FAMINE IN WEST AFRICA
KABORE Abdoul Aziz KABORE B Abdoulaye TINTO Nazira
FAMINE IN WEST AFRICA
The main reasons for the persistence of famine in West Africa.
KABORE Abdoul Aziz, KABORE Abdoulaye, TINTO Nazira
S7 GEAAH Group 12
SUMMARY
The 2024 report of the Food and Agriculture Organization of the United Nations (FAO) revealed that famine still persists in the majority of African countries, particularly in West Africa. Despite all the efforts made by these countries to improve their food situation, it is clear that part of their population struggles to have access to their daily food. This therefore reflects the persistence of famine in these countries. Research into the causes of the persistence of famine in West Africa is therefore necessary to achieve SDG 2 “zero hunger” which aims to eradicate hunger in the world. Our study therefore aims to identify the causes of the persistence of famine in West African countries. From the analysis of our selected variables, it emerges that there is a strong positive correlation between the GDP and Literacy Rate variables, an average correlation between the GDP and food loss variables and a negative correlation between the hunger index and GDP variables. From the linear regression, it is found that to reduce the famine rate, emphasis should be placed on the variables Population growth rate, Food inflation rate, Poverty rate and Literacy rate.
INTRODUCTION
The world faces many problems, including poverty, hunger, lack of access to clean water and many others. To address these problems, the Sustainable Development Goals (SDGs) were established. The one that will catch our attention is SDG 2 “zero hunger”, which aims to reduce world hunger and improve food security by 2030..According to FAO, despite progress, hunger remains a major problem in many parts of the world, particularly in sub-Saharan Africa and Asia. SDG 2, ‘Zero Hunger’, aims to eradicate hunger and improve food security by 2030. This goal is based on the need to ensure adequate nutrition for all, improve the resilience of food systems and make agriculture more sustainable and accessible.[1]
But it is clear that SDG2 is far from being achieved because according to the FAO report, in 2024 nearly 755 million people in the world will suffer from hunger. Africa alone represents approximately 20.4% of this population. Indeed, famine in Africa remains a major, complex and recurring problem, affecting millions of people each year, exacerbated by economic, political and environmental factors. In its annual report, the Food and Agriculture Organization of the United Nations (FAO) estimates that in 2024 approximately 70.4 million people will suffer from hunger in West Africa, and this situation continues to deteriorate. According to the same report, severe or moderate food insecurity affects nearly 61.4% of people in West Africa.[2]. This phenomenon is not only a question of lack of food, it is also linked to the inability of populations to have regular and adequate access to it. As some researchers point out, “famine in West Africa is a product of political and economic choices.”[3].The link between persistent poverty, political instability and climate change is therefore central to understanding the root causes of this crisis.
The problem with our study lies in the fact that although studies have focused on the role of some of these factors in the persistence of famine in West Africa, very few of them provide us with answers to our study.
General objective of the research:
The overall objective of our study is to identify and assess the key factors contributing to the persistence of famine in West African countries.
Specific objectives:
More specifically, this will involve:
Firstly, to filter using the bibliographic review the most relevant explanatory variables to explain the persistence of famine in West African countries.
Then, to carry out a descriptive statistical analysis of these variables and spatially characterize their distribution in West Africa.
-Finally, to evaluate the weight of each of these variables in explaining the persistence of famine in West Africa using multiple linear regression and PCA.
Research questions
The main research questions that motivate this study are:
· What are the most relevant variables to explain the persistence of famine in West Africa?
· Is the statistical distribution of variables and their distribution different depending on the countries of West Africa?
1. Literary review
The causes of famine in West Africa are multiple and can be classified into several categories. First, on the environmental level, the region has had to face several food crises, including that of 1972, linked to a major drought in the Sahel. This drought is particularly significant if we consider the extent of rainfall deficits and water flows. Today, the situation remains worrying with an increased frequency of natural disasters, a phenomenon that is expected to worsen under the effect of global warming. According to experts, recent drought trends in West Africa have shown an increase in the frequency and intensity of extreme weather events. These changes have a direct impact on water availability and on agriculture, a key sector of the region’s economy. Future climate change projections indicate that droughts will become more frequent and severe, exacerbating conditions of poverty and food insecurity for the most vulnerable populations.[4]
Then, land degradation and desertification are also significant factors. Deforestation, overgrazing and intensive agriculture contribute to the decline in soil fertility and desertification, which directly harms agricultural yields. As one study highlights, land degradation in the Sahel, due to deforestation and intensive agriculture, has led to a decrease in the capacity of these lands to support sustainable agriculture, exacerbating food crises in the region.[5]. Furthermore, access to water and water stress represent major challenges. Irregular rainfall, combined with inefficient water management and overexploitation of groundwater, leads to water stress that further complicates agricultural production. “Water management in the Sahel remains one of the greatest challenges, as unpredictable rainfall patterns and overexploitation of water resources exacerbate the vulnerability of farming populations to famine.[6].
