# Trois Essais sur les Effets de l'Expansion des Terres Agricoles sur la Soutenabilité de l'Agriculture en Afrique Sub-saharienne

Longang Gamo Saubaber

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Longang Gamo Saubaber. Trois Essais sur les Effets de l'Expansion des Terres Agricoles sur la Soutenabilité de l'Agriculture en Afrique Sub-saharienne. Agriculture, économie et politique. Université de Dschang (Cameroun), 2023. Français. NNT: . tel-04481316

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REPUBLIQUE DU CAMEROUN
Paix- Travail- Patrie

UNIVERSITE DE DSCHANG
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ECOLE DOCTORALE
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REPUBLIC OF CAMEROON
Peace- Work- Fatherland

UNIVERSITY OF DSCHANG
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POST GRADUATE SCHOOL
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DSCHANG SCHOOL OF ECONOMICS AND MANAGEMENT
Laboratoire de recherche en Economie Fondamentale et Appliquée (LAREFA)

# Three Essays on the Effects of Agricultural Land Expansion on Agricultural Sustainability in Sub-Saharan Africa

*Thesis submitted in partial fulfilment of the requirements for the Award of a Doctor of Philosophy Degree (PhD) in Economics*

Department: Economic Policy Analysis

Option: Mathematical Economics

By

LONGANG GAMO Saubaber

CM04-09SEG0615

MSc in Mathematical Economics

Master in Agribusiness Management

DIPET II Economics

## Members of the jury

### President

NEMBOT NDEFFO Luc

Professor

University of Dschang

### Director

NINGAYE Paul

Associate Professor

University of Dschang

### Examinators

FON Dorothy ENGWALI

Professor

University of Dschang

MOLUA Ernest LYTIA

Associate Professor

The University of Bamenda

KAMDEM Cyrille Bergaly

Associate Professor

University of Yaoundé II - Soa

May 2023
*The University of Dschang does not intend to give any approval or disapproval of the opinions expressed in this thesis. These opinions are to be considered as solely those of the author.*
Table de matières

<table><tr><td>Table de matières</td><td>iii</td></tr><tr><td>Dedication</td><td>iv</td></tr><tr><td>Acknowledgements</td><td>v</td></tr><tr><td>List of abbreviations</td><td>vi</td></tr><tr><td>List of tables</td><td>viii</td></tr><tr><td>List of figures</td><td>ix</td></tr><tr><td>Abstract</td><td>x</td></tr><tr><td>Résumé</td><td>xi</td></tr><tr><td>GENERAL INTRODUCTION</td><td>1</td></tr><tr><td>ESSAY I FOOD SECURITY EFFECTS OF AGRICULTURAL LAND EXPANSION IN SUB-SAHARAN AFRICA</td><td>20</td></tr><tr><td>1. Introduction</td><td>21</td></tr><tr><td>2. Stylised facts on the state of food security in sub-Saharan Africa</td><td>22</td></tr><tr><td>3. Literature review relating agricultural expansion to food security</td><td>26</td></tr><tr><td>4. Methodology of analysis of the effects agricultural expansion on food security</td><td>37</td></tr><tr><td>5. Results and Discussion</td><td>47</td></tr><tr><td>6. Conclusion</td><td>66</td></tr><tr><td>ESSAY II EFFECTS OF AGRICULTURAL LAND EXPANSION ON STRUCTURAL CHANGE IN SUB-SAHARAN AFRICA</td><td>68</td></tr><tr><td>1. Introduction</td><td>69</td></tr><tr><td>2. Stylised facts on the structural change process in sub-Saharan Africa: A Markov Chain Analysis</td><td>70</td></tr><tr><td>3. Literature review linking agricultural land expansion and structural change</td><td>73</td></tr><tr><td>4. Methodology of analysis of the effects of land expansion on structural change</td><td>88</td></tr><tr><td>5. Results and Discussions</td><td>98</td></tr><tr><td>6. Conclusion</td><td>123</td></tr><tr><td>ESSAY III EFFECTS OF AGRICULTURAL LAND EXPANSION ON BIODIVERSITY LOSS IN SUB-SAHARAN AFRICA</td><td>124</td></tr><tr><td>1. Introduction</td><td>125</td></tr><tr><td>2. Stylised Facts on the Biodiversity status in Africa</td><td>126</td></tr><tr><td>3. Literature review linking agricultural land and biodiversity loss</td><td>129</td></tr><tr><td>4. Methodology of analysis of the effects of agricultural expansion on biodiversity loss</td><td>144</td></tr><tr><td>5. Results and discussion</td><td>152</td></tr><tr><td>6. Conclusion</td><td>162</td></tr><tr><td>GENERAL CONCLUSION</td><td>164</td></tr><tr><td>Bibliography</td><td>169</td></tr><tr><td>Annexes</td><td>190</td></tr><tr><td>Table of contents</td><td>201</td></tr></table>
Dedication

*My progeny Nayah Tsira, Samuel Ahmès, Amarah Céleste and*
*Constant of blessed memory.*

*This is the opportunity cost of my absence.*
## Acknowledgements

I thank the Almighty God for the flow of blessings he has bequeathed on me.

In particular, I am grateful to Professor Ningaye Paul my research mentor, for the rigorous, humble and generous guidance he has given me through my research journey. You taught me systematically how to question each idea and answer.

To Pr. Wandji Georges, all the previous deans and the academic staff of the Faculty of Economics Science and Management of the University of Dschang, that made my academic journey a wonderful and rich experience.

To Dr Muluh Georges for the useful help while designing the research project and all the academic support, you supplied when required.

To Pr. Fon Dorothy and Pr. Jaza who guided my foot steps in agricultural economics.

To my academic elders and friends, Drs Kaffo Hervé, Tanga Achille, Abba Barnabé, Soh Boris and Severin Tamwo for the pertinent comments and orientations all the busts at each of our meets.

To my colleagues and chiefs Mme Mesame Amabo Mary, Mr Ako Philip, Mr Bande Justin, Mme Mme Motue Charlene, Mme Fofuleng Misparine for the hospitality when writing the document, and the proofreading.

To my mother Kamo Titcho Celestine, for your patience and all your sacrifices. To my aunt Kepyep Iritha for the hospitality in Banyo and constant support.

To my beloved wife Longang Yollande, my partner for life and best friend. For your noticed patience, love and support.

To my Dad Mr. Kamo Gamo Ruben and Mr. Tchenneboin Vincent, 'my Dschang dad' all of blessed memory, who hosted and provided me moral support during this academic itinerary. Thanks for your encouragements.

To whoever omitted, find here my deep gratitude.
List of abbreviations

<table><tr><td>ACET</td><td>African Conference for Economic Transformation</td></tr><tr><td>AFS</td><td>Agri-Food Systems</td></tr><tr><td>AGRA</td><td>Alliance for a Green Revolution in Africa</td></tr><tr><td>AU</td><td>African Union</td></tr><tr><td>BII</td><td>Biodiversity Intactness Index</td></tr><tr><td>CBD</td><td>Convention on Biological Diversity</td></tr><tr><td>DGMM</td><td>Difference Generalised Method of moments</td></tr><tr><td>EBV</td><td>Essential Biodiversity Variables</td></tr><tr><td>ECOWAS</td><td>Economic Community of West African States</td></tr><tr><td>EKC</td><td>Environmental Kuznets Curve</td></tr><tr><td>ELD</td><td>Economics of Land Degradation</td></tr><tr><td>EMT</td><td>The environmental sociology ecological modernisation theory</td></tr><tr><td>FAO</td><td>Food and Agriculture Organisation</td></tr><tr><td>FDI</td><td>Foreign Direct Investments</td></tr><tr><td>FEMLOGIT</td><td>Fixed Effect Multinomial Logit</td></tr><tr><td>FIES</td><td>Food Insecurity Experience Scale</td></tr><tr><td>FSIN</td><td>Food Security Information Network</td></tr><tr><td>GDP</td><td>Gross Domestic Product</td></tr><tr><td>GLS</td><td>General Least Squares</td></tr><tr><td>GNI</td><td>Gross National Income</td></tr><tr><td>GVCs</td><td>Global Value Chains</td></tr><tr><td>HLPE</td><td>High Level Panel of Experts on Food Security and Nutrition</td></tr><tr><td>IFAD</td><td>International Fund for Agricultural Research</td></tr><tr><td>IFPRI</td><td>International Food Policy Research Institute</td></tr><tr><td>IIA</td><td>Independent or Irrelevant Alternative</td></tr><tr><td>ILO</td><td>International Labour Organisation</td></tr><tr><td>IPS</td><td>Im Pesaran and Shin</td></tr><tr><td>IUCN</td><td>International Union for Conservation of Nature</td></tr><tr><td>LLC</td><td>Levin Lin and Chu</td></tr><tr><td>LPG</td><td>Labour Productivity Growth</td></tr><tr><td>LPI</td><td>Living Planet Index</td></tr><tr><td>LSLA</td><td>Large-Scale Land Acquisition</td></tr><tr><td>MDG</td><td>Millennium Development Goals</td></tr><tr><td>MEA</td><td>Millennium Ecosystem Assessment</td></tr><tr><td>MLI</td><td>Modified Lilien Index</td></tr><tr><td>MNL</td><td>Multinomial Logit</td></tr><tr><td>NAV</td><td>Norm Absolute Value</td></tr><tr><td>NCAER</td><td>National Council of Applied Economic Research</td></tr><tr><td>NEPAD</td><td>New Partnership for African Development</td></tr><tr><td>OECD</td><td>Organisation for Economic Cooperation and Development</td></tr><tr><td>OLS</td><td>Ordinary Least Squares</td></tr><tr><td>PA</td><td>Population Average</td></tr><tr><td>PCA</td><td>Principal Component Analysis</td></tr><tr><td>PFGLS</td><td>Pooled Feasible General Least Squares</td></tr><tr><td>POMLOGIT</td><td>Pooled Multinomial Logit</td></tr></table>
**REMLOGIT** Multinomial Random-Effects Logit

