Abstrat

West Africa is a region heavily dependent on agriculture, but it faces growing challenges related to climate change and demographic pressure. This study analyzes the relative impact of climate variables (rainfall, temperature) and economic factors (GDP, population, irrigation) on agricultural yield in 10 countries in the region between 2000 and 2020. Based on a statistical analysis (correlation and multiple linear regression), the results show that climate variables have a limited impact, mitigated by local adaptation strategies such as the adoption of modern irrigation systems (“drip”, “sprinkler”) and the efficient use of water resources. In contrast, economic and technological factors, including GDP, irrigable area, and irrigation methods, play a predominant role in improving agricultural productivity. These results highlight the importance of public and private investments in agriculture to strengthen the resilience of agricultural systems to the impacts of climate change. The study concludes with recommendations aimed at promoting sustainable public policies and technological innovations adapted to the specific needs of the region.

Keywords

Food security Changement climatique Famine

Introduction

Agriculture and food security issues in sub-Saharan Africa represent critical challenges that significantly affect the region’s socio-economic landscape. Despite its rich natural resources and diverse agricultural potential, sub-Saharan Africa faces persistent food insecurity, with over 250 million people currently classified as undernourished.[1][2] This situation is exacerbated by a range of factors, including historical legacies of colonialism, socio-economic disparities, and climate change, which collectively hinder agricultural productivity and access to adequate nutrition.[3][4]

The region’s agricultural sector has struggled to keep pace with global advancements, particularly in the wake of the Green Revolution that transformed food production in other parts of the world. Key impediments to progress include low investment in modern agricultural practices, inadequate infrastructure, and the challenges posed by rapid population growth and urbanization, which shift labor away from traditional farming practices.[5][6] As a result, agricultural yields in sub-Saharan Africa remain significantly lower than those in other regions, leading to increased vulnerability among rural populations who rely on subsistence farming.[7]

Food security in sub-Saharan Africa is further complicated by external pressures such as political instability, economic volatility, and the impacts of climate change, including extreme weather events that disrupt agricultural systems.[8][9] Recent evaluations indicate that climate change could adversely affect up to 56% of agricultural land in the region by 2050, posing a severe threat to food production and nutritional outcomes.[10][11] The Global Hunger Index highlights alarming levels of hunger and malnutrition in countries such as the Central African Republic and Madagascar, underscoring the urgent need for effective and equitable interventions.[12][13]

Addressing these multifaceted challenges necessitates comprehensive policy responses that encompass innovative agricultural practices, enhanced access to resources for smallholder farmers, and investments in sustainable agriculture technologies. Initiatives promoting climate-smart agriculture and agro-ecological practices are crucial for improving productivity and resilience against climate-related shocks, while social protection programs can help vulnerable populations navigate food price fluctuations and localized shortages.[14][15] As the region seeks to build a more food-secure future, the integration of diverse strategies tailored to local contexts will be essential for overcoming the entrenched barriers to agricultural development and food security.[16]

It is imperative to examine the specific factors hindering the achievement of food self-sufficiency in West Africa and to explore hypotheses on the main obstacles that must be overcome to achieve this. Such an analysis is essential for formulating effective strategies to strengthen food security and restore the dignity of West African populations.

Bibliographic review

Topic: “Factors Influencing Food Insecurity in Selected West African Countries”

Problem statement: In a context marked by recurring climate shocks and significant demographic pressure, why do West African countries struggle to ensure their food self-sufficiency?

More specifically:

West Africa, a region heavily dependent on agriculture, is facing increasing challenges due to climate change (variations in rainfall, rising temperatures) and socioeconomic pressures (rapid population growth, rural poverty). In this context, how do climatic factors (rainfall, temperature) compare with other determinants in explaining food insecurity?

What are the economic (GDP, population) and technological (irrigation, water resource management) factors in explaining agricultural yields between 2000 and 2020? In other words, to what extent does climate change actually influence agricultural productivity compared to local adaptation strategies (modern infrastructure, public policies)?

Objective: To assess the relative impact of climatic variables (rainfall, temperature) and economic and technological factors (GDP, population, irrigable area, irrigation methods) on agricultural yield in West Africa between 2000 and 2020.

Main Objective: Assess the relative impact of climatic factors (rainfall, temperature) and socio-economic factors (GDP, population, irrigation) on agricultural production and food security.

Specific Objectives:

Increasing the irrigable area and adopting modern irrigation methods (drip, sprinkler) contribute significantly to improving agricultural yield.

The availability of renewable water and the efficiency of agricultural water use play a crucial role in maintaining and improving agricultural yield, even in the face of declining water

Historical Context

The agricultural landscape in sub-Saharan Africa has been shaped by various historical factors, including colonial legacies, socio-economic transformations, and environmental changes. Following independence, many nations in the region attempted to reform agricultural practices to enhance productivity and food security. However, these efforts often encountered challenges, including limited access to resources and inadequate infrastructure, which have persisted into the present day[1][2].

The Green Revolution, which significantly improved agricultural productivity in Asia and Latin America, has not been as effectively implemented in sub-Saharan Africa. This disparity can be attributed to a range of factors, including the diversity of agro-ecological conditions, socio-economic disparities, and insufficient investment in agricultural technology and infrastructure[2][3]. Furthermore, rapid population growth and urbanization have intensified pressure on agricultural systems, leading to a shift from farm to non-farm employment and altering traditional agricultural practices[4][5].

In recent years, there has been a growing recognition of the need for innovative farming practices tailored to the unique challenges of the region. Programs aimed at improving mechanization access for smallholder farmers and enhancing the adoption of improved crop varieties have been initiated, such as the DeSIRA Integrated Rice-Fish Farming System (IRFFS) funded by the European Union[6][7]. Additionally, projects like the AICCRA, supported by the World Bank, have been established to increase yield and income for farmers in countries like Mali[8][6].