On the socio-economic front, several factors also play a central role. Rapid population growth in developing regions, particularly in sub-Saharan Africa, is exacerbating the situation of hunger and food insecurity. As the population increases, agricultural resources are becoming increasingly limited. This puts additional pressure on arable land and existing food systems, making it even more difficult to combat hunger and poverty.[7]. Furthermore, increasing urbanization rates in the region may lead to increased risks of famine. While urbanization is often seen as an economic driver, it can put excessive pressure on food production and distribution infrastructure in urban areas, making them more vulnerable to price increases and food shortages. Urbanization, while generally associated with economic improvements, can also lead to increased risks of famine in some contexts. Urban areas, particularly in developing countries, may be more vulnerable to food crises when food production and distribution infrastructure is inadequate.[8].
Poverty and social inequalities are also among the main causes of food insecurity. The low purchasing power of rural populations limits access to sufficient food resources, and economic inequalities aggravate this situation. Persistent poverty in West Africa has a direct consequence of a lack of access to food. The majority of the population, particularly in rural areas, cannot afford to buy the necessary food, even when markets are supplied. This situation is aggravated by economic inequalities and the absence of social safety nets.[9]. Moreover, food inflation is not only an economic factor, but a direct threat to the right to food. When prices of essential foods increase significantly, many poor and vulnerable populations are deprived of access to sufficient and nutritious food.[10].
In addition, some institutional causes, such as lack of infrastructure and governance, also contribute to food insecurity. Inefficient resource management, lack of support for small producers and weak agricultural infrastructure contribute to crop losses and inadequate management of food crises. Inefficient agricultural policies, lack of support for small producers and weak infrastructure for food storage and transportation lead to significant crop losses and inadequate management of food crises.[11]. The region’s over-reliance on food imports, particularly cereals, also exposes West Africa to global price fluctuations and supply chain disruptions. West Africa’s growing reliance on food imports makes the region particularly vulnerable to global crises.[12].
In addition, ineffective agricultural policies and poor management of natural and human resources contribute to the persistence of famine. Agricultural policies in West Africa have often failed to support smallholder farmers and improve agricultural productivity in a sustainable manner. This ineffectiveness is exacerbated by weak governance, low public investment in agriculture, and poor management of natural resources.[13]. The lack of long-term resilience strategies is also an aggravating factor. The persistence of food crises in West Africa is largely due to the lack of effective strategies to strengthen the resilience of farming communities to climate, economic and political risks.[14].
Finally, on the political front, armed conflict and instability play a major role in the persistence of famine. These conflicts disrupt agricultural systems, supply chains, and access to humanitarian assistance, while forcing people to flee their lands. Ongoing armed conflicts, particularly in Mali, Burkina Faso, and Nigeria, continue to play a major role in the persistence of famine in West Africa.[15]. These conflicts lead to forced migration of rural populations to cities, where they can no longer cultivate, reducing agricultural capacity. The relationship between migration, famine and conflict has become increasingly evident. The violence of armed conflicts in countries such as Syria, Yemen and South Sudan is often accompanied by famine and food shortages.[16].
In summary, we can say that the causes of the persistence of famine in West Africa are of three types. Firstly, we have environmental factors such as drought, climate change, soil degradation, desertification, irregular rainfall and poor management of water resources.
Then we have socio-economic factors such as: rapid population growth, high rate of urbanization, poverty and social inequality and food inflation.
Finally, we have institutional and political factors such as: lack of infrastructure and governance, poor agricultural policies, poor management of natural and human resources, armed conflicts and political instability.