**RLI** Red List Index

**SDGs** Sustainable Development Goals

**SEM** Structural Equation Modelling

**SGMM** System Generalised Method of moments

**SSA** Sub-Saharan Africa

**TSC** Type of Structural Change

**UN** United Nations

**UNCBD** United Nations Convention on Biological Diversity

**UNCCD** United Nations Convention to Combat Desertification

**UNDP** United Nations Development Program

**UNEP** United Nations Environment Program

**UNICEF** *Fonds des Nations Unies pour l'Enfance*

**WCMC** World Conservation Monitoring Centre

**WCED** World Commission on Environment and Development

**WDI** World Development Indicators

**WDPA** World Database of Protected Areas

**WFP** World Food Program

**WHO** World Health Organisation

**WWF** World Wild Fund
List of tables

<table><tr><td>Table 1: Expected signs of variables explaining undernourishment</td><td>45</td></tr><tr><td>Table 2: Descriptive statistics</td><td>48</td></tr><tr><td>Table 3: Stationarity results</td><td>49</td></tr><tr><td>Table 4: Effects of agricultural land expansion on undernourishment, FE and RE</td><td>52</td></tr><tr><td>Table 5: Hausman Test</td><td>53</td></tr><tr><td>Table 6: Effects of agricultural land expansion on undernourishment, D-GMM and S-GMM</td><td>55</td></tr><tr><td>Table 7: Effects of agricultural land expansion on undernourishment with 5 years average</td><td>61</td></tr><tr><td>Table 8: Alternative measures of agricultural land expansion and undernourishment</td><td>63</td></tr><tr><td>Table 9: Effects of agricultural land expansion on water and sanitation (overall)</td><td>64</td></tr><tr><td>Table 10: Sensitivity analysis</td><td>66</td></tr><tr><td>Table 11: Variables and expected type of structural transformation</td><td>96</td></tr><tr><td>Table 12: Descriptive statistics of the overall sample</td><td>98</td></tr><tr><td>Table 13: Summary statistics of the dependent variable</td><td>99</td></tr><tr><td>Table 14: Unit root test</td><td>105</td></tr><tr><td>Table 15: Results of the baseline model</td><td>107</td></tr><tr><td>Table 16: Effects of agricultural land expansion on stages of structural change: Random effects</td><td>116</td></tr><tr><td>Table 17: Effects of agricultural land expansion on stage of structural change: Fixed-effects</td><td>121</td></tr><tr><td>Table 18: Variables and expected signs to threats to mammals</td><td>150</td></tr><tr><td>Table 19: Descriptive statistics</td><td>152</td></tr><tr><td>Table 20: Agricultural land and biodiversity loss</td><td>156</td></tr><tr><td>Table 21: Agricultural land and biodiversity loss, robustness check</td><td>161</td></tr><tr><td>Table 22: Agricultural expansion and biodiversity loss, alternative measure of land expansion</td><td>162</td></tr><tr><td>Table 23: Full description of variables</td><td>192</td></tr><tr><td>Table 24: Principal components/correlation: governance index</td><td>193</td></tr><tr><td>Table 25: Principal component/correlation: production index</td><td>193</td></tr><tr><td>Table 26: Agricultural land and type of structural change - POMLOGIT</td><td>194</td></tr></table>
List of figures

<table><tr><td>Figure 1: Conceptual model</td><td>12</td></tr><tr><td>Figure 2: Trends in agricultural land in Africa countries</td><td>13</td></tr><tr><td>Figure 3: Prevalence of undernourished people in the world and Africa, and the number of undernourished in Africa</td><td>23</td></tr><tr><td>Figure 4: Evolution of the Prevalence of undernourishment, for countries</td><td>24</td></tr><tr><td>Figure 5: Share of population in moderate or severe food insecurity</td><td>25</td></tr><tr><td>Figure 6: Food price vulnerability-built index</td><td>46</td></tr><tr><td>Figure 7: Undernourishment prevalence against land</td><td>50</td></tr><tr><td>Figure 8: Undernourishment prevalence, within variation</td><td>51</td></tr><tr><td>Figure 9: Transition diagram of structural change, All countries</td><td>70</td></tr><tr><td>Figure 10: Transition diagram of structural change, sea cost countries</td><td>71</td></tr><tr><td>Figure 11: Transition diagram of structural change, landlocked countries</td><td>72</td></tr><tr><td>Figure 12: Sectoral value-added share and agricultural land</td><td>101</td></tr><tr><td>Figure 13: Agricultural land vs industry and services spillovers, overall</td><td>103</td></tr><tr><td>Figure 14: Agricultural land vs industrial and services spillovers, within variation</td><td>104</td></tr><tr><td>Figure 15: Agricultural land vs industrial and services spillovers, between</td><td>105</td></tr><tr><td>Figure 16: Marginal effects of agricultural land expansion on probability of structurally underdeveloped economy</td><td>112</td></tr><tr><td>Figure 17: Marginal effects of agricultural land expansion on probability of structurally developing economy</td><td>113</td></tr><tr><td>Figure 18: Marginal effects of agricultural land expansion on probability of structurally developed economy</td><td>114</td></tr><tr><td>Figure 19: Marginal effects of Governance on types of structural change</td><td>115</td></tr><tr><td>Figure 20: Margins after GSEM, random effects</td><td>120</td></tr><tr><td>Figure 21: Redlist Index</td><td>126</td></tr><tr><td>Figure 22: Redlist index for groups of species</td><td>127</td></tr><tr><td>Figure 23: Model for land rent for natural habitats: Case of deforestation</td><td>134</td></tr><tr><td>Figure 24: Example of land displacement mechanism causing indirect deforestation</td><td>135</td></tr><tr><td>Figure 25: Agricultural land expansion and endemic species</td><td>153</td></tr><tr><td>Figure 26: Agricultural land expansion and total species</td><td>154</td></tr><tr><td>Figure 27: The egg of sustainability</td><td>190</td></tr><tr><td>Figure 28: The prism of sustainability</td><td>190</td></tr><tr><td>Figure 28: Evolution of agricultural land in Africa countries</td><td>193</td></tr><tr><td>Figure 29: Agricultural land and sectoral value added</td><td>194</td></tr></table>
The aim of this thesis is to analyse the effects of agricultural land expansion on agricultural sustainability in sub-Saharan Africa. Though it is a prominent feature of these countries, its effects on agricultural sustainability have not been examined yet enough. Increasing agricultural land is hypothetically linked with social responsiveness like food security, economic aspect like structural change and environmental aspect such as biodiversity. This thesis shows in three essays that, agricultural land expansion can influence the prevalence of undernourishment, promote virtuous structural change and cause mammal species extinction. A panel analysis is conducted over 1990–2018 for 41 sub-Saharan African countries. The first essay uses the Generalised Method of Moments (GMM), the second essay employs a Pooled Multinomial Logit (POMLOGIT) model and the third, a pooled – Averaged Model. In all essays of the thesis, the three respective research hypotheses are rejected. The results disclose that agricultural land expansion influences three dimensions of agricultural sustainability in sub-Saharan Africa: Firstly, an increase of agricultural land by 1% reduces the prevalence of undernourishment by 0.23%, and helps to curb food insecurity. Secondly, it increases the probability of experiencing industrial-oriented structural transformation by 1.312, but by 1.089 services-oriented structural change. It promotes favourable transfer of agricultural labour towards the industrial sector. However, it causes serious damages to biodiversity. It is found that an expansion of agricultural land by 1% significantly increases the risk of extinction of endangered species, especially endemic mammals, by 0.0543. Therefore, the valuable benefits of agricultural land expansion must be weighed against the negative environmental impacts of converting forests and other natural habitat for agricultural purposes, increased carbon emissions, loss of ecosystem services and biodiversity, risk of disease, and impacts on local livelihoods and customs. As a policy recommendation, states should improve and promote access to land for small and large farmers in high potential agricultural and low wildlife density areas, further support intensive and organic agriculture and increase the number of protected areas.

**Keywords:** Agricultural land expansion, food security, structural change, biodiversity loss, sub-Saharan Africa.
Résumé

L'objectif de cette thèse est d'analyser les effets de l'expansion des terres agricoles sur la durabilité de l'agriculture en Afrique subsaharienne. Bien que l'expansion des terres agricoles soit une caractéristique importante des pays d'Afrique subsaharienne, ses effets sur les piliers social, économique et environnemental de la durabilité de l'agriculture, mesurés par la sécurité alimentaire, le changement structurel et la biodiversité respectivement, n'ont pas été suffisamment examinés. Cette thèse s'organise en trois essais et montre que, l'expansion des terres agricoles peut influencer la prévalence de la sous-alimentation, promouvoir un changement structurel vertueux et provoquer l'extinction d'espèces mammifères. Une analyse de panel est menée sur la période 1990-2018 pour 41 pays d'Afrique sub-saharienne. Le premier essai utilise la méthode des Moment Moments Généralisés (GMM), le deuxième essai utilise un modèle *pooled multinomial logit* (POMLOGIT) et le troisième, un *pooled – Averaged Model*. Les trois hypothèses de recherche de la thèse sont rejetées. Les résultats révèlent que l'expansion des terres agricoles influence trois dimensions de la durabilité agricole en Afrique sub-saharienne : Premièrement, une augmentation des terres agricoles de 1% réduit la prévalence de la sous-alimentation de 0,23%, et contribue à freiner l'insécurité alimentaire. Deuxièmement, elle augmente la probabilité de connaître une transformation structurelle orientée vers l'industrie de 1,312, mais seulement de 1,089 vers les services. En effet, elle favorise le transfert de la main-d'œuvre agricole vers le secteur industriel. Cependant, elle cause de sérieux dommages à la biodiversité. On constate qu'une expansion des terres agricoles de 1% augmente significativement de 0,0543 le risque d'extinction des espèces menacées, en particulier des mammifères endémiques. Par conséquent, les précieux avantages de l'expansion des terres agricoles doivent être mis en balance avec les impacts environnementaux négatifs de la conversion des forêts et d'autres habitats naturels à des fins agricoles, l'augmentation des émissions de carbone, la perte de services écosystémiques et de biodiversité, le risque de maladie et les impacts sur les moyens de subsistance et les coutumes locales. En guise de recommandation politique, les États devraient améliorer et promouvoir l'accès à la terre pour les petits et grands agriculteurs dans les zones à fort potentiel agricole et à faible densité de faune sauvage, soutenir davantage l'agriculture intensive et biologique et augmenter le nombre d'aires protégées.