Moreover, the impact of climate change has emerged as a critical concern, exacerbating food insecurity and complicating agricultural practices across sub-Saharan Africa. Studies indicate that climate change is likely to intensify existing challenges, including reduced crop yields and increased vulnerability to food shortages[9][5]. As such, a nuanced approach that integrates sustainable agricultural practices with climate adaptation strategies is crucial for improving food security in the region[3][10].

Current State of Agriculture

Agricultural productivity in Sub-Saharan Africa (SSA) remains critically low and is increasingly lagging behind other regions globally.[11][12]. Factors contributing to this stagnation include low crop productivity, inadequate investment in modern irrigation, weak economic growth, and rising inflation rates coupled with food prices.[13]. Although there have been efforts to enhance agricultural performance, such as the Comprehensive Africa Agriculture Development Programme (CAADP) and the African Peer Review Mechanism (APRM), these initiatives have had varying impacts on agricultural policies and strategies at the country level.[14].

Role of Women in Agriculture

Women’s involvement in agricultural decision-making is particularly significant for enhancing food security and nutritional outcomes. In households where women have greater control, practices such as crop diversification and the management of home gardens tend to flourish, contributing positively to dietary diversity and community health.[15][16]. Studies indicate that regions where women manage agricultural resources often witness improved conservation and sustainability practices, which are vital for long-term agricultural success.[15].

Economic Impact

Boosting agricultural productivity has been shown to stimulate economic growth and alleviate poverty in multiple ways. It raises the income of farming households and increases food availability, thereby enhancing nutritional outcomes across communities.[11]. In smallholder farming systems, diversification of crops and livestock, along with soil and water conservation practices, are essential strategies that can improve overall productivity.[17][18]. Moreover, the linkage between farmers and processors is critical for fostering effective agricultural growth that contributes to poverty reduction and enhances food security.[19].

Challenges and Opportunities

Despite the potential benefits of improved agricultural practices, challenges such as limited access to mechanization and the low extent of farm mechanization persist, significantly hindering agricultural intensification.[8]. Additionally, soil health is crucial for sustainable agriculture, and practices such as conservation agriculture and integrated soil fertility management are critical for maintaining soil productivity and health.[18][20]. Furthermore, public policies and investments in agricultural infrastructure are necessary foundations for achieving sustained improvements in agricultural productivity across the region.[21].

Food Security Challenges

Food security in Sub-Saharan Africa (SSA) faces numerous challenges, exacerbated by a combination of historical and contemporary factors. One of the most significant barriers to accessing adequate food is poverty, which severely limits financial resources and thereby restricts individuals’ ability to afford nutritious food.[22][23] In rural areas, this issue is compounded by an urban-biased distribution system that often overlooks the needs of these populations, leaving millions vulnerable to food insecurity.[24]

Climate change further complicates the landscape of food security in SSA. It is projected to adversely affect up to 56% of agricultural land in the region by 2050, leading to reduced agricultural yields and heightened food insecurity.[25][26] Extreme weather events, such as droughts, floods, and heatwaves, disrupt agricultural practices and exacerbate soil erosion, which in turn compromises food production and availability.[26][10] The ongoing impact of climate change is expected to worsen as greenhouse gas emissions continue to affect environmental conditions negatively.[25]

Moreover, political instability, conflicts, and epidemics have historically besieged the region, creating shocks that disrupt both physical and human factors of production.[27] These conditions have been further intensified by weak governance and the lack of diverse economic activities, which distort resource allocation and hinder effective responses to food security challenges.[27] The COVID-19 pandemic, for example, led to increased uncertainty and a significant reduction in output and productivity across various sectors, causing a decline in aggregate demand that outstripped the initial labor supply reductions.[27]

The Global Hunger Index 2023 highlights the dire situation, with countries such as the Central African Republic and Madagascar showing alarmingly high levels of hunger and malnutrition.[24][28] These conditions indicate that while SSA is the most affected region overall, specific nations also suffer from extremely high rates of undernourishment and food insecurity, necessitating urgent and equitable interventions to address these pressing challenges.[29]

Geographic location of the project

Our project is based on the study of several West African countries in order to obtain a complete regional perspective of the situation of food insecurity in West Africa; there are 12 of these countries as shown in the map below

Keys words definition:

Food security: A condition in which all people, at all times, have physical, social, and economic access to sufficient, safe, and nutritious food to meet their dietary needs and preferences for an active and healthy life. Starvation: An extreme scarcity of food, often accompanied by widespread starvation, malnutrition, and mortality, typically caused by a combination of factors such as drought, conflict, and economic collapse.

Famine: An extreme scarcity of food, often accompanied by widespread starvation, malnutrition, and mortality, typically caused by a combination of factors such as drought, conflict, and economic collapse

Malnutrition: A condition resulting from deficiencies, excesses, or imbalances in the intake of nutrients, including undernutrition (e.g., stunting, wasting) and overnutrition (e.g., obesity).

Context

Food insecurity remains a pervasive and complex challenge for many countries in West Africa. Its root causes are multifaceted, stemming from both environmental constraints and socioeconomic challenges. These factors not only limit food production but also exacerbate poverty and inequality, creating a vicious cycle that undermines the region’s ability to achieve sustainable food security. The consequences of this crisis are far-reaching, affecting health, education, and overall societal development.

Underlying Causes of Food Insecurity

To address food insecurity effectively, it is essential to identify and understand its underlying causes. Key factors include:

Insufficient Agricultural Production

Farmers in West Africa face significant barriers to increasing agricultural output. Traditional farming methods, coupled with unpredictable weather patterns, crop diseases, and limited access to modern agricultural technologies, restrict productivity. As a result, the region struggles to produce enough food to meet the needs of its growing population.

Poverty and Economic Inequality

Widespread poverty and deep-rooted economic disparities prevent millions of people from accessing adequate and nutritious food. Vulnerable populations, including smallholder farmers and rural communities, are disproportionately affected, further entrenching cycles of hunger and malnutrition.