2. Presentation of the study area
West Africa, located in the western region of the African continent, is a study area of great importance due to its geographical, cultural, economic and socio-political diversity. This region, composed of 16 member countries of the Economic Community of West African States (ECOWAS), has particular characteristics that make it an essential observation ground for the study of various issues, particularly in terms of development, agriculture, environment, governance and food security. Our study focuses on the 16 countries of West Africa, namely: Benin, Burkina Faso, Cape Verde, Côte d’Ivoire, Gambia, Ghana, Guinea, Guinea-Bissau, Liberia, Mali, Mauritania, Niger, Nigeria, Senegal, Sierra Leone and Togo.
3. Materials and methods used
To carry out our study, we started by collecting data through the Our World in Data database, then we processed and analyzed this data. Finally, the results obtained from this analysis were interpreted in space at the scale of these 16 West African countries through maps.
a. Materials
· R Studio:
R Studio is an integrated development environment (IDE), designed specifically for users of the R programming language. It has given us a complete suite of tools for our programming, data analysis, visualization, and reporting.
· QGIS
QGIS (Quantum GIS) is an open source geographic information system (GIS) software that allows users to create, visualize, edit, analyze, and publish spatial and cartographic data. It has been useful to us in the spatial interpretation of the data after its analysis.
· Kobotoolbox
KoboToolbox is an online platform that allows you to collect data in a simple and efficient way. Whether it is to conduct surveys, conduct polls, or collect information. It offers a complete and customizable solution.
· Zotero
Zotero is a free and open source reference management software that helps researchers and students organize and cite their sources. It allows users to collect, manage, and organize bibliographic data, including journal articles, books, websites, and other research materials.
· Office tools: Excel and Word
b. Method
In the collection, we would have opted for a qualitative approach by favoring surveys, which can be an appropriate approach for a population with lower literacy levels. Here are some key points that we mentioned regarding the methodology:
· Survey method:We have chosen structured interviews conducted through questionnaires that we will have written in advance, administered during door-to-door visits. This can facilitate the collection of information in a systematic and standardized manner.
· Structure of the questionnaire:The Kobocollect software questionnaire is divided into four parts, such as interviewer information, location information, household information, and data collection variables.
The data that were used for processing were extracted from databases
site datahttps://ourworldindata.org/,FAO.org, Worldbankdata.org.
West Africa Famine Data Collection Sheet
4. Results
The downloaded data were filtered on Excel to obtain the variables needed for our study. The variables retained are: Population Growth Rate (PGR), Precipitation (PRE), Poverty Rate (PR), Hunger Index (HI), Food Inflation Rate (FI), Gross Domestic Product (GDP), Pesticide Use (PU), Population Displacement Due to Natural Disasters (DCN), Literacy Rate (LR), Urbanization Rate (UR), and Food Losses (FL).
5. Method of analysis
4.1.1. Data analysis and interpretations
Data analysis with Nigeria and interpretation
Further analysis of our initial scatterplot highlighted one particularly atypical individual, namely Nigeria. This observation is reinforced by the results of the hierarchical clustering, illustrated in Figure 1, which reveal that Nigeria does not have similar characteristics to the other countries included in our study. In other words, Nigeria clearly stands out because of its exceptional qualities, which puts it in a category of its own.
Looking at the factorial design data, we find that Nigeria accounts for an impressive share in the total inertia which is 68.31% as shown in Figure 2. Analyzing the contributions to the axes in more detail, we find that Nigeria contributes 81.15% to the formation of axis 1. This means that its presence powerfully influences the structural dimensions we are studying. Moreover, Nigeria is characterized by remarkably high values in all the variables we considered, which further accentuates its uniqueness in this analysis.
From a socio-economic perspective, Nigeria is undeniably a leader in West Africa. With a population exceeding 200 million, Nigeria is the most populous country on the African continent, giving it a vast consumer market.
Furthermore, Nigeria’s industrialization process has advanced significantly in recent decades. The country has invested in various sectors, such as oil, construction materials and information technology, becoming a major economic force globally. This is reflected in a gross domestic product (GDP) that far exceeds that of other countries in the region. For example, according to the latest figures, Nigeria’s GDP is several times higher than that of its direct neighbors, illustrating its status as an economic powerhouse in West Africa.
However, despite these impressive economic advances, Nigeria faces significant structural challenges, including the growing phenomenon of famine. This paradox highlights the inequalities that persist within the country, where, despite significant mechanization and high agricultural production, a significant portion of the population suffers from hunger. This observation underlines the importance of an integrated approach that takes into account not only the economic successes, but also the social and environmental challenges that Nigeria faces.