**Mots clés** : Expansion des terres agricoles, sécurité alimentaire, changement structurel, perte de biodiversité, Afrique subsaharienne.
# GENERAL INTRODUCTION

## 1.1. Context of the study

The role that land plays in agricultural production is relevant to any study of agriculture and development. Economists have always considered land as a key factor in solving countries' 'food problem'. Though it helps to support the productivity of human efforts and agricultural growth (Schultz, 1951), it also has a key role in development challenges (Johnson and Vollrath, 2020). Moreover, 'land is foundational to entrepreneurship, capital accumulation and wealth formation; therefore, the long-run prosperity of society depends on how well we manage this resource whose use is not always reversible' (Barlowe et al., 2014).

In economic thought, there are major controversies on the effects of agricultural land expansion. It is the only wedge of economic progress and the base of state organisation (Boulding, 1992; Cantillon, 1755; Quesnay, 1758), responsible for economic growth (Barbier, 2020; Ricardo, 1817; A. Smith, 1776), economic rent (James Anderson, 1777; D. Ricardo, 1817; A. Smith, 1776; Steuart, 1767; Turgot, 1793), but can cause damages to nature (Kuznets, 1955; J. S. Mill, 1848; Perrings and Halkos, 2015), is a factor of little importance that can be substituted by other factors (Patinkin, 1973).

Firstly, the two pre-classical schools have contrary views. Both mercantilism and physiocracy recognise the importance of agricultural land but at different stages. Land is of equal importance to work for mercantilism (Cantillon, 1755; Daniel, 2010). According to this school, the bases for economic prosperity are population, extractive industries and growth of agricultural output and land. In feudalism, land is at the core of the feudal order. It constitutes the basis of the military, judicial, administrative, and political system (Polanyi, 1957).

Physiocrats, however, attribute their economic surplus and agricultural product to agricultural land. It is a reservoir of wealth comprised of abundant and diversified food from farming activities (Boulding, 1992). The essential means through which a nation can derive agricultural surpluses (Quesnay, 1758), and improve on the living standards of her citizens. Meaning that agricultural land ensures food production and stabilises the food chain hence, conquering new lands for agriculture means progress.

Classical economists consider two additional factors namely capital and labour, but still attribute an important role to agricultural land, as a factor of production. Expanding land for cultivation means increasing both output and wealth (A. Smith, 1776; Steuart, 1776, Turgot, 1793), and can also cause
changes in the natural environment (J. S. Mill, 1848). Though, in that school there is not unanimity concerning the returns to scale of land and the effects of agricultural land expansion.

For instance, agricultural land is considered as the principal source of revenue and wealth of every country and agricultural land is assumed to have increasing returns (A. Smith, 1776). Conversely, land is said to have decreasing returns called either extensive margin (Steuart, 1776) or intensive margin (Turgot, 1793). The extensive margin of Steuart holds that as population increases, it leads to the use of poorer and poorer soils, and productive efforts then produce smaller and smaller returns. On the other hand, Turgot's intensive margin states that as equal amounts of capital or labour are successively applied to a given piece of land, the level of output derived from each application will first increase towards its maximum, and then decrease, and finally converge towards zero (Schumpeter, 1981).

Another critical divergence under the classical school of thought concerns competition with the natural environment. Theoretically, land expansion for agriculture appears to conflict with natural beauty. The prosperity functions of land in providing food and precious materials are opposed to nature's amenity services. Taking up additional hectares of land does damage to the natural beauty, natural habitats, and the pleasures of the diversity of both animal and plant species (J. S. Mill, 1848). However, the impact of agriculture on biodiversity depends on the extent and types of agriculture and the degree to which agricultural land contrasts with the natural ecosystem (Wilson et al., 2020).

Neoclassical economists posit substitutability between human-made and natural capital and refute the importance of agricultural land expansion. This led to a production function of the form: $Y=f(K)$. Therefore, land was found to be a valueless factor of production and agricultural land must be developed and maintained, just like any other capital asset (Patinkin, 1973).

Although it has been efficient and useful for allocating land resources in the short run, the neoclassical economic approach to land has received several criticisms. For instance, the inadequate treatment of aspects such as irreversibility, non-substitutability of production factors, and the pervasiveness of external effects, means neglecting the unique services land provides, which are not traded in markets (Hubacek and van den Bergh, 2006).

Agricultural economics is the original farm management tradition, developed to help farmers gain a satisfactory income from their land (Fox, 1987). Land economics differs from agricultural economics in that it retains the classical view of the uniqueness of land resources, and stresses the factor land, rather than its management (Hubacek and van den Bergh, 2006; Renne, 1947).
As land suitable for agriculture is not constant over time (Hubacek and van der Bergh, 2006) and farming land often creates negative externalities (Abler et al., 1999), one of the early research questions in land economics is how optimally bring unused lands into agricultural production. With a seeming abundance of agricultural land, the interest shifted to the more general concern about situations in which land, its use, or its limitations was of strategic importance. Salter (1942) focused on changes in major land uses and their effects on social well-being. A subject that has received little attention until date (Maehle et al., 2020; Opp, 2017).

After years of treating land as a secondary resource that could be subsumed under other types of capital, economic theorists recognise that land expansion for agriculture can have effects on outcomes such as food production, labour spillovers, better living conditions for rural populations and biodiversity change. Land and agricultural economists guide government regulations in finding ‘more appropriate balances between public and private ownership’ (Gnedenko, 2020) that favour agricultural sustainability.

This study's conceptual plan contains several approaches and definitions of the main notions. The concepts are agricultural land expansion, sustainability of agriculture, food security, structural change and biodiversity. Therefore, it is essential to discuss and clarify the concepts and links between agricultural land and agricultural sustainability.

Agricultural land expansion refers to the increase in agricultural land. In general, ‘agricultural land’ is the land resource upon which agriculture takes place for the production of food and fibre, including croplands, pastures, orchards, groves, vineyards, nurseries, ornamental horticultural areas, confined feeding operations, and other use applications (Qi, 2014). Lyuri and Lands (2008) consider that agricultural lands consist of three main types: arable land (including cropland and fallows), land under permanent crops, and pastures and hayfields.

The analysis of the concept of agricultural land expansion split the empirical literature into two while considering its sources. The first wave points out the intranational view of agricultural land expansion. Here, authors (Barbier, 2020, 2021; FAOStat, 2013; Keating et al., 2014) consider the Ricardian sense of agricultural land expansion. It refers to an increase of the extensive margin of farmland, which comes from the household and the farmer. It is also termed as extensive growth of agriculture (Perrings and Halkos, 2015), frontier expansion or extensification (Dawson et al., 2016; Wilson et al., 2020), land use change (Mora et al., 2020) or simply agricultural expansion (Laurance et al., 2014). The terms agricultural land expansion and agricultural expansion are used interchangeably in this thesis.
However, a second wave emphasises on the foreign origins of agricultural land expansion. Authors refer to this as agricultural land acquisition or large-scale land acquisitions (Mechiche-Alami et al., 2021; Santangelo, 2018), land use cover change (Degife, 2020), international land deals for agriculture (Nolte et al., 2016) or large-scale agricultural investments (LSAIs) (Nolte and Ostermeier, 2017). In that case, the sources of expansion are foreign countries through land deals or acquisitions by foreign countries or foreign direct investments. This analysis differs from this conception because it focuses on endogenous land expansion, which has not been thoroughly explored. Using the Ricardian sense of agricultural land expansion, the study seeks to analyse the global effects of land expansion on agricultural sustainability.

The expression agricultural sustainability, incorporates two important terms: sustainability and agriculture. In order to understand sustainable agriculture, a clarification of the concept of sustainability is needed. Sustainability means meeting the needs of the present without compromising that of the future generations (Chandan and Das, 2017; Mota and Scott, 2014; White, 2013). The understanding of the sustainability concept varies according to authors perception, who represent different models<fnref n="1" />. Three models<fnref n="2" /> of sustainability are presented here: the egg of sustainability, the prism of sustainability and the three pillars basic model.

Firstly, the ‘Egg of Sustainability’. Also known as the dependency model, which was designed in 1994 by the International Union for the Conservation of Nature, IUCN (Guijt and Moiseev, 2001). It demonstrates the relationship between people and ecosystems as one circle inside another. This traduces the reciprocal links between people and ecosystems. People are within the natural environment, and depends entirely on other. Social and economic development can only take place if the environment offers the necessary resources: raw materials, space for new production sites and jobs, constitutional qualities (recreation, health, etc.). The ecosystem is therefore to be regarded as a super-coordinated system to the other dimensions of the triangle or prism models: social, economic, and institutional. This model is not used in the study as it does not assess the links between one dimension and the other. The study, however, aims to investigate how land expansion phenomenon affects the three dimensions (Herath and Rathnayake, 2019).

Secondly, the prism of sustainability. This is a four-dimension model. It is developed by the German Wuppertal Institute and defines sustainable development using the following components: economy, environment, society, and institutions. There are a number of inter-related dimensions in this model,

<footnote n="1">¹ For a more detailed description of models of sustainable development see Herath and Rathnayake, (2019)</footnote>

<footnote n="2">² For convenience reasons, figures of the two first models are presented in the appendix, in figures 26 and 27 respectively.</footnote>
including care, access, democracy, and eco-efficiency that can lead to policy changes. The main critic of this prism theory argues that ‘the economic dimension tends to include assets emanating from all four dimensions, thus, adding confusion to the description and analysis’ (Keiner, 2005). Moreover, how agricultural land expansion affects institutions outreaches the framework of this study, making the theory unsuitable for this study.