Climatic Variability

Climate change has intensified the frequency and severity of extreme weather events such as droughts, floods, and erratic rainfall. These climatic variations disrupt planting and harvesting cycles, reduce crop yields, and threaten the livelihoods of millions who depend on rain-fed agriculture.

Rapid Population Growth

The rapid increase in population across West Africa places immense pressure on already limited food resources. With more mouths to feed and insufficient corresponding increases in food production, ensuring adequate nutrition for all becomes increasingly challenging.

Conflicts and Political Instability

Armed conflicts and political instability in parts of the region disrupt food supply chains, displace communities, and destroy agricultural infrastructure. These disruptions often lead to acute food shortages, particularly in conflict-affected areas.

Consequences of Food Insecurity

The impacts of food insecurity are profound and multifaceted, affecting individuals, families, and entire societies. Key conséquences inclue: Food insecurity often leads to inadequate dietary intake, resulting in malnutrition. Children are especially vulnerable, with long-term effects on their physical and cognitive development. This undermines their potential to thrive and contribute to society.

High Infant Mortality Rates

Infants and young children are at heightened risk during periods of food insecurity. Malnutrition and diet-related illnesses significantly increase child mortality rates, perpetuating a cycle of loss and hardship for affected families.

Deteriorating Health

Nutritional deficiencies weaken immune systems, making individuals more susceptible to diseases and infections. Poor health outcomes further strain already fragile healthcare systems, creating additional burdens for communities.

Reduced School Attendance

Children suffering from hunger or malnutrition are less likely to attend school regularly. This compromises their education, limits future opportunities, and perpetuates intergenerational poverty.

Persistent Poverty

Food insecurity reinforces cycles of poverty by reducing individuals’ capacity to work, earn income, and invest in their futures. Without sufficient food, people cannot fully participate in economic activities, stifling broader development efforts.

Approaches to Addressing Food Insecurity

Tackling food insecurity requires comprehensive, long-term strategies that address both immediate needs and systemic issues. Potential solutions include:

Promoting Food Education

Raising awareness about balanced diets and efficient food resource management can empower communities to make informed decisions about nutrition and food consumption.

Strengthening Food Safety Awareness

Organizing campaigns to educate the public on safe food practices and locally adapted solutions can help improve food handling, storage, and preparation techniques.

Improving Access to Markets

Investing in agricultural infrastructure—such as roads, storage facilities, and market connectivity—can enable farmers to sell their produce at fair prices, boosting incomes and encouraging higher productivity.

Encouraging Sustainable Agricultural Development

Adopting sustainable farming practices, such as agroecology and climate-smart agriculture, can enhance food production while preserving natural resources. This approach ensures resilience against climatic shocks and reduces environmental degradation.

Enhancing Access to Quality Seeds

Providing farmers with high-quality, locally adapted seeds can significantly improve crop yields and resistance to pests and diseases, contributing to greater food availability.

Materials and methods

Materials used for the project

  • Ourworldindata.org

“Our World in Data (OWID) is a non-profit research and data project that combines rigorous analysis, interactive visualizations, and open data to explore global challenges. The platform covers a wide range of topics, including health, education, poverty, hunger, climate change, and other key areas. Its main objective is to provide reliable, up-to-date, and accessible information on phenomena at a global scale, helping policymakers, researchers, and the general public better understand complex issues. OWID aggregates data from reputable sources such as the United Nations, the World Bank, and the World Health Organization, ensuring accuracy and credibility. The platform is widely recognized for its ability to present complex data in a clear and engaging manner through interactive charts and graphs. For our research, OurWorldInData.org served as the primary source for exploring variables related to Food insecurity

  • RStudio software

RStudio is an open-source integrated development environment (IDE) specifically designed for the R programming language, a powerful tool widely used for statistical analysis, data visualization, and modeling. In this project, RStudio was chosen for its flexibility, extensive library of specialized packages, and strong community support. It enabled us to perform advanced statistical analyses and create clear, informative visualizations of our data. Specifically, we utilized packages such as ggplot2 for data visualization and dplyr for data manipulation These tools were essential in helping us interpret and present our findings effectively.”

  • ZOTERO software

Zotero is an open-source bibliographic management tool designed to help researchers organize, store, and cite references in their work. It enables the creation of a personal database of bibliographic references, which can be easily integrated into research documents and used to generate formatted bibliographies. In this project, Zotero was selected for its user-friendly interface, compatibility with various citation styles (e.g., APA, MLA), and seamless integration with word processors like Microsoft Word and Google Docs. It allowed us to efficiently manage our references, insert citations into our report, and produce a consistent and properly formatted bibliography. This tool significantly streamlined our workflow and ensured the accuracy of our citations throughout the research process.”

  • QGIS software

QGIS is a free and open-source Geographic Information System (GIS) software that enables the creation, analysis, visualization, and management of geographic and spatial data. It supports a wide range of vector data formats, including Shapefiles, ArcInfo coverages, Mapinfo, and GRASS GIS, making it highly versatile for handling diverse datasets. In this project, QGIS was chosen for its user-friendly interface, extensive plugin ecosystem, and compatibility with various data formats. It allowed us to perform advanced spatial analyses, create thematic maps, and visualize geographic data effectively. These capabilities were essential for interpreting spatial patterns and relationships within our dataset.

Data collection tools

The collection and processing of data is necessary to carry out our project. Depending on our study theme and the elements we wanted to highlight, we selected from the available and usable data in the database https://ourworldindata.org/ via the link https://ourworldindata. org/hunger-and-undernourishment ; The data used for our analysis was also extracted from the World Bank https://www.worldbank.org/ . The data downloaded for our theme was sent to Excel to be filtered in order to be able to extract those we needed for our work

To research our data, we consulted the official data site OurWorldInData.com. This site has thematic data concerning different problems in the world. The most recent data for all our variables were taken in the same time interval (the year 2020). They concern the countries in our study area; So we decided to carry out our work in a few countries in West Africa which is our main target. These are: Benin, Burkina Faso, Cape Verde, Ivory Coast, Gambia, Ghana, Liberia, Mali, Nigeria, Senegal, Sierra Leone and Togo.