In summary, Nigeria’s distinctive position within our analysis stems from its exceptional performance across the multiple variables examined. This country stands out not only for its economic strength, but also for the need to address crucial socio-economic challenges, placing it in a complex dynamic that clearly distinguishes it from other countries in the region.
4.1.1.1. Data Analysis Without Nigeria
a. The correlation matrix and circle
b. Correlation circle
A closer look at the correlation matrix presented in Figure 4 reveals significant relationships between several key variables, highlighting the interconnectedness of socio-economic and environmental factors. These correlations provide a good understanding of the causes that influence famine, economic growth and social well-being in general.
ü Literacy Rate (LR)
· Positive Correlations:
o With Urbanization Rate (UR): Increased urbanization is often associated with better access to education, suggesting that migration to cities provides more educational opportunities.
· Negative Correlations:
o With Hunger Index (HI): A very strong correlation indicates that when literacy rate increases, the hunger index probably decreases. This shows that education can increase awareness of sustainable food practices, thereby reducing food losses.
o With Poverty Rate (PR): this shows that an increase in the literacy rate could be associated with a significant reduction in the poverty rate.
o With Population Growth Rate (PGR): This suggests that communities with high literacy rates may have lower population growth rates, possibly due to better family planning and reproductive health education.
2. Urbanization Rate (UR)
· Positive Correlations:
o With Literacy Rate (LR): reinforces the idea that cities offer better educational infrastructure and more learning opportunities.
· Negative Correlations:
o With Poverty Rate (PR): High urbanization appears to reduce the poverty rate, indicating that urban areas provide more jobs and services.
o With Food Loss (FL): Increased urbanization may be linked to more efficient management of food resources, resulting in less loss.
3. Food Inflation Rate (FIR)
· Positive Correlations:
o With Hunger Index (HI): Increases in food inflation are often correlated with increases in hunger, showing that higher food prices jeopardize food security.
· Negative Correlations:
o With Gross Domestic Product (GDP): When GDP increases, food inflation may decrease, indicating that economic growth provides a means to stabilize food prices.
o With Poverty Rate (PR): shows a weak correlation indicating that food inflation has only a modest impact on poverty.
4. Hunger Index (HI)
· Positive Correlations:
o With Food Inflation Rate (FIR): a strong correlation that shows that increasing food inflation is strongly linked to increasing hunger, suggesting that policies should focus on controlling food prices.
· Negative Correlations:
o With Literacy Rate (LR): indicates a strong interdependence where higher literacy levels are linked to less hunger, reinforcing the idea that education is essential to improve food security.
o With Population Growth Rate (PGR): suggests that in regions where hunger is more pronounced, lower population growth rates may be observed, potentially due to precarious living conditions.
5. Population Growth Rate (PGR)
· Positive Correlations:
o With Literacy Rate (LR): This could suggest that higher literacy rates are associated with more effective birth control.
· Negative Correlations:
o With Hunger Index (HI): indicates that higher levels of hunger may coincide with lower population growth, suggesting that poor living conditions may limit growth.
o With Poverty Rate (PR): A strong correlation indicates that regions with high population growth often have high poverty levels, because resources are stretched.
o With Precipitation (PRE): a correlation suggesting that precipitation may have a negative effect on population growth in some regions, possibly linked to resource availability.
6. Population Displacement Due to Natural Disasters (DCN)
· Positive Correlations:
o With Hunger Index (HI): a slight positive relationship, indicating that displacement due to disasters could lead to food difficulties.
· Negative Correlations:
o With Literacy Rate (LR): no significant relationship, indicating that travel does not directly affect access to education.
o With Population Growth Rate (PGR): very weak correlation, suggesting that disasters do not significantly influence population growth.
c. The inertia graph
The inertia graph is intended to allow us to determine how many dimensions can be retained to explain a fairly large portion of the variance in the data. Thus, we notice that the first dimension explains 38.8% of the variance, it is very important because it contains almost 40% of the information. The second dimension, for its part, contains 22.5% of the information. And finally, from the third to the tenth dimension, contain low percentages of information. This can be easily seen through the trend line which shows a rapid decrease at the beginning, illustrating that the first dimensions are essential to understand the structure of the data. And once we reach the third dimension, the percentage of variance explained becomes relatively low, indicating that adding more dimensions will not justify the complexity.