Finally, the three pillars basic model or the three interlocking circles model. This model is among the most widely known models in the field, using three dimensions: economy, environment and society as dimensions. The figure shows three interlocking circles with the triangle of environmental (conservation), economic (growth), and social (equity) dimensions. Sustainability is modelled on these three pillars. This model is based on considering the society, but does not explicitly take into account ‘human quality of life’ (Hamedani, 2014). The second problem associated with the model is that it does not incorporate a time dimension which is a core component of the World Commission on Environment and Development (WCED, 1987) definition (Thatcher, 2014; Thatcher and Yeow, 2016). The study overcomes the first limit by using an indicator of the social dimension which is the least developed (Maehle et al., 2020; Murphy, 2012) and the second limit by using time-series data, to evaluate the effects of agricultural land expansion on sustainability of agriculture.

Sustainable agriculture is not an arbitrary concept. Any attempt to seek a single correct universal definition fruitless (Oberč and Schnell, 2020; Siebrecht, 2020). There is a plurality of definitions, which stem from both the epistemological divergences of the concept of sustainable development, and from the different normative or positive principles, underlying the concept of sustainable agriculture. The definition of sustainable agriculture can be adjusted to the respective contexts (Trigo et al., 2021), even though there are three main standpoints by which authors understand sustainable agriculture: On the one hand, it is interpreted as an approach or a philosophy motivated by an awareness of the negative impacts of agricultural activities. On the other hand, it is perceived as a set of strategies that should be applied to enhance resilience (Hansen, 1996; Thompson, 2007; Trigo et al., 2021). Lastly, agricultural sustainability is understood both as a philosophy and system of farming.

Firstly, the farming system approach or the ‘goal-prescribing approach’ (Siebrecht, 2020) or ‘mean oriented’ (Binder et al., 2013; Deytieux et al., 2016; Silva and Marta-Costa, 2013). Goal-prescribing concepts interpret sustainability as an ideological or management approach. It stresses the adaptability, resilience or reproducibility of agricultural inputs and results. Here, certain types of management, measures, or prescribed techniques are defined as sustainable in general. As long as these requirements or standards are met, sustainability is assumed (Siebrecht, 2020).
Some definitions falling under this category consider sustainable agriculture as agriculture that is based on viable, liveable, transferable and reproducible farms (Landais, 1998). A system capable of maintaining its productivity when subjected to severe stress or disturbance (Conway, 1985), to last into the future (Hansen and Jones, 1996), without excessive degradation of other ecosystems (Dover and Talbot, 1987; Edwards, 1987), where the resources used for production are conserved in a way that is more or less self-sustaining and competent to be part of a process of continuous improvement beyond conventional approaches (Rodale, 1988), and which favours dependence on chemical inputs (Reganold et al., 1990; Zahm et al., 2015).

The second approach is ideological and considers agricultural sustainability as a system-describing approach (Siebrecht, 2020). It supports the concept of sustainable agriculture that aligns with sustainable development. From this view, agriculture fulfils different or conflicting goals, over time ('goal-oriented'). Thus, this understanding of sustainability is related to the 'competing objectives' view of sustainability, where the focus is on 'balancing social, economic, and ecological goals' (Siebrecht, 2020, Hansen, 1996; Farrell and Hart, 1998).

From this viewpoint, Francis (1990) and MacRae et al. (1990) define sustainable agriculture as a philosophy based on human goals and values that reflect a state of empowerment, responsibility, and awareness of ecological and social issues. This philosophy guides the application to create resource-efficient agriculture and equitable farming systems. In this case, sustainability is an ideal state, and the more goals an agricultural system can achieve, the more sustainable it will be (Siebrecht, 2020).

From the literature, two subcategories of definitions are identified: The first subcategory gathers authors who define agricultural sustainability in relation to two dimensions of sustainable development: on the one hand, the social and environmental dimensions (CGIAR/TAC, 1988; MacRae et al., 1989), on the other hand, the environmental and economic dimensions (Godard and Hubert, 2002).

The second subcategory comprises authors who perceive the philosophy of agricultural sustainability from three dimensions: It considers sustainable agriculture as ecologically sound, economically viable, socially just and humane agriculture (Zhang et al., 2021), which over the long term, improves the quality of the environment and the resources on which it depends, provides basic food and fibre needs for people, improves the quality of life for farmers and the rest of society (American Society of Agronomy, 1989), ensuring an equitable supply between and within countries (Bonny, 1994; Brodhag, 2000; Otchia, 2014; Yang and Zhu, 2013; Zhang and Diao, 2020).
The third approach integrates the previous two approaches. Here, sustainable agriculture is both farming practices and philosophy<fnref n="3" />. It comprises a set of agricultural practices, backed by strong principles, philosophical and theoretical standpoints. It involves many concepts like agroecology, permaculture, ecological, and sustainable intensification.

The first and third approaches are hinged on operations, agricultural practices, management and sciences at farm level; the practices of plant growing, animal rearing and input management are at the forefront of these approaches. Our study does not analyse farming practices and farm management at the individual producer level. Nevertheless, this thesis is to investigate on the effects of agricultural land expansion on sustainable agriculture, as the latter could easily be measured in a macroeconomic analysis.

Thus, the philosophical definition of agricultural sustainability is more relevant to the study. Indeed, it incorporates the three dimensions of sustainability: social responsibility (human health, nourishment), economic viability (development, thriving economy...), ecological soundness (ecosystem function conservation, natural resource conservation) (Velten et al.; 2015; Runowsky, 1999; American Society of Agronomy, 1988; Hardwood, 1990). Hence, sustainable agriculture is a commitment to satisfy human food and fibre needs and to enhance the quality of life for farmers and society as a whole, now and into the future (Bird and Ikerd, 1993).

This thesis assesses the effects of agricultural land expansion on agricultural sustainability in sub-Saharan Africa. To achieve this aim, the thesis will examine how land expansion for agriculture affects the social responsibility, economic viability, and ecological soundness of agriculture in the region.

Many indicators are used to capture the social, economic, and environmental dimensions of agriculture in the literature. To start with, the social dimension of agricultural sustainability. This dimension tends to address issues related to social justice and social inclusion, such as better education and health (Halisçelik and Soytas, 2019). At the micro level, it focuses on aspects such as the level of education, training, basic needs, and cultural values. However, at the macro level, it focuses on aspects such as the distribution of income and assets, achieving full and productive employment and decent work for all, with equal pay, eradicating forced labor and human trafficking,

<footnote n="3">³ Agroecology, nature-inclusive agriculture, permaculture, biodynamic agriculture, organic farming, conservation agriculture, regenerative agriculture, carbon farming, climate-smart agriculture, high nature value farming, low external input agriculture, circular agriculture, ecological intensification, sustainable intensification. Definitions of these concepts, and how they are linked to sustainability, are sourced from (Oberč and Arroyo Schnell, 2020), though it is not always easy to know what each term refers to - whether it is an approach, a practice, a set of related practices, an activity or tool, etc... For more details see Oberč and Arroyo Schnell (2020).</footnote>
and political inclusion of all, food security (Maehle et al., 2020). Sub-Saharan African countries host one-third (282 million) of the world's undernourished, and the highest level (66.2%) of food insecurity according to the moderate and severe food insecurity (Sachs et al., 2021). Due to these poor statistics, the study tries to shed more light on the relationship between agricultural land expansion and food security. There are similar studies (Kinda and Badolo, 2019; Mechiche-Alami et al., 2021; Mughal and Sers, 2020; Santangelo, 2018) carried out in this domain.

Looking at the economic dimension of agricultural sustainability, broadly, it focuses on maintaining
economic growth and encompasses high income levels (Halisçelik and Soytas 2019). Many indicators
are found under the economic dimensions of agricultural sustainability such as eradicating poverty,
productivity of small-scale farmers, women's access to ownership over land and financial services,
higher levels of agricultural productivity (Maehle et al., 2020).

African states are known for experiencing fragile economic growth, due to labor moving toward the service sector (CUA and OECD, 2018). As structural change and economic growth are twins (Gabardo et al., 2017), it appears obvious that an effective structural change can make nations being more prosperous, while a bad structural change leads to a reduction in national income (McMillan et al., 2014). Few studies (Barbier 2020; 2021) show that agricultural land expansion promotes economic growth and few other studies (Baymul and Sen, 2020; Mensah et al., 2016) relate agricultural land to structural change. Hence, structural change is used to capture this economic dimension.

Thirdly, the environmental dimension of agricultural sustainability is based on the notion of
ecosystem services. Ecosystem services are both renewable and non-renewable resources and waste
absorptive capacity that provide benefits to humans and thus improve their welfare (Moldan et al.,
2012). Environmental sustainability involves maintaining these services and, consequently, living
within the limitations of the biophysical environment. Environmental sustainability involves
maintaining these services and, consequently, living within the limitations of the biophysical
environment. Some targets falling under the environmental dimension of agricultural sustainability
include ensuring resilient agricultural practices that help maintain ecosystems and strengthen capacity
for adaptation to climate change; Increasing the share of renewable energy; protecting and
safeguarding the world's cultural and natural heritage; preventing or reducing pollution or depletion
of natural resources (Maehle et al., 2020).

The rate of biodiversity loss is critical in sub-Saharan African (SSA) countries. Indeed, known as the
first reservoir of biodiversity, forest losses operate at the rate of 4 million ha/year (FAO and UNEP,
2020). Moreover, 80% of deforestation originates from agricultural expansion. In addition, the
decline of species is alarming, with a decrease of 65% in population sizes of mammals, fish, amphibians and reptiles. On top of that, 50% of Africa's bird and mammal species could disappear by 2100 (Almond et al., 2020). Few studies (Balima et al., 2020; Perrings and Halkos, 2015; Tan, Chen, et al., 2022; Ullah and Kim, 2021) investigate the relationship between agricultural land expansion and biodiversity change. The study seeks to contribute to this literature.