Regarding bibliographic information, we have consulted a certain number of scientific documents on the theme of our research.

Data collection is the process by which researchers capture the information they need, the purpose of which is to carry out a study. This is a necessary step before conducting a statistical study. Indeed, these data are necessary for the processing of information and its subsequent interpretation.

We chose a questionnaire for collecting our data because information on food insecurity is difficult to find, especially in rural areas; In these areas it is difficult to carry out investigations, especially if there is no access to networks. The questionnaire will allow simpler understanding for these populations; and at the same time it will be more easily accessible for populations in more developed urban areas who will be able to access it directly online. https://ee.kobotoolbox.org/x/CZvV3VDU

Principal component analysis (PCA ). PCA makes it possible to explore connections between variables and similarities between individuals. It also consists of transforming variables linked to each other (called “correlated” in statistics) into new variables separated from each other. These new variables are called principal components or principal axes.

Results and discussions

Thematic maps

Analysis Data collected

Principal component analysis (PCA)

In our study, our dataset on food insecurity in some West African countries includes 12 variables including 2 additional and 12 individuals.

Data visualization

setwd("C:/Users/Yameogo/Desktop/RTI_Final")
donn=read.csv(file="Boo.csv", header = TRUE, sep = ";",
          dec = ",")
donn[,1:14]
##             Pays Année Precipitation Temperature Population EAU_Renouvelable
## 1          Bénin  2000          1024       26.59    7221618          1426.27
## 2          Bénin  2010          1089       26.86    9797488          1051.29
## 3          Bénin  2020           969       26.39   13070170           788.05
## 4   Burkina Faso  2000           622       28.75   11925548          1048.70
## 5   Burkina Faso  2010           726       28.54   16176504           772.73
## 6   Burkina Faso  2020           700       28.36   21478692           581.97
## 7  Cote d'ivoire  2000          1154       25.46   17699006          4341.49
## 8  Cote d'ivoire  2010          1371       25.98   22488068          3416.92
## 9  Cote d'ivoire  2020          1150       26.16   28915452          2657.40
## 10         Ghana  2000          1006       26.31    1963087          1543.00
## 11         Ghana  2010          1166       27.39   25475001          1189.40
## 12         Ghana  2020          1019       26.88   31887801           950.21
## 13 Guinée-Bissau  2000          1351       27.16    1234746         12958.18
## 14 Guinée-Bissau  2010          1366       27.59    1566350         10214.85
## 15 Guinée-Bissau  2020          1779       28.17    2013259          7947.33
## 16          Mali  2000           248       28.51   11559291          5190.63
## 17          Mali  2010           298       30.31   15945201          3762.80
## 18          Mali  2020           277       30.21   21713839          2763.22
## 19         Niger  2000            65       28.40   11509635           304.09
## 20         Niger  2010           114       30.46  165488320           211.50
## 21         Niger  2020            96       29.18   23717614           147.57
## 22       Sénégal  2000           543       28.01    9968263          2588.22
## 23       Sénégal  2010           601       29.18   12635418          2041.88
## 24       Sénégal  2020           688       29.26   16789215          1536.70
## 25          Togo  2000          1242       25.75    5140046          2237.34
## 26          Togo  2010          1319       26.25    6732595          1708.11
## 27          Togo  2020          1133       26.43    8669722          1326.46
## 28       Nigeria  2000          1033       23.97  126382491          1748.66
## 29       Nigeria  2010          1127       25.13  166642887          1326.19
## 30       Nigeria  2020          1001       26.63  213996186          1032.73
##    EAU_Agricole       PIB Rendement_Agri Superficie_Irr
## 1          48.3   3250000       993383.0        901.324
## 2          28.3   9535000      1333435.8    1110660.000
## 3          25.2  15650000      2203105.0    1537675.000
## 4          58.0   2968000      2279246.5    2661349.000
## 5          51.4  10110000      4560546.0    4291496.000
## 6          51.4  17730000      5179104.0    4104826.000
## 7          42.6  16580000      1285904.0     764363.000
## 8          44.8  34940000      1961771.0     864043.000
## 9          51.6   6303000      2817182.0    1291849.000
## 10         66.4   4983000      1710622.4    1306632.000
## 11         72.9  32200000      2906709.2    1602099.000
## 12         73.1  70040000      4636041.0    1923044.000
## 13         82.3    391300       177884.0     162150.000
## 14         75.8    940100       256131.2     148586.000
## 15         75.8   1646000       257733.3     191878.000
## 16         98.2   2961000      2310195.5    2295214.000
## 17         97.9  10690000      5338937.0    3674945.000
## 18         97.9  17470000     10352055.0    6140158.000
## 19         67.3   2420000      2126435.0    7354042.000
## 20         87.5   7851000      5264113.5   10639915.000
## 21         91.0  13750000      5775197.0   10463628.000
## 22         89.9   6013000      1025921.0    1166613.000
## 23         90.7  16130000      1767822.2    1477513.000
## 24         91.3  24530000      3640544.8    1998542.000
## 25         47.2   2107000       740520.0     699716.000
## 26         34.1   4746000      1046866.0     880827.000
## 27         34.1   7400000      1355508.0    1178372.000