Based on this analysis, we decided to take the first two dimensions which explain 61.3% of the cumulative variance of the data, or approximately 60% of the information.
d. Contribution of variables
The analysis of these graphs allows us to say that:
Dimension 1 is influenced by GDP, TA, FI, TP and TCD. High population growth would mean that the population increases, making economic expansion necessary to meet the needs of all. GDP, as an indicator of economic performance, is also very important here, as it reflects the wealth and ability of a country to provide goods and services.
So, countries that experience strong economic growth while maintaining controlled population growth can better meet the basic needs of their population.
At the end of this analysis, we named this dimension: Economic Growth and Capacity to Satisfy Basic Needs
Figure 10: Contribution graph of variables in the formation of dimension 2
Dimension 2 is strongly related to elements that influence famine. Rainfall (PRE) is essential for agriculture, and therefore for food availability. The hunger index (FI) measures the level of malnutrition, which is crucial for assessing famine. A high hunger index indicates problems of access to food resources. The food inflation rate (FIR) also plays an important role, as high inflation in food products can make access to food more difficult for vulnerable populations. The variable population displacement due to natural disasters
(DCN) is also very impactful, because it can cause losses of arable land.
Thus this dimension is essential for agricultural and food policies. Particular attention to these variables can help to solve famine problems.
Following this analysis, we named this dimension: famine and access to resources.
e. Contribution of individuals
· Dimension 1
Figure 11 plots the contribution of different countries to the first dimension of the PCA, indicating which ones have the greatest impact on the variance explained by this dimension.
CPV (Cape Verde), NER (Niger), GHA (Ghana): These three countries show high contributions, which shows that they are major players in training in this dimension.
ML (Mali): With a significant contribution, Mali also positions itself as a key country. This suggests that it has characteristics that closely align with the dynamics presented in Figure 11.
CIV (Ivory Coast), SLE (Sierra Leone), GNB (Guinea-Bissau), BFA (Burkina Faso), MRT (Mauritania)and LBR (Liberia): These countries have moderate contributions. They might have indicators that interact with the first dimensions, but less than the leading countries.
Countries like: SEN (Senegal), BEN (Benin), GMB (Gambia), GIN (Guinea) and TGO (Togo) show very low contributions. This suggests that they are less influential in determining the characteristics that make up dimension 1. These low contributions may indicate that they do not have much in common with the attributes represented in this dimension 1.
The horizontal red line in the graph gives a clear separation of the most and least contributing countries. It represents the average of the contributions to dimension 1.
· Dimension 2
This graph shows the contribution of different countries to the second dimension of the PCA, indicating which ones have the greatest impact on the variance explained by this dimension.
Sierra Leone (SLE),Niger (NER), Guinea (GIN), Mali (ML), Mauritania (MRT) and Liberia (LBR): These countries show high contributions, showing that they are major players in this dimension.
Senegal (SEN)and Cape Verde (CPV): These countries have moderate contributions. They could have indicators that interact with the first dimensions, but less than the leading countries.
Countries like Togo (TGO), Ghana (GHA), Côte d’Ivoire (CIV), Burkina Faso (BFA), Gambia (GMB), GNB (Guinea-Bissau), TGO (Togo) and Benin (BEN) show very low contributions. This suggests that they are less influential in determining the characteristics that make up Dimension2. These low contributions may indicate that they do not have much in common with the attributes represented in this dimension.
The horizontal red line in the graph gives a clear separation of the most and least contributing countries. It represents the average of the contributions to dimension 2.
f. Factorial Plan and Quality of Representation:
This graph represents a principal component analysis (PCA) with two main dimensions, whose arrows represent the variables and the value of cos2 shows us the quality of representations.
The managementThe arrow’s length indicates the direction of the relationship and the length indicates the strength of the variable’s contribution to the dimensions. The longer an arrow, the more significantly the variable is correlated with the dimension considered, and especially very well represented. The shorter an arrow, the more weakly the variable is correlated with the dimension considered, and especially poorly represented.
The angle that is formed by two variables indicates the strength and directions of the correlation that exists between them.Long, close arrows indicate a strong, positive correlation (example: BP and GDP), while long, far apart or opposite arrows indicate strong, negative correlations.