The concept of food security has several definitions. Looking at the dimensions they take into account, the first definitions emphasise explicitly on the access dimensions: food security for a household means access by all members at all times to enough food for an active, healthy life (Reutlinger, 1986); The second considers production dimensions and stability as well: the amount of food in a country or area through all forms of domestic production, imports, food stocks and food aid (World Food Programme, 2006). Other approaches to food security emphasise accessibility more than availability. Food must be available at the immediate neighbourhood of all households. It is a basic human condition that exists when all people, at all times, are free from hunger (Drèze and Sen, 1991), or have access to sufficient, safe and nutritious food to meet their dietary needs and food preferences for an active and healthy life (Pérez-Escamilla, 2017; Pinstrup-Andersen, 2009).

More elaborated approaches to food security enumerate five essential components: availability of food, its access, the utilisation of food, the sustainability of the food system providing these components and its stability (Timmer, 2017b) or the notorious five principles – universality, stability, dignity, quantity and quality – of McKeown (2006). Concerning the dimensions of food security, the access dimension fits with the study. Two reasons can be given. First, access to economic resources constitutes one of the key components of the social dimension of sustainability (Maehle et al., 2020). Thus, for agricultural sustainability, access to food should be prioritised. Second, while farm productivity in Africa is high, more than one third representing 282 million people of the world's undernourished is found in SSA (Sachs et al., 2021).

On the other hand, food security can be defined at three levels: individual and household level, national and global levels (Berry et al., 2015; World Food Programme, 2006). In a household, food security refers to the existence of adequate sources of food and the ability to acquire them to maintain an adequate nutritional intake for an active and healthy lifestyle (World Food Programme, 2006).

According to Smith et al. (2013) is the nation's ability to ensure uninterrupted arrangements of food supply so that the dimensions of food security are accessible to all citizens in both normal and emergency situations if any. Global food security is the assurance of an adequate food supply or access for all humans on earth. It essentially means equal distribution of food among the nations
irrespective of their economic status, regional disparities and sociocultural variations in a sustainable
and eco-friendly manner (Mishenin et al., 2021)

In this study, we adopted the national view of food security that fits better with the analysis of sub-Saharan African countries. In summary, this work considers only the access dimension of food security at the national level. This is captured at national levels by undernourishment prevalence. That it provides a deeper and more coherent analysis of food security (Soriano and Garrido, 2016). If food security is ensured, it may lead to structural change as labour force will leave the agricultural sector (Timmer, 2017a).

The notion of structural change is not different from structural transformation in the present study. The classical sense of structural transformation as defined by (Lewis, 1954) and Kuznets (1955) refers to the movement of population and economic activity from low productivity to the more productive industry sector (Kanbur, 2017) and then from industry to services (Herrendorf et al., 2013). It is refers as the reallocation or natural rearrangement of economic activity across the three main sectors that eases the process of modern economic growth (Bloem, 2019; Gabardo et al., 2017; Herrendorf et al., 2013).

Countries do not follow the same types of structural changes (Bah, 2011). While developing countries may experience a 'bypassing' (Cadot et al., 2015) or growth reducing (McMillan et al., 2014) structural change, where labour moves from high to low productivity sectors, others may have a classical or growth enhancing structural change, where labour moves in the opposite direction and contributes positively to overall productivity growth.

Labour-oriented definitions consider structural change as the movement of labour from agriculture to either industry or service sectors (Bloem, 2019) or the movement of workers from low productivity to high productivity activities and sectors (Baymul and Sen, 2020). When agricultural labour migrates toward a sector of higher productivity, it is called virtuous structural change. Meanwhile, when agricultural labour moves toward a sector of lower productivity, it is termed as vicious structural change with dynamic losses (de Vries et al., 2015). Because the scope of the work is in sub-Saharan Africa, the labour oriented definition of Bloem (2019) is adopted as it fits well with the African context. The economic growth through structural change can provoke biodiversity loss.

The definition of biodiversity given by the Convention on Biological Diversity in 1992 is enounced as follows: 'Biological diversity means the variability among living organisms from all sources, including inter alia, terrestrial, marine and other aquatic ecosystems and the ecological complexes of which they are part; these include diversity within species, between species, and of ecosystems'.
Strictly speaking the word biodiversity refers to the quality, range or extent of differences between biological entities in a given set. In total it would thus be the diversity of all life and its characteristics (S. R. Singh, 2022).

Literature identifies three types of diversity, from micro to macro: genetic diversity, species diversity, and ecosystem diversity (Dasgupta, 2021; Iritie, 2015; Kontoleon et al., 2007; S. R. Singh, 2022). Firstly, genetic diversity refers to the information contained in the genes of individual plants, animals, and microorganisms. Species diversity is the diversity of species within which gene flow occurs under natural conditions. No two individuals of the same species are exactly alike. For instance, humans have a lot of genetic diversity between themselves. People living in different areas show a high level of difference. Genetic diversity is related to the diversity of genetic information stored in each species (Dasgupta, 2021; Kontoleon et al., 2007; Singh, 2022). Secondly, species diversity is related to the richness and abundance of species. It is the biodiversity seen within a community. It signifies the number and distribution of species. The number of species in an area varies broadly according to the environmental surroundings or ecosystems (Iritié, 2015; Kontoleon et al., 2008). Generally, the greater the species richness, the greater is the species diversity (Singh, 2022). The third type of biodiversity is ecosystem diversity. This type of biodiversity is related to the variability of ecosystems. It refers to the diversity seen between the ecosystems in a region. Several ecosystems, such as rainforests, deserts, mangroves, etc., show a vast diversity of life forms living in them (Iritié, 2015; Kontoleon et al., 2008).

A second diverging conception of biodiversity measures is based on spatial scales (Whittaker, 1975). Three terms for measuring biodiversity are formulated: alpha, beta, and gamma diversity. Firstly, alpha diversity or within habitat diversity. It characterises diversity within a particular habitat or ecosystem. It is expressed as the species' number per unit area. Secondly, beta diversity or between-habitat diversity, referring to species diversity between two habitats or ecosystems when compared. Thirdly, gamma or regional diversity known as the total species diversity in a landscape.

However, this work focuses on African states. Alpha, beta, and gamma diversity do not coincide sharply with state boundaries. Moreover, due to the lack of spatial data, this spatial scale description is not in line with our research. In biodiversity economic literature, it is difficult to capture genetic and ecosystem biodiversity because of their complexity. However, biodiversity of species receives more attention (Balima et al., 2020; Dasgupta, 2021; Dietz and Adger, 2003; McPherson and Nieswiadomy, 2005; Perrings and Halkos, 2015; Tan, Chen, et al., 2022). Indeed, species richness, or the number of species present in per unit area is the simplest measure of species diversity (Singh,
2022). This indicator allows to capture biodiversity change, and to evaluate the effects of agricultural land expansion on the reduction of wildlife.

Although agricultural land expansion has many causes, it also has links to the three dimensions of agricultural sustainability. Figure 1 illustrates how land expansion is connected to agricultural sustainability dimensions.

<figure><img src="image_3.png" /><figcaption>Figure 1: Conceptual model
Source: Author</figcaption></figure>

Building such a conceptual model has a double aim. First, it helps to have a better representation and understanding of the structure and completeness of the study itself. The expansion of agricultural land may influence the continent's ability to deal with its food production problems. Dealing with food security is a good cause of structural transformation, as it may propel farmers in other sectors as workers. However, it will also cause changes in the natural habitat of many species and biodiversity. Thus, land expansion has important social, economic, and environmental effects in nations (Lanz et al., 2018) and the achievement of one objective may affect that of the others. This reveals the need for authorities to balance different interests of agricultural sustainability (Williams et al., 2021), because economic growth alone, for instance, cannot be considered as a success, unless it leads to a more equal income distribution (Halisçelik and Soytas, 2019) or exceeds the planet's ecological limits (Dasgupta, 2021).

The interactions between agricultural land and the three dimensions of sustainable agriculture make it possible to assess the effect of the former on these important components. The analysis of these effects would constitute an important key for the establishment of new integrated and optimal policies, as well as the understanding of this factually increasing phenomenon.
In the empirical literature, agricultural growth comes either from intensification or extensification; intensifying the use of the existing cultivated area and increasing the extensive margin, that is, the expansion of agricultural land (Barbier, 2020; Keating et al., 2014; FAOStat, 2013).

<figure><img src="image_4.png" /><figcaption>Figure 2: Trends in agricultural land in Africa countries</figcaption></figure>

The stylized facts about land expansion show that agricultural land expansion is an actual phenomenon in Sub-Saharan African (SSA) developing countries. The total area of agricultural lands in the world is 4,973.4 million hectares (ha). They cover 33.3% of terrestrial surface, including 10.3% of arable land and land under permanent crops and 23% of pastures and hayfields. However, SSA is the most land abundant region of the world with roughly 60% of the world's uncultivated arable land (African Center for Economic Transformation (ACET), 2017). In 1961-70, the surface of arable land and land under permanent crops was estimated at 136.66 million hectares (mHa) and increased to 194 mHa in 2001-08, representing a 42% increase (Fuglie and Rada, 2013; Lyuri, 2008).

Similarly, the crop area shares of total area harvested rose by 89%, from 92.73 to 175.08 million ha. It also represents an annual increase of 1.76 million ha over the same period. More specifically, land under permanent crops increased by almost 67%, moving from 12.58 mHa in 1961-70 to 20.95 mHa in the 2001-08 decade. Looking at land under permanent pasture, it shows a slower increase of only 6% within the same period, which translates to a growth of 0.89 mHa each year. In SSA, between 1961 and 2010, arable land and permanent crops rose by almost 60%, from about 144 to 230 million hectares, permanent meadows and pastures went from 801 million to 827 million hectares; forest areas decreased by 10%, from 741 million hectares in 1990 to 666 million hectares in 2010 (Fuglie and Rada, 2013).