## 28         53.5  69170000     21370000.0   18242000.000
## 29         72.9 367000000     24650298.0   16132376.000
## 30         44.2 432000000     29210106.0   18025044.000
##              Irrigation_Effi Indice_alim Pauv_Rurale Prix_alim
## 1                    surface        17.2       53.10    910.50
## 2            goutte à goutte         9.8       54.30   1335.40
## 3  aspersion/goutte à goutte        10.8       12.70   1887.77
## 4                    surface        22.7       79.90    971.20
## 5            goutte à goutte        15.1       52.60   1517.00
## 6  aspersion/goutte à goutte        15.1       25.30   1790.65
## 7                    surface        20.2       30.40   1274.70
## 8            goutte à goutte        16.6       34.40   1651.04
## 9  aspersion/goutte à goutte         8.8        9.70   1723.88
## 10                   surface        14.9       55.00   1280.17
## 11           goutte à goutte         7.0       25.70   1881.70
## 12 aspersion/goutte à goutte         6.0       25.20   2349.09
## 13                   surface        15.7       53.20   1457.85
## 14           goutte à goutte        21.0       66.70   1978.82
## 15 aspersion/goutte à goutte        30.0       26.00   2033.45
## 16                   surface        16.6       57.20   1031.90
## 17           goutte à goutte         6.0       48.20   1699.61
## 18 aspersion/goutte à goutte         4.6       80.50   1918.12
## 19                   surface        23.2       60.20   1214.00
## 20           goutte à goutte        15.6       50.60   2003.10
## 21 aspersion/goutte à goutte        12.0       84.60   2082.01
## 22                   surface        24.4       52.40   1031.90
## 23           goutte à goutte        10.9       41.00   1638.90
## 24 aspersion/goutte à goutte        15.1        9.90   1796.72
## 25                   surface        31.5       53.10    934.78
## 26           goutte à goutte        20.3       58.40   1396.10
## 27 aspersion/goutte à goutte        16.0       12.70   1644.97
## 28                   surface         8.8       47.90   1214.00
## 29           goutte à goutte         9.2       34.09   1821.00
## 30 aspersion/goutte à goutte        15.1       30.90   1930.26
data=read.csv(file="Data.csv", header = TRUE, sep = ";",
          dec = ",")
data[,1:12]
##             Pays Annee Precipitataion Temperature Population Eau_Renou Eau_Agri
## 1   Burkina Faso  2010         682.66        28.6   16526915    801.13     53.6
## 2          Benin  2010        1027.33        26.6   10029759   1088.54     33.9
## 3  Cote d'Ivoire  2010        1225.00        25.9   23034175   3471.94     46.3
## 4          Ghana  2010        1063.67        26.9   19775296   1227.54     70.8
## 5       Guinee_B  2010        1498.67        27.6    1604785  10373.45     78.0
## 6           Mali  2010         274.33        29.7   16406110   3905.55     98.0
## 7          Niger  2010          91.67        29.3   66905190    221.05     81.9
## 8        Nigeria  2010        1053.67        25.2  169007188   1369.19     56.9
## 9        Senegal  2010         610.67        28.8   63498610   2055.60     90.6
## 10          Togo  2010        1231.33        26.1    6847454   1757.30     38.5
##          PIB Rend_agricol Super_culti In_secAli Prix_alim
## 1   10269333    4006298.8   3685890.3     17.60  1426.283
## 2    9478333    1509974.6    883078.8     12.60  1377.900
## 3   19274333    2021619.0    973418.3     15.20  1549.873
## 4   35741000    3084457.5   1610591.7      9.30  1836.987
## 5     992467     230582.9    167538.0     22.23  1823.373
## 6   10373667    6000395.8   4036772.3      9.07  1549.877
## 7    8007000    4388581.8   9485861.7     16.93  1766.370
## 8  289390000   25076801.3  17466473.3     11.03  1655.087
## 9   15557667    2144762.7   1547556.0     16.80  1489.173
## 10   4751000    1047631.3    919638.3     22.60  1325.283
summary(donn)
##      Pays               Année      Precipitation     Temperature   
##  Length:30          Min.   :2000   Min.   :  65.0   Min.   :23.97  
##  Class :character   1st Qu.:2000   1st Qu.: 606.2   1st Qu.:26.33  
##  Mode  :character   Median :2010   Median :1012.5   Median :27.27  
##                     Mean   :2010   Mean   : 875.9   Mean   :27.48  
##                     3rd Qu.:2020   3rd Qu.:1153.0   3rd Qu.:28.53  
##                     Max.   :2020   Max.   :1779.0   Max.   :30.46  
##    Population        EAU_Renouvelable   EAU_Agricole        PIB           
##  Min.   :  1234746   Min.   :  147.6   Min.   :25.20   Min.   :   391300  
##  1st Qu.:  8951664   1st Qu.: 1036.7   1st Qu.:47.48   1st Qu.:  3624000  
##  Median : 14507686   Median : 1539.8   Median :66.85   Median :  9822500  
##  Mean   : 34326784   Mean   : 2627.1   Mean   :64.85   Mean   : 40383480  
##  3rd Qu.: 23410228   3rd Qu.: 2736.8   3rd Qu.:86.20   3rd Qu.: 17665000  
##  Max.   :213996186   Max.   :12958.2   Max.   :98.20   Max.   :432000000  
##  Rendement_Agri     Superficie_Irr     Irrigation_Effi     Indice_alim   
##  Min.   :  177884   Min.   :     901   Length:30          Min.   : 4.60  
##  1st Qu.: 1297787   1st Qu.:  938285   Class :character   1st Qu.:10.05  
##  Median : 2241176   Median : 1569887   Mode  :character   Median :15.10  
##  Mean   : 4951111   Mean   : 4077682                      Mean   :15.34  
##  3rd Qu.: 5043338   3rd Qu.: 4244828                      3rd Qu.:19.45  
##  Max.   :29210106   Max.   :18242000                      Max.   :31.50  
##   Pauv_Rurale      Prix_alim     
##  Min.   : 9.70   Min.   : 910.5  
##  1st Qu.:27.10   1st Qu.:1276.1  
##  Median :49.40   Median :1648.0  
##  Mean   :44.20   Mean   :1580.0  
##  3rd Qu.:54.83   3rd Qu.:1886.3  
##  Max.   :84.60   Max.   :2349.1
var.qt=sapply(data,is.numeric)
data.qt=data[,var.qt]