In our case here, to make it easier to understand, we have associated colors to the variables according to the length. Thus, the greener the color, the variable is well correlated and well represented. If this color tends towards blue, then the variable is moderately correlated and represented and if the color is red, the variable is weakly correlated and poorly represented.
The graph in Figure 14 represents a principal component analysis (PCA) with two main dimensions, of which the countries represent the individuals studied and the value of cos2 shows us the quality of representation of these individuals on the factorial level.
Sierra Leone (SLE): Positioned in the upper right quadrant, indicates a strong positive influence in dimension 2.
Niger (NER): On the other hand, it is located in the lower right quadrant, indicating a strong influence on both dimensions. All other countries are in varied positions, reflecting heterogeneous performances.
We have associated colors to facilitate understanding in terms of representation. The more the color tends towards orange, the representation is well done, but the more it tends towards red, the representation is poorly done.
Thus, we can say that countries close to each other on the graph sharing similar characteristics might have similar expectations or performances in the dimensions considered.
This graph is a biplot from a principal component analysis (PCA), combining both the factorial design of individuals (countries) and the correlation circle of variables. There is a relationship between the arrows and the individuals.
Indeed, the closer an individual is to a long arrow, this indicates that this country is strongly affected by this variable. We can give here the example of Sierra Leone (SLE) and the precipitation variable (PRE). Conversely, short arrows can indicate that the variable has a limited influence on the distribution of countries. We can give here the example of Ghana (GHA) and the pesticide use variable (UP).
5.1.2. Classification
The presented dendrogram is a visual representation of a hierarchical clustering analysis. It allows to group countries according to their similar characteristics.
From this graph, three (03) large groups emerge:
The first group consists of Ghana (GHA), Cape Verde (CPV) and Ivory Coast (CIV). This group of individuals represents countries that are superior in terms of development compared to the other countries studied. They particularly have a very high GDP and a slightly low hunger index compared to other countries. These are countries that have been able to balance economic growth and population growth in order to be able to satisfy the basic needs of their populations.
The second group, consisting of countries such as Liberia (LBR), Guinea (GIN), Sierra Leone (SLE), are countries that are halfway between developed and developing countries. These are countries that can switch from one moment to the next into one of the categories.
The third group, consisting of countries such as Senegal (SEN), Mali (MLI), Gambia (GMB), Burkina Faso (BFA), Guinea Bisseau (GNB), Benin (BEN) and Togo (TGO), are developing countries that are faced with recurring problems of famine. These are countries that deserve special attention, and development projects intended for them can focus their interventions on the crucial factors of the occurrence of famine.
5.1.3. Linear Regression
This graph illustrates a simple linear regression, highlighting the relationship between actual values (independent variable) and forecasts (dependent variables).In this specific case, we used 6 independent variables.
The blue line represents the regression model that predicts values
based on actual values.It is determined by the linear regression
equation which can be expressed in the form:
The black dots represent the individual observations used to build the regression model. Each dot indicates how an actual value compares to the prediction generated by the model.The points below and above the line represent deviations between the actual values and the predictions. Evaluating the differences between the actual and predicted values could help us understand the accuracy of the model. The intercept (or constant coefficient) of the linear regression model is the value of the dependent variable (in this case, the predictions) when all the independent variables (the actual values) are equal to zero. This is the reference value of the model.
Ultimately, we can say that the linear regression model is acceptable because it has a coefficient of determination is R²=0.94.5.2. Map results
6. Discussion
With the results we obtained, some variables stood out for their important contributions in the formation of the two dimensions which are:Economic Growth and Ability to Satisfy Basic Needs, Famine and Access to Resources respectively dimension 1 and dimension 2.
The variables that best explain the persistence of famine in West African countries are: the population growth rate (DGR) with a correlation of 0.77, the poverty rate (PR) with a correlation of 0.78, GDP with a correlation of -0.9, the literacy rate (LR) with a correlation of -0.93, precipitation (PR) with a correlation of 0.6. Thus, as authors D. Satterthwaite and C. Tacoli pointed out, “as the population increases, agricultural resources become increasingly limited. This creates additional pressure on arable land and existing food systems, further complicating the fight against hunger and poverty” [7].
The strong interdependence between literacy rates and hunger highlights the key role that education plays in combating hunger. Higher literacy levels are not only linked to better food production capacities and resource management, but are also fundamental to building resilient and self-reliant communities. To maximize the impact on hunger, it is crucial to integrate education initiatives into development strategies aimed at reducing food insecurity in developing countries, particularly in West Africa.