Despite a general positive trend (figure 2), stylized facts show some heterogeneities in countries' agricultural land expansion. As a matter of fact, in 2007, countries like Burkina Faso, The Gambia,
and Malawi, Sierra Leone in 2012, and Lesotho in 2015 have recorded an increase of more than 5 percentage points of agricultural land. Since 2014, Lesotho, Nigeria, and South Africa have 75 percent of their land area occupied by agricultural land. However, countries like Cameroon, Gabon, and Liberia have less than 30% of their total land area used for agricultural purposes (WDI, 2021).

Although demand for new agricultural land among most low- and middle-income countries shows little sign of debilitation, agricultural land remains highly demanded. Over the period 1970–2016, agricultural land area in low- and middle-income countries rose by 16% and by 15% in Sub-Saharan Africa (Barbier, 2021). After independence, country development policies through agricultural green revolutions have contributed to the expansion and increase in agricultural output in Africa (Dawson et al., 2016). This was in conformity with the policies of the African Union and NEPAD. The latter have included access to land, equitable distribution, security and tenure, cultivation of new varieties, irrigation, and sustainable intensification in their policies (AGRA, 2013).

Today, the conflict between Russia and Ukraine is causing the world to experience unprecedented food crisis. This poses major threats to developing countries, challenges agricultural sustainability, and calls for more solidarity between nations. To withstand the crisis at least in the short run, it is necessary to expand agricultural land to increase food and cereal production in sub-Saharan African countries (Macron, 2022).

Although non-exhaustive, other factors identified as causes of land expansion include institutional factors like population dynamics and government policy (Hye and Khan, 2010; Jellason et al., 2021), ill-defined property rights and open access conditions (Barbier, 2004a; Barbier and Burgess, 2001; Juniyanti et al., 2021; Kaimowitz and Angelsen, 1998), more secure rights over land (Besley, 1995; Bohn and Deacon, 2000; Feder and Feeny, 1991; Meyfroidt et al., 2022), and tenure insecurity (Kontoleon et al., 2007; Mosisa and Asefa, 2022). In addition to the above, poor intensification, low agricultural performance, low input utilisation, food and cash crop production (Jellason et al., 2021; Keating et al., 2014), agricultural prices, fertiliser use, irrigation and technology (Hye and Khan, 2010; A. P. Singh and Narayanan, 2013), change in diets and adoption of occidental rich animal products (van Dooren et al., 2014), inadequate consumption levels of animal products (Mora et al., 2020), urban expansion (Jiang et al., 2013), quick exhaustion of soil (Frankema, 2014) and absence of terrorism (Adelaja and George, 2019) are other causes of land expansion.

In SSA, expanding agricultural land was meant to meet food security objectives. In 1960, 15% of the agricultural land was used for crop cultivation. It rose to 22% in 2010, with the majority pertaining to livestock. Agricultural production rose in absolute terms, but mainly because growing numbers of
smallholders brought more land under cultivation (Dawson et al., 2016; Mugera and Ojede, 2014; Otsuka and Larson, 2012).

As the population is rising, food security and hunger indicators worsen in Africa. The number of undernourished people was estimated at 217.8 million in 2014–2016 above the 175.7 million in 1990–1992 (Madzivhandila et al., in AGRA 2016). Similarly, the number of people at risk of hunger is expected to increase from about 250 million to 280 million people between 2010 and 2025 (Barrett, 2013). Statistics from the UNICEF website suggest that up to six million children will be affected by life-threatening severe acute malnutrition in West and Central Africa in 2021.

Therefore, Sub-Saharan African countries are expected to require anywhere from 0.9 to 1.35 million km² of new cropland by 2030, and will also need new land for biofuel crops, grazing pasture and industrial forestry, and also to replace land lost to degradation (Lambin and Meyfroidt, 2011; Laurance et al., 2014; UNCCD, 2017). Despite these facts, continued land area expansion for the next decades may not seem problematic (Barbier, 2020; OECD and FAO, 2016).

Agricultural land expansion followed by food production and undernourishment is paradoxical. The purpose therefore is to understand the effects of agricultural land expansion on undernourishment. Thus, understanding how the use of this abundant natural resource influences food security, structural transformation and biodiversity preservation is crucial.

The expansion of agricultural land may influence the continent's ability to deal with food production problems Furthermore, food security makes structural transformation possible since it could propel many farmers into other sectors. However, it may also cause changes in the natural habitat of many species and biodiversity. Throughout the developing world, cultivated land area is expected to increase above 47% by 2050, with about 66% of the new land coming from the depletion of natural habitats for terrestrial species (Barbier, 2004a; Fischer and Heilig, 1997).

**1.2. Problem statement and contributions**

Agricultural land expansion is expected to continue rising because of low production yields in SSA. Both from the theoretical and empirical view, there are numerous contradictions about the plausible effects of agricultural land expansion on agricultural sustainability.

In a threefold perspective, this work focuses on the repercussions of agricultural land expansion in sub-Saharan countries on food security, structural change and biodiversity change: Firstly, as African countries still have high rates of undernourishment (Sachs et al., 2021), it is interesting to evaluate the effects of land expansion on the former. Secondly, from an optimistic perspective and based on
theoretical reasoning (Schultz, 1951), agricultural land expansion is expected to impact the type of structural change of sub-Saharan countries. Thirdly, sub-Saharan Africa's abundant biodiversity makes agricultural land expansion risky for species, especially terrestrial mammals, birds and amphibians.

Firstly, between 2016 and 2017, the percentage of undernourishment in SSA remained at 23%, the highest among developing regions (FAO, 2021b). Also, the absolute number of undernourished people rose from 44 million in 1990-92 to 218 million in 2014-16 (OECD and FAO, 2016). This trend of food insecurity results from low agricultural productivity, climate change (Kinda and Badolo, 2019; Kogo et al., 2021), political instability and civil strife (Barnett, 2003; Schleifer and Sun, 2020).

Literature on agricultural land expansion and food security in Africa is relatively scanty, especially from a macro perspective. Recent studies analysing the links between land expansion and food security reveal that up to 300–550 million people in developing countries could be fed by crops grown on acquired land (Rulli and D'Odorico, 2014). Land expansion for crop cultivation in the region is negatively correlated with food insecurity (Keating et al., 2014). Access to agricultural land plays a significant role in ensuring adequate nutritional attainment in both rural and urban areas (Anríquez et al., 2013). In South Asia, Mughal and Sers (2020) have shown that a 1% increase in cereal production and yield is associated with up to 0.84% decrease in undernourishment.

Nevertheless, it is not certain that agricultural land expansion reduces undernourishment, as food availability may not always lead to its access nor use (Zabala, 2018), neither in Africa (Ziem Bonye et al., 2021) nor in Mexico (Galeana-Pizaña et al., 2018). It is noteworthy that most of those studies do not consider the role of agricultural land on undernourishment in sub-Saharan Africa from a macroeconomic perspective and are mostly focused on a diversified sample of developing countries. The present study seeks to close this gap as no study sheds light on this relationship in sub-Saharan Africa.

Secondly, food insecurity in Africa can justify the large share of employments in agriculture. Indeed, until countries can enough food, labour is trapped in agriculture (Schultz, 1964; Yang and Zhu, 2013). This is comforted by two facts: almost all the actors of the agricultural sector suffer from poverty and poor yields (Barrett, 2013). These are obvious evidence that agriculture has a subsistence purpose for SSA countries, which confirms that agriculture has the highest proportion of employment.

In SSA, agricultural employment remains high, despite rapid decline. More than 60% of workers were engaged in the agricultural sector in 1991, but in 2016 up to 53% of workers are still in the sector (WDI, 2021). In addition, the reduction in agriculture shares in GDP and employment has not
been followed by an expansion of the manufacturing sector as expected (Diao et al., 2017), causing
a structural change ‘bypassing’ the secondary sector (Cadot et al., 2015). The informal tertiary sector
has little evidence of driving sustained growth (Mensah et al., 2018).

Therefore, increasing agricultural land can enhance structural change considered as the movements
of labour force out of the agriculture sector. Recent economic literature reveals that agricultural land
expansion effects are not homogeneous. Some of its advantages include that it supports long-term
economic growth (Barbier, 2020; 2021). While economic growth and structural change are
companions (Gabardo et al., 2017), land elasticity may explain structural transformation (Johnson
and Vollrath, 2020). Conversely, agricultural land expansion could increase youths' involvement in
agriculture, making economies be more agrarian (Yeboah et al., 2019) or reducing industrial
development (Rozaki, 2020). Literature is silent on the relationship between agricultural land
expansion and structural change in sub-Saharan Africa, and this work intends to fill that gap.

Thirdly, species declines in SSA are due to increasing land use for agriculture. This increases loss of
habitats and threatens wildlife species (Perrings and Halkos, 2015), alters tropical old-growth forests,
woodlands, and semi-arid environments (Fenta et al., 2020; Laurance et al., 2014). The Millennium
Ecosystem Assessment concluded that the main driver of the process is the extensive growth of
agriculture, caused by population-driven demand for food, fuels and fibres in poor countries
(Halisçelik and Soytas, 2019).

Many studies find that biodiversity loss has several drivers such as climate change and global
warming (Newbold, 2018), ongoing expansion, emergence and integration of markets and states
(Kontoleon et al., 2008), logging is the biggest threats to species on the IUCN Red List (Maxwell et
al., 2016), economic growth (Iritie, 2015; Sol, 2019), infrastructure expansion (Laurance et al., 2014),
institutional quality (Muchapondwa and Stage, 2015; 't Sas-Rolfes, 2017) and community governed
protected areas (Ullah and Kim, 2021) and agricultural expansion (Perrings and Halkos, 2015).