``

library(corrplot)
## Warning: le package 'corrplot' a été compilé avec la version R 4.4.3
## corrplot 0.95 loaded
library(ggplot2)
donn_rd=scale(data.qt,center = TRUE , scale = TRUE)
matrice=cor(data.qt)
## Warning in cor(data.qt): l'écart type est nul

Correlation between variables

Tableau 1:Matrice de corrélation

The correlation matrix is a matrix grouping together a set of values called correlation coefficient. In fact, these values show the relationship that exists between the variables taken two by two. They vary between -1 and 1 depending on whether the relationship between the two variables is strong or weak. Indeed, there are three types of correlation, namely:

  • Positive Correlation: It allows us to say that two variables evolve in the same direction, that is to say that the increase in one leads to that of the other and vice versa. The closer the value is to +1, the greater the relationship. The most notable values are in red and greater than 0.5

  • Negative correlation: It tends to show that two variables move in opposite directions.

  • This means that an increase in one leads to a decrease in the other and vice versa. The closer the value is to -1, the greater the opposition relationship. Values in purple are more remarkable and less than -0.5

  • Zero correlation: This shows that the increase or decrease in one of the variables has no influence on the others.

In our correlation matrix we can describe the relationship between some variables. For example the variables Popundershmnt and FdAbleCon are negatively correlated. Thus we can say that the more the undernourished population increases, the more the food available for consumption decreases and vice versa.

There is also a negative correlation between the variables Popundershmnt and GDP. Which allows us to say that the more the GDP increases, the more the undernourished population decreases and vice versa.

We also have a positive correlation between the variables ( MinDietRequ and Minico ) which allows us to say the more the Minico increases, the more the Minico increases MinDietRequ increases. To say that the more income we have, the more we are able to eat foods containing the minimum number of calories.

In general, we have as many negative as positive correlations which make it possible to highlight the existing relationship between the variables and therefore to know what food insecurity can be due to, for example, and also the influence of the population on food production.

Study of eigenvalues

This table presents the eigenvalues with their percentage of variance on each axis as well as the cumulative percentage of variance which is equal to 100% at the 9th variable. The number of eigenvalues is equal to 10 because according to PCA the number of eigenvalues must be equal to the number of variables. These eigenvalues also correspond to the variance of the cloud of individuals.

From these eigenvalues we can determine a priori the number of axes that we can retain using the method called Kaiser criterion. So from the Kaiser criterion which says that we must retain the axes associated with eigenvalues greater than 1, then we can retain axes 1, 2 and 3 which bring together 85.46% of the information.

To solidify our choice of the first two axes we can also base ourselves on the elbow or scree rule of eigenvalues.

Figure 9: Décomposition of inertia

The total inertia of the factorial axes indicates on the one hand whether the variables are structured and on the other hand suggests the number of principal components appropriate to study.

So by observing this figure, we see that the first two axes of the analysis express 68.45% of the total inertia of the dataset which means that 68.45% of the total variability of the cloud of individuals or variables is represented in this plan. This is a high percentage, and the foreground therefore well represents the variability contained in a very large part of the active dataset. This value is greater than the reference value of 56.29%, the variability explained by this plan is therefore significant (this reference interval is the 0.95-quantile of the distribution of inertia percentages obtained by simulating 1633 random data sets of dimensions comparable on the basis of a normal distribution).

Due to these observations, we can say that only the first too axes carry real information. Consequently, we will only keep these too axes for the description of the analysis. Thus, we reaffirm our choice of axes based on the Kaizer criterion.

Study of the correlation circle

Figure 10: Correlation Circle

This figure, called the correlation circle, results from the PCA of the variables. It takes into account the correlations of the variables with each other, of the variables with the two axes as well as the quality of representation of the variables. Indeed, the variables whose vectors are close to each other are positively correlated with each other, whereas those whose vectors are opposite are negatively correlated. Furthermore, the closer they are to an axis, the more they are correlated to this axis (positively or negatively). Also, the closer the vector of a variable is to the circumference of the circle the better it is represented, otherwise it is less represented . We have the variables ( Population,Super_culti ) which are well represented and in addition which are positively correlated with each other and also with axis1 because they are close to the latter. We also have the variables (Temperature and EAU_Agri ) which are each correlated to axis 2 because they are close to the latter and also correlated with each other but in a negative way.

Study of the representation of individuals in the Factorial plan

Figure 11: Graph of the représentation of individuals in the factoriel design

The representation of individuals in the factorial plan was done according to the previously chosen axes.

The projection of individuals on to the factorial plane defined by Dim1 and Dim2 makes it possible to obtain this graph bringing together as much information as possible to be visualized on a plane from the initial data. The inertia explained by the factorial plan is 42.16% for Dim1 and 26.32% for Dim2, 68.48% of the information retained. Individuals far from the origin are better represented while those close to the origin are poorly represented. In our case, Nigeria and Cape Verde are better represented compared to other individuals.

Study of variables

By observing this table we can highlight the different variables correlated to the different axes (Dim1 and Dim2) both positively and negatively and thus see if we have a size effect. In our case, we have 6 variables correlated to axis 1 (these are the variables whose data are colored in yellow) and 3 variables correlated to axis 2 (these are the variables whose data are colored in red). In addition, the contribution and the cos2 which shows the contribution that each variable makes and the quality of representation on the axes. However, the contribution and the cos2 of the variables with respect to the dimension evolve in the same direction as that of the variables with respect to the coordinates.

To better observe the variables contributing the most to the different axes, we will use the following graphs:

On this graph, we clearly observe that the variables (Pop, GbleFood , CePro and PopextPvrty ) are those which contribute the most to axis 1.

Figure 13: Contribution of variables to Dim-2

On this graph, we clearly observe that the variables (GDP, PopUndershmnt and FdAbleCon ) are those which contribute the most to axis 2.