The relationship between population growth rate and the hunger index reveals how poor living conditions can influence households’ decisions about family size. Lower population growth rates in hungry regions can be the result of difficult economic choices, limited access to education and health care, and migration. To reverse this cycle, it is crucial to implement integrated policies that improve both food security and education, in order to promote sustainable and balanced development.
The negative correlation between GDP and the hunger index indicates that economic growth can play a fundamental role in reducing hunger. However, for this relationship to be beneficial, it is crucial to ensure an equitable distribution of wealth and invest in infrastructure essential to the well-being of all segments of the population. In this way, it will be possible to transform economic development into concrete improvements in food security, particularly in areas plagued by poverty and hunger.
The positive correlation between the poverty rate and the hunger index demonstrates that the challenges of poverty and food insecurity are deeply interconnected. To combat this phenomenon, it is essential to put in place integrated policies and programs that simultaneously target poverty and hunger. By addressing these problems holistically, it is possible to reduce hunger and promote sustainable economic development in vulnerable communities. Thus according to the FAO report of 2020“Poverty is a major driver of global hunger, preventing millions of people from accessing sufficient and nutritious food, thereby exacerbating the hunger index, particularly in low-income countries.”[17].
The positive correlation between precipitation and the hunger index highlights the importance of water as a critical resource for food production and nutritional security. While recognizing the challenges related to climate variability and socio-economic conditions, it is essential to develop integrated strategies that maximize the use of precipitation to improve food conditions, strengthen the resilience of food systems, and promote sustainable agricultural practices. Thus, effective water resource management and adapted policies can contribute to reducing the hunger index and improving the living conditions of vulnerable populations.Climate variations, including changes in precipitation, have a direct impact on agricultural production, which increases food insecurity and the hunger index, especially in regions where agriculture is highly dependent on climatic conditions.[18].
As for our linear regression model, we notice a reference value for all variables. For anyone who wants to use the model it is important to take a certain threshold so that the regression can work well.
Proposed solutions:
To maximize impact on famine:
Ø It is crucial to integrate educational initiatives into development strategies aimed at reducing hunger in developing countries, particularly in West Africa;
Ø It is essential to develop integrated strategies that maximize the use of precipitation to improve food conditions, strengthen the resilience of food systems, and promote sustainable agricultural practices;
Ø Have effective water resource management and appropriate policies;
Ø It is essential to put in place integrated policies and programmes that simultaneously target poverty and hunger;
Ø It is crucial to ensure a fair distribution of wealth and to invest in infrastructure essential to the well-being of all sections of the population.
After observing the different results obtained above, we can name the axes to facilitate interpretation.
Axis 1 will characterize the countries that suffer the least from food insecurity (Ghana, Ivory Coast) and axis 2 will characterize the countries that suffer the most from food insecurity (Cape Verde, Niger, Burkina Faso).
The results also reveal that population growth, food production, food losses (losses due to poor storage and preservation of food), gross domestic product and situations of extreme poverty constitute the major causes of food insecurity in West Africa.
The proportionality between agricultural production and population growth must be ensured in order to meet the food needs of the population. However, the availability of food resources is not a solution to the problem of food insecurity because it raises the question of accessibility (economic accessibility).
In West Africa, situations of extreme poverty result more or less from social inequalities and the poor distribution of national wealth. Thus, even when we observe economic growth (increase in gross domestic product), we notice that instead of a decrease (result observed on the regression analysis graph), we observe an increase in the food insecurity index. This description therefore allows us to affirm that of all the causes of food insecurity, economic access remains and continues to be the privilege because food generally represents half and often more of the total budget of the poorest households. Thus, whether food resources are available or not, all people in situations of extreme poverty will suffer from food insecurity.
CONCLUSION
Our study was able to take into account the causes of the persistence of famine in West Africa on the environmental and socio-economic levels. It emerges that the variables GDP, TA, IF, TP and TCD greatly influence the causes of the persistence of famine in West Africa in countries such as Cape Verde, Niger and Ghana while variables such as PRE, IF and TIA are the causes of the persistence of famine in Sierra Leone, Niger (NER), Guinea (GIN) and Liberia (LBR). However, as a perspective, we should consider collecting data on the institutional and political level to better deepen our study.
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