No recent study establishes the link between agricultural land expansion and fauna biodiversity loss
in Sub-Saharan Africa. In this context, African countries have achieved remarkable progress in
protected area designation for terrestrial and inland water areas since 2015 (UNEP-WCMC and
IUCN, 2018). Although a recent study concludes that habitat change caused by land expansion threats
significantly fauna biodiversity in South and Southeast Asia (Tan, Chen, et al., 2022), closer studies
show that agricultural land reduces plant biodiversity in west Africa (Balima et al., 2020) and sources
animal biodiversity extinction (Perrings and Halkos, 2015). However, they use country-level
approaches and ignore the temporal dimension in their analysis, which this study does using a more
recent database and appropriate methodology.
In this study, the triple imperatives of agricultural sustainability which are food security, structural change and preservation of the environment or biodiversity require stepping beyond conventional and traditional conversationalist approaches. If nature conservation is essential (Dasgupta, 2021), particularly in certain very sensitive areas in terms of biodiversity such as the ‘hot spots’ of sub-Saharan Africa, it cannot, however, constitute a credible response on the scale of tens of millions of hectares which represent considerable socio-economic challenges (Gartlan, 1989).

Policies and governments are called upon to undertake measures so that the extensive growth of agricultural land ties with the objective of food security, fits into the objective of virtuous structural change and aligns with the restriction of biodiversity loss in sub-Saharan Africa. Analysing the effects of agricultural land expansion is crucial to formulating appropriate policies at the national and continental levels.

Therefore, the central question of this study is: **What are the effects of agricultural land expansion on agricultural sustainability in sub-Saharan African countries?**

This main question is subdivided into three secondary questions:

1. What are the effects of agricultural land expansion on food security?

2. What are the effects of increased agricultural land on structural change?

3. What effects does agricultural land expansion have on biodiversity change in sub-Saharan Africa?

**1.3. Objectives**

The main objective of this thesis is to analyse the effects of agricultural land expansion on agricultural sustainability in sub-Saharan African countries. More specifically, it unfolds as follows:

1. To measure the effect of agricultural land expansion on food security

2. To evaluate the effect of agricultural land expansion on the type of structural change.

3. To assess the contribution of agricultural land expansion to biodiversity change.

**1.4. Hypotheses**

The study has the following research hypotheses:

1. Agricultural land expansion reduces the prevalence of undernourishment.

2. Increased agricultural land raises the probability for countries to have industrial type rather than service type of structural change.

3. Agricultural land expansion increases the number of threatened mammal species in sub-Saharan Africa.
**1.5. Interest of the study**

The present study has three contributions to the existing literature. Each contribution is tied to a
specific research question which is exposed in the problem statement section. The first contribution
of this thesis is heuristic as it identifies how agricultural land expansion relates to undernourishment
alleviation in sub-Saharan Africa. Mughal and Sers (2020), support that expanding land does not
guarantee food availability, nor access nor use. At the end, this essay verifies whether increasing land
for agriculture purposes is efficient in reducing undernourishment prevalence.

This thesis's second main contribution is to identify the type of structural change agricultural land
expansion causes in sub-Saharan Africa. Although abundant, all recent studies fail to analyse land
expansion as a determinant of the type of structural change, except Mensah et al. (2016). The
methodology contribution is based on the use of a pooled multinomial logit. This model allows
identifying which type of structural change is more likely to be created by agricultural land expansion
in sub-Saharan Africa.

Thirdly, this thesis implements a short panel that accounts for temporal dimension. Similar studies
(Tan, Chen, et al., 2022; Tan, Yiew, et al., 2022) focus on the Asia and South Asia context however.
This methodology approach is different from studies using theoretical analysis ('t Sas-Rolfes, 2017;
Muchapondwa and Stage, 2015; Iritié, 2015), calibration techniques, simulations and scenarios
(Chaplin-Kramer et al., 2015; Molotoks et al., 2021; Zabel et al., 2019) satellite imagery, map
descriptions and analyses (Fitzherbert et al., 2008; Gaveau et al., 2016; Gibbs et al., 2010; Kraemer
et al., 2015; Laurance et al., 2014; Phalan et al., 2013; Zeng et al., 2018) non-causal or descriptive
analyses (Lawrence et al., 2014; Medan et al., 2011); econometric causal analyses for country-level
studies (Perrings and Halkos, 2015; Sol, 2018) and only long panel analysis (Ullah and Kim, 2021).
According to this review, the temporal dimension is ignored in studies linking agricultural land
expansion and biodiversity loss in Africa.

**1.6. Organisation of the thesis**

This thesis assesses the effects of agricultural land expansion in sub-Saharan Africa on three wedges
of sustainable agriculture: social, economic and environmental. The first essay analyses the
consequences of agricultural land expansion on food security. The second essay investigates the type
of structural change that agricultural land expansion dictates. The third essay analyses the effects of
agricultural land expansion on biodiversity in sub-Saharan Africa. The essays are framed by a general
introduction and conclusion.
ESSAY I

FOOD SECURITY EFFECTS OF AGRICULTURAL LAND EXPANSION IN SUB-SAHARAN AFRICA

*This essay is published as a working paper available using this link https://doi.org/10.31235/osf.io/2djpe*

Longang, S. G., Ningaye, P., and Meli, T. (2023, April 29). Food security effects of agricultural land expansion in Sub-Saharan Africa. *SocArXiv working paper*, University of Minnesota.
# 1. Introduction

Despite national and international efforts to reduce poverty, the number of people suffering from chronic hunger increased by 821 million, and are mostly found in Africa and South America (FAO, 2018b). In sub-Saharan Africa, one in four people suffered from chronic food deprivation in 2017. The prevalence of undernourishment has increased, from 22.7% in 2016 to 23.4% in 2017. The number of severe food-insecure people in Central Africa rose from 50.6 million in 2005 to 79.6 million in 2017 (FAO, 2019).

Agricultural land expansion is expected to play an important role in mitigating this high priority goal of nourishment. To meet current food demand, it is estimated that more than one third of the land area is used as cropland or pasture (FAO, 2018b; Mora et al., 2020). Rapid population growth coupled with increased food demand, requires either expansion of agricultural land or sufficient production gains from current resources (Fitton et al., 2019; Ricardo, 1817).

Although estimates of additional arable land vary, Alexandratos and Bruinsma (2012) report up to 1.4 billion ha of good or prime land that could be brought under cultivation if needed. However, this is with some loss of pastures and significant infrastructure investment. Lambin et al., (2013) suggest that much lower areas of land are suitable for expansion of arable agriculture when all constraints are considered.

However, there are great controversies in the literature concerning the food security effects of agricultural land expansion. Some studies find that land expansion is principally at the benefit of food security (Busch and Ferretti-Gallon, 2017; Carrasco et al., 2017; Lambin and Meyfroidt, 2011; Laurance et al., 2014; Meyfroidt, 2018; Meyfroidt et al., 2014). Meanwhile, other studies find that agricultural land expansion has not led to food security improvements (Mechiche-Alami et al., 2021; Zabala, 2021; Galeana-Pizaña et al., 2018;), or can exacerbate social disharmony and food insecurity through violence and conflicts (Mechiche-Alami et al., 2021; Sauer, 2018). Most of these studies use simulations and landscape analysis, but not econometric techniques. Furthermore, the links between agricultural land expansion and food security are not yet clearly established in sub-Saharan Africa. This essay examines the effect of agricultural land expansion on undernourishment alleviation in sub-Saharan Africa.

This essay is organised as follows. Section 2 contains the stylised facts of food security in sub-Saharan Africa; Section 3 presents the literature review; Section 4 exposes the methodology; Section 5 presents results and discussion and Section 6, the conclusion.
## 2. Stylised facts on the state of food security in sub-Saharan Africa

Despite global commitment and efforts to improve undernourishment, 821 million people (about 9% of the world's population) remain undernourished, of whom 250 million (36%) live in Africa (FAO, 2018a; Foley et al., 2011). Looking ahead, food security remains high on the agenda of the Sustainable Development Goals (SDGs) with a new target of 'zero hunger' by 2030 (Porter et al., 2014). Achieving this target is a challenge given the need to feed more people from the projected increase in the world's population (Sachs et al., 2021).

This section analyses food security trends in sub-Saharan Africa, focusing on undernourishment. The prevalence of undernourishment is the traditional indicator of the access dimension of food security. This indicator is derived from official country data on food supply, food consumption and energy needs, while taking into consideration demographic characteristics such as age, sex and levels of physical activity (FAO, 2021a).

However, two other types of comprehensive indicators represent the access dimension of food security. First, moderate food insecurity describes the situation in which people face uncertainties about their ability to obtain food and have been forced to reduce the quality and/or quantity of food they consume at certain times of the year due to lack of money or other resources. Second, severe food insecurity occurs when people are likely to have run out of food, experienced hunger and, in extreme cases, gone several days without food, putting their health and well-being at serious risk (FAO et al., 2020).

Undernourishment occurs when food security is not guaranteed, due to a gap in one or more of the three pillars. In 2015, about 793 million people (10.9% of the world's population) were predominantly undernourished (FAO et al., 2015). Thus, even if enough food to feed the Earth's population is available, undernourishment can occur when access and use are not met locally (Abbade and Dewes, 2015; Holt-Giménez et al., 2012).

However, if the use of natural resources such as land for agriculture increases, it can lead to a quantitative increase in production in absolute terms (for all crops) as well as in relative terms (for a given crop). This will improve food stability.

This section presents the evolution of undernourishment in sub-Saharan Africa, compares some countries and illustrates food security by using other indicators.
**2.1. Evolution of undernourishment in Sub-Saharan Africa**

Figure 3 shows trends in undernourishment in Africa region and the world, on the one hand, and the number of undernourished in Africa on the other. The available data allow analysis from 2000 to 2020, with 2020 being the projection. The first important point is that, similarly to other regions of the world, between 2000 and 2017, the share of undernourishment prevalence in Africa has decreased from 27% to 22%.

<figure><img src="image_5.png" /><figcaption>Figure 3: Prevalence of undernourished people in the world and Africa, and the number of undernourished in Africa.</figcaption></figure>

Source: FAO et al. (2021)

Notes: Values for 2020 are estimates. The bars indicate the range for the 2020 estimates.

