculture to ensure consistent food production.
- Promote agricultural diversification: Encouraging the diversification of crops can help mitigate the risks associated with relying on a single crop, increase resilience to climate change, and enhance food security by providing a broader range of nutritious food options. Strengthen food storage and distribution systems: To minimize post-harvest losses and ensure the equitable distribution of food, improving storage and transportation infrastructure is essential, particularly in landlocked areas.
- Enhance regional cooperation: Regional collaboration is key to achieving SDG 2. By working together, countries in the region can share best practices, exchange resources, and foster regional agricultural trade, creating a more resilient food system.
- Build resilience to climate change: As climate change poses a significant threat to food security, it is crucial to strengthen agricultural systems’ resilience by adopting sustainable farming practices and implementing strategies to adapt to changing climate conditions.
CONCLUSION
In conclusion, the extent of hunger and food insecurity within the WAEMU region varies significantly across countries. Several factors, including food resource availability, climate conditions, security issues, and market access, contribute to these disparities. Countries like Benin and Senegal have made considerable progress in food security, largely due to their emphasis on agriculture, favorable geographical conditions, and relative stability. In contrast, nations such as Burkina Faso, Mali, Ivory Coast and Niger face critical challenges, where insecurity, a harsh climate, and landlocked status hinder their economic and agricultural development. Meanwhile, Guinea, and Togo, despite benefiting from favorable geography and climate for agriculture and political stability, still suffer from severe hunger due to the uneven distribution of resources. To achieve the goal of eradicating hunger by 2030, it is crucial to implement coordinated, urgent policy actions aimed at addressing long-standing inequalities, transforming food systems, promoting sustainable agricultural practices, and mitigating the impacts of conflict and pandemics on food security. By working collaboratively to meet SDG 2, we can build a world where hunger is eradicated, everyone has access to healthy, nutritious food, and agriculture is sustainable and environmentally responsible.
References
063 ERNE CMAM Surge Approach Update (Fr).pdf 2022. (s. d.). Baudin, B. (2014). Malnutrition et sous-alimentation. Revue Francophone des Laboratoires, 2014(466), 25‑37. https://doi.org/10.1016/S1773-035X(14)72709-1 Byerlee, D., & Fanzo, J. (2019). The SDG of zero hunger 75 years on : Turning full circle on agriculture and nutrition. Global Food Security, 21, 52‑59. https://doi.org/10.1016/j.gfs.2019.06.002 Ce qui peut mener à l’insécurité alimentaire dans un pays d’abondance | CIUSSSCN. (2023). https://www.ciusss-capitalenationale.gouv.qc.ca/sante-publique/inegalites-sociales-sante/vivre-sans-faim/informer/causes Deng, Z., & Lindeboom, M. (2022). Early-life famine exposure, hunger recall, and later-life health. Journal of Applied Econometrics, 37(4), 771‑787. https://doi.org/10.1002/jae.2897 Dufumier, M., & Hugon, P. (2008). PIQUES ET POLÉMIQUES LES « ÉMEUTES DE LA FAIM » : DU SOUS INVESTISSEMENT AGRICOLE À LA CRISE SOCIOPOLITIQUE. Revue Tiers Monde, 196(4), 927‑934. https://doi.org/10.3917/rtm.196.0927 Ebata, A., Nisbett, N., & Gillespie, S. (2021). Food Systems After Covid-19. IDS Bulletin, 52(1). https://doi.org/10.19088/1968-2021.107 FAO. (2023). L’insécurité alimentaire : La faim au quotidien et la crainte permanente de la famine. https://www.fao.org/3/x8200f/X8200F05.htm Fiche_thematique_odd.pdf. (s. d.). Consulté 19 octobre 2023, à l’adresse https://www.unicef.fr/sites/default/files/fiche_thematique_odd.pdf Flourens, M. (2023, février 18). Faim dans le monde en 2023 : Causes, conséquences et solutions. Oxfam France. https://www.oxfamfrance.org/agriculture-et-securite-alimentaire/faim-dans-le-monde-causes-consequences-solutions/ Goulenok, C., & Cariou, A. (2006). Obésité en réanimation, définition, épidémiologie, pronostic. Réanimation, 15(6), 421‑426. https://doi.org/10.1016/j.reaurg.2006.09.003 Https://ourworldindata.org/sdgs/zero-hunger. (2023). Https://perspective.usherbrooke.ca/bilan/servlet/BMTendanceStatPays?langue=fr&codePays=BFA&codeStat=NY.GDP.MKTP.CD&codeTheme=2. (2023). Iversen, T. O., Westengen, O., & Jerven, M. (2023). Measuring the end of hunger : Knowledge politics in the selection of SDG food security indicators. Agriculture and Human Values, 40(3), 1273‑1286. https://doi.org/10.1007/s10460-023-10418-6 ONU. (2023). Https://www.un.org/sustainabledevelopment/fr/hunger/. Rastoin, J.-L. (2020). Éditorial : Crises sanitaires, résilience et refondation des systèMCO alimentaires [Application/xhtml+xml,application/pdf,online,database]. 15 pages, pages 17-31. https://doi.org/10.15122/ISBN.978-2-406-11062-0.P.0017 Thivet, D. (2012). Des paysans contre la faim. La « souveraineté alimentaire », naissance d’une cause paysanne transnationale. Terrains & travaux, 20(1), 69‑85. https://doi.org/10.3917/tt.020.0069 Traore, A. (2023). LA BIOTECHNOLOGIE, UN OUTIL EFFICACE DANS LA LUTTE CONTRE LA FAIM ET LA PAUVRETE EN AFRIQUE SUBSAHARIENNE : CAS DU BURKINA FASO. WHO. (2023). Malnutrition. https://www.who.int/health-topics/malnutrition
ANNEXES
Link Interview guide https://kf.kobotoolbox.org/#/forms/aStVh8ysMccX6hkUon6SzF
Link Research questionnaire https://kf.kobotoolbox.org/#/forms/aStVh8ysMccX6hkUon6SzF