Study of Individuals

Tableau 4: Coordonate,Contribution ,quality of representation

By observing this table we can list the different individuals correlated to the axes (Dim1 and Dim2) both positively and negatively. Thus we have 6 individuals correlated to axis 1 (these are the individuals whose data are colored in red) and 6 individuals correlated to axis 2 (these are the variables whose data are colored.

Using this graph, we clearly see the individuals contributing the most to axis 1. These individuals are Nigeria.

Using this graph, we clearly see the individuals contributing the most to axis 2. These individuals are Liberia, Mali, Niger.

Figure 15: Contribution of individual to Dim-1

Figure 16: Contribution of individuals to Dim-2

Based on this graph1 and our scatterplot on the factorial level, we can then say that Nigeria is an atypical individual because it has the greatest contribution among all individuals which means that it is also well represented and has greater data at the variable level.

Hierarchical Ascending Classification (CAH

Classification is a method which aims to group individuals with characteristics or a few characteristics in common. It can be done in a supervised manner (Knowing the number of classes desired we seek to know to which class an individual may belong) and unsupervised ( We do not know in advance the number of classes). In our case we carried out a classification unsupervised (an ascending hierarchical classification).

Class 1 is made up of individuals such as Nigeria characterized by high values for the PIB , Population , Rend_agricol , Super_culti .

Class 2 brings together individuals such as Togo, the Ivory Coast ,Benin and Guinee_Bissau characterized by high values for the variables Precipitation

Class 3 is made up of individuals such as Mali , Niger,Ghana ,Senegal and Burkina-Faso characterized by high values for the variables Temperature , Eau_Agri )

Results and Discussion

Analysis and interpretation of the results obtained

Study of Eigen value

This table presents the eigenvalues with their percentage of variance on each axis as well as the cumulative percentage of variance which is equal to 100% at the 10th variable of our PCA without the atypical element. The number of eigenvalues is equal to 9 because according to PCA the number of eigenvalues must be equal to the number of variables. These eigenvalues also correspond to the variance of the cloud of individuals.

From these eigenvalues we can determine a priori the number of axes that we can retain using the method called Kaiser criterion. So from the Kaiser criterion which says that we must retain the axes associated with eigenvalues greater than 1, then we can retain axes 1, 2 and 3 which bring together 85.43% of the information. However, analysis on 3 axes can be difficult so we can then keep the first three axes which bring together 85.43% of the information for easier analysis.

To reaffirm our choice of the first two axes we can also base ourselves on the elbow rule or Scree of eigenvalues.

The total inertia of the factorial axes indicates on the one hand whether the variables are structured and on the other hand suggests the number of principal components appropriate to studystructured and on the other hand suggests the number of principal components appropriate to study.

So by observing this figure, we see that the first three axes of the analysis express 85.43% of the total inertia of the dataset which means that 85.43% of the total variability of the cloud of individuals or variables is represented in this plan. This is a high percentage, and the foreground therefore well represents the variability contained in a very large part of the active dataset. This value is greater than the reference value of 58.22% , the variability explained by this plan is therefore significant (this reference interval is the 0.95-quantile of the distribution of inertia percentages obtained by simulating 7899 random data sets of comparable dimensions on the basis of a normal distribution). Due to these observations, we can say that only the first two axes carry real information. Therefore , only the first three axes are better for the description of the analysis. Thus, we reaffirm our choice of axes based on the Kaizer criterion .

Study of the correlation circle

Indeed, the variables whose vectors are close to each other are positively correlated with each other, whereas those whose vectors are opposite are negatively correlated. Furthermore, the closer they are to an axis, the more they are correlated to this axis (positively or negatively). Also, the closer the vector of a variable is to the circumference of the circle the better it is represented, otherwise it is less represented . We note in particular that the variable ( Temperature,Precipitation, Eau_Agri ) can be correlated to axis 2. For example, we have the variables ( Population, Super_culti ) which are well represented and in addition which are positively correlated with each other and also with axis1 because they are close to the latter. We also have the variables (GPD , Rend_Agri) which are each correlated to axis 1 because they are close to the latter and also correlated with each other but in a positive way. They are also well represented because their vector is more or less close to the circle.

  • Variables positively correlated with Axis 1: Population and Irrigable Area (Super_culti) are strongly positively correlated with Axis 1. These variables represent countries with extensive natural resources (high irrigable area) and large populations.

These characteristics may reflect countries where agriculture is still traditional, but where agricultural productivity remains limited.

  • Variables negatively correlated with Axis 1:

GDP and Agricultural Yield (AgriYield) are strongly negatively correlated with Axis 1. These variables represent countries with more modern/developed economies and more efficient (high-yield) agriculture.

This contrast suggests that countries with high GDP and high agricultural yield tend to have smaller populations and smaller irrigable areas. So Our PCA analysis clearly shows that food insecurity is influenced by two main types of factors:

  • Socioeconomic factors (population, GDP, agricultural yield) captured by Axis 1.

  • Environmental factors (temperature, precipitation, agricultural water) captured by Axis 2.

We can therefore conclude that:

Demographic impact:

A large population can exacerbate food insecurity if it is not accompanied by improved agricultural yields and effective water resource management.

Role of economic development:

High GDP and efficient agricultural yields significantly reduce food insecurity by enabling greater productivity and better resource distribution.

Climatic conditions as a key factor:

Countries facing high temperatures and low agricultural water availability are particularly vulnerable to food insecurity. These factors must be taken into account in natural resource management policies.

Study of the representation of individuals in the Factorial plan

The PCA Individuals plot shows the projection of West African countries onto the first two principal dimensions (Dim1 and Dim2), which explain 42.16% and 26.32% of the total variance, respectively.

Country Positioning:

Country positions reflect their contributions to the principal axes

Nigeria is to the right on Dim1, indicating a strong positive contribution related to factors such as a large population or a large irrigable area.

Togo is at the bottom on Dim1, showing a negative contribution.

Study of variables without atypical individuals

Hierarchical Ascending Classification (CAH)

Figure22: Cluster Dendrogram

Main Results:

  • Climatic Factors:

Rainfall and temperature have a moderate impact on agricultural production, mitigated by the adoption of modern technologies (drip irrigation, sprinklers). Countries like Mali and Niger, facing harsh climatic conditions, show increased vulnerability.

  • Economic Factors:

GDP and population are strongly correlated with irrigable areas and agricultural yield. Nigeria stands out for its immense agricultural potential but faces specific challenges related to rural poverty.

  • Agricultural Technologies:

The adoption of modern irrigation systems has significantly improved agricultural yields in several countries (e.g., Côte d’Ivoire, Ghana).

Discussion

These results confirm that economic and technological factors play a predominant role in food security. However, climate shocks remain a major challenge for resource-limited countries (e.g., Burkina Faso, Senegal). An integrated approach combining local adaptation and national policies is essential.

These conclusions align with those of several earlier studies that highlight the critical role of agricultural infrastructure and public policies in enhancing the resilience of food systems against climate shocks (FAO, 2022; Nyaku et al., 2022). For instance, the study recalls that since the 1970s, West Africa has faced an increase in climatic and socio-economic shocks, but local food systems have often been hindered by a lack of structural investments. Our analysis reinforces this idea by showing that countries with high GDP and strong agricultural yields, such as Nigeria, are better positioned to address food insecurity.

Moreover, the results indicate that countries facing high temperatures and low agricultural water availability, such as Mali and Niger, are particularly vulnerable. This observation is consistent with the World Bank’s (2022) findings, which emphasize that semi-arid zones in sub-Saharan Africa are the most affected by the combined effects of climate change and rural poverty.

Implications of the Results

The findings have several important implications:

  • Public Policies : Governments must prioritize investments in agricultural infrastructure (irrigation, storage, transportation) to strengthen the resilience of food systems. In particular, countries with large populations but limited agricultural yields, such as Togo and Benin, require increased support to improve their productivity.

  • Water Resource Management : The availability and efficient use of agricultural water resources emerge as crucial factors for maintaining food production, even under unfavorable climatic conditions.

  • Climate Change Adaptation : While local adaptation strategies are useful, they are insufficient to offset the growing impacts of climate variability. An integrated approach combining local adaptation and national policies is necessary.

Limitations of the Study

Despite promising results, this study has certain limitations:

  • limit the representativeness of the results

  • Analysis Period : The study focuses on the period from 2000 to 2020, which may not reflect recent developments or long-term trends.

  • Unquantified Factors : Certain social, cultural, and political factors influencing food security were not included in the analysis, such as armed conflicts or trade policies.

Perspectives

  • This study opens several avenues for future Public Investments:

    • Develop agricultural infrastructure (irrigation, storage).

    • Promote the adoption of modern technologies (drip irrigation, drones).

  • Natural Resource Management :

    • Optimize the use of agricultural water.

    • Encourage sustainable agricultural practices (agroecology).

  • Public Policies:

    • Strengthen safety nets for vulnerable populations.

    • Foster regional cooperation to share best practices.

General Conclusion

Food insecurity in West Africa is a complex challenge influenced by climatic, economic, and social factors. While climate shocks have a moderate impact, they exacerbate existing vulnerabilities. Solutions must be multidimensional, combining local adaptation, public investments, and coordinated regional policies. This project opens important avenues for future research, particularly on the impact of new agricultural technologies and public policies.

Appendix

References:

[1]: [PDF] 2 Soil Fertility Management Organic Africa [PDF] - Worcester Now … [2]: [PDF] What is the evidence on smallholder agriculture interventions in … - 3ie [3]: [PDF] Sustainable Agriculture and Climate Resilience in Sub- Saharan Africa [4]: [PDF] Part 1, chapter 2. Agriculture in Sub-Saharan Africa [5]: Global Agricultural Development - Investing in Transformation and … [6]: Africa Agricultural Policy Leadership Dialogue - World Bank [7]: Sub-Saharan Africa - SDG 6 Data Portal [8]: Research Raises Agricultural Productivity in Sub-Saharan Africa [9]: Climate Change and Chronic Food Insecurity in Sub-Saharan Africa in [10]: Economic and Agriculture Development in Sub-Saharan Africa [11]: [PDF] Resources, Policies, and Agricultural Productivity in Sub-Saharan … [12]: [PDF] The Effectiveness of Development Aid for Agriculture in Sub … [13]: Addressing Food Insecurity in Sub-Saharan Africa - Ipsos [14]: Agricultural policies in Sub-Saharan Africa: understanding CAADP … [15]: [PDF] Agricultural Policies in Sub-Saharan Africa [16]: Agricultural productivity and policies in Sub-Saharan Africa - CGSpace [17]: Building resilience in Africa’s smallholder farming systems [18]: Soil health and ecosystem services: Lessons from sub-Sahara Africa … [19]: Publication: Agriculture for Development in Sub-Saharan Africa[20]: Climate-Smart Agriculture in Sub-Saharan Africa [21]: Prioritizing Productivity in Sub-Saharan Africa - GAP Initiative [22]: Beyond hunger: Unveiling the rights to food in sub Saharan Africa [23]: Why food insecurity persists in sub-Saharan Africa: A review of … [24]: Enhancing Food and Nutrition Security in the Sahel and Horn of Africa [25]: Agriculture and economic reform in Sub-Saharan Africa (English) [26]: Agricultural productivity must improve in sub-Saharan Africa - Science [27]: [PDF] Africa - Regional Overview of Food Security and Nutrition 2023 [28]: Food security: Regional solutions key to solving Africa’s challenges [29]: How to end hunger in sub-Saharan Africa: fight inequality, gender …

Questionnaire for data collection

https://ee.kobotoolbox.org/x/CZvV3VDU