However, a deeper look at the figures reveals that the prevalence of undernourishment has deteriorated significantly since 2014. In 2020, 21% of the population was undernourished, an increase of 4.3 percentage points since 2014. About 70% of the increase in the undernourishment prevalence between 2014 and 2020 occurred in the period from 2019 to 2020.

Due to these figures, Sub-Saharan Africa remains the region with the highest proportion of undernourished people in the world. Its probability of achieving the goal of 'zero hunger' by 2030 is in question. Moreover, the forecasted population growth, global warming and climatic conditions, as well as the negative impacts of the COVID-19 pandemic, call for the unwanted possibility that the number of undernourished people could climb from 83 to 132 million by the end of 2020 (FAO et al., 2020).

In 2020, 281.6 million Africans were undernourished, an increase of 89.1 million since 2014. There is significant variation in the levels and trends of hunger across the subregions. Of the total number
of undernourished, 44.4% or 125.1 million people live in Eastern Africa, 26.7% (75.2 million) in Western Africa, 20.3% (57.1 million) in Central Africa, 6.2% (17.4 million) in Northern Africa and 2.4% (6.8 million) in Southern Africa. Africa accounts for 55% of the global rise in the number of undernourished over the 2014 to 2020 period. In addition, Eastern and Western Africa account for 83% of the Africa-wide increase over the 2014 to 2020 period (FAO, 2021a).

**2.2. Prevalence of undernourishment in some countries**

Analysis of the evolution of undernourishment prevalence in Sub-Saharan African countries (figure 4) shows a generally decreasing trend over time. Thus, based on this indicator, food security in sub-Saharan countries is improving. Two countries whose prevalence of undernourishment was above 45%, have in almost two decades seen this rate drop to around 20%.

<figure><img src="image_6.png" /><figcaption>Figure 4: Evolution of the Prevalence of undernourishment, for countries
Source: Author</figcaption></figure>

For the rest of the countries, there are particularities where undernourishment is increasing. However, it should be noted that in other countries, the prevalence rate halved from 20 to 10% between 2001 and 2018. The study's basic assumption is that this improvement is a response to an increase in agricultural land area. It is mainly due to the availability of arable land and improved access to irrigation which has enabled greater production of food. Additionally, government initiatives such as subsidies and other financial incentives also helped to increase agricultural production.

**2.3. Prevalence of moderate or severe food insecurity in the population**

FAO introduced the prevalence of severe food insecurity in 2017. The indicator is based on the Food Insecurity Experience Scale (FIES) as a complementary indicator of hunger to FAO's traditional
indicator, the prevalence of undernourishment, to provide additional information on the access dimension of food security.

<figure><img src="image_7.png" /><figcaption>Figure 5:Share of population in moderate or severe food insecurity</figcaption></figure>

Source: FAO

The upward trend in undernourishment over the 2014 to 2018 period in Africa is confirmed by the rise in the prevalence of moderate or severe food insecurity within the population (figure 5). This trend was particularly noticeable in Southern Africa, which may have reflected the severe economic conditions in South Africa in 2016 and 2017. In all subregions, severe food insecurity appears to have fallen from 2017 to 2018, even if only marginally in some cases. The improvement was strongest in Eastern and Northern Africa.

However, moderate food insecurity has worsened or remained unchanged in Western and Southern Africa. The measure of moderate or severe food insecurity also shows that in addition to the 277 million people in Africa who are severely food insecure, there are 399 million people who are moderately food insecure, i.e. they did not have regular access to nutritious and sufficient food, even if they were not necessarily suffering from hunger. Of these, 87 percent live in sub-Saharan Africa.
**3. Literature review of agricultural land expansion and food security nexus**

This section makes a presentation of the theoretical background and the empirical literature, linking
agricultural land expansion and food security.

**3.1. Agricultural land expansion and food security: Theoretical background**

Numerous approaches explain how expansion of agricultural land area can explain food security. The theories can be subcategorised into three approaches: the productivity approach, the ends approach and the demographic density approach.

**3.1.1. Agricultural land expansion and food security: a productivity approach**

There is non-consensual debate on the returns of agricultural land and its ability to sustain food security. In economic theories, the ability of agricultural land to advocate food security is subject to authors' views of productivity of agricultural land. Increasing or diminishing returns of agricultural land affects food security differently.

**3.1.1.1. The physiocracy and the surplus of agriculture**

The physiocrats were the precursors of political economy and, like Xenophon, based all achievements on agriculture. The leader of this school of thought, Quesnay, believed that wealth came from the primitive goods of land, men and livestock (Béraud and Faccarello, 1992). For the physiocrats, appropriating land was synonymous with food abundance. Indeed, they supported the idea that extensive agricultural growth would produce a quantity of product greater than the community's food needs. Thus, the expansion of agricultural land is central to the creation of wealth and therefore a source of well-being for the population, and the availability of a variety of foods (Cantillon, 1755). The agricultural sector creates subsistence goods for consumption by the population, which improves the nutritional status of the population. To fight hunger and an unequal diet, they advocate the increase of agricultural land so that the surplus can be transformed into luxury goods (Quesnay, 1758). The more land that is cultivated, the greater the agricultural surplus that is available to ensure the food security of the population.

Similarly, from the analysis of Adam Smith's view point, land for agriculture is subject to increasing returns (A. Smith, 1776). Had it been expanded agricultural land would have produced more and more output. The agricultural output is ready for farmers' consumption or the community through markets and trade. The trade guarantees an equitable access to wealth of the nation, and makes all commodities affordable to all citizens, because prices are all flexible. This traded output could not only increase the availability of varied food stuffs, but also improve the quality of living standard of the population and raise the wealth of the nation. Here, food security cannot be threatened either in the short or the long run, unless there are no cultivable lands to bring into cultivation.
### 3.1.1.2. The classicalist returns of agricultural land expansion: the debate

Contrarily, next considerations to diminishing returns seen by others (Marshall, 1890; Ricardo, 1817) question the real power of agricultural land to guarantee food security. Indeed, the Ricardian diminishing returns' theory holds that land has different fertility statuses. In absence of technology, the increase in land results in very short-lived increase of agricultural production. The use of land will be followed by decreasing returns and decreasing agricultural output. More land will be necessary to ensure food security of the nation while causing agricultural expansion. Extensive agriculture will yield lower and lower agricultural product. However, because of a steadily growing population and decreasing returns of land as well, food security could not be solved for a long period of time.

Another interesting view concerns Marshall (1890) conception of diminishing returns with respect to time. The diminishing returns tendency operates if the time element is eliminated, if the quantity of land is assumed fixed, and if constructive ideas are not developed in technique or organisation. In his *Principles of Economics*, he claims that diminishing returns runs as follows: 'An increase in the capital and labour applied in the cultivation of land causes in general a less than proportionate increase in the amount of produce raised, unless it happens to coincide with improvements in the arts of agriculture.' This means that, if there is a possibility to increase agricultural land, diminishing returns will not operate, and agricultural produce will increase enough to solve food insecurity.

Therefore, there is no unanimous perception about the ability of agricultural land expansion to alleviate food insecurity, because conceptions of returns of agricultural land do differ. This essay seeks to shed light on how increase in cultivated areas is linked to the problem of food insecurity.

### 3.1.2. Agricultural land expansion and food security: the ends approach

According to the ends approaches, agricultural land expansion has many ends such as for biofuel production, commercial agriculture and food security of foreign countries. Therefore, expanding agricultural land may have diversified effects on food security.

#### 3.1.2.1. Debate over the ethics of industrial and biofuel production against food crop production

While it is acknowledged that increasing land can reduce food security in the early stages of development, chances are great that land expansion reduces food availability as countries modernise.

Linking expansion of industrial crop production and food security various outcomes can lead to diversified outcomes. Indeed, food security has emerged as one of the most extensively studied impacts (Jarzebski, Ahmed, Boafo, et al., 2020), largely due to the rapid expansion of biofuel projects during the recent land rush, and the popularity of negative narratives such as land grabbing, land
dispossession and ‘food vs. fuel’. Many studies have explored the food security outcomes of industrial
crop production in SSA across different scales, from the household level to the national and
international level (Jarzebski, Ahmed, Karanja, et al., 2020; Negash and Swinnen, 2013; Zeller and
Sharma, 2000). The perceptions and outcomes of industrial crops articulated in these studies are very
polarised, ranging from ‘industrial crops as major risks to food security’ (Jarzebski, Ahmed, Boafo,
et al., 2020; Wiggins et al., 2015) to ‘important agents of economic growth and rural transformation’
whose expansion could have positive food security outcomes (Arndt et al., 2008, 2011; Hartley et al.,
2019; HLPE<fnref n="4" />, 2013).

However, Schiffman (2013) holds that biofuel production threatens the survival of local communities and exacerbates their starvation statuses. He identifies biofuels as a factor contributing to local food shortages in some areas. This is because it involves the shift from growing food crops to the production of biofuels – mostly for export – in regions of Africa, Asia, and Latin America. Palm oil, jatropha, and sugar cane are being grown in the Philippines, Indonesia, Guinea, and elsewhere to feed Europe’s (and to a lesser extent the United States’) appetite for a cheap and sustainable replacement for gasoline. But in places like Sumatra – where Asia’s last great rainforest is being razed to make way for massive palm oil plantations – the current biofuels boom is anything but sustainable. Nor is it sustainable for the Guarani of Brazil, who is fighting a losing battle to maintain their tribal lands against the U.S. food giant Bunge, which is buying great tracts to produce ethanol from sugar cane.

Goklany (2011) estimates that the increase in biofuel production may be responsible for as many as
200,000 deaths per year from hunger and hunger-related illnesses. He argues that this massive shift
in land use has decreased the amount of food available for human consumption, and consequently
raised the prices of vital staples. There are now millions who can no longer afford to buy these staples
in sufficient quantities. He further says that agricultural area expansion effects on food security mostly
depend on the nature of the product cultivated in the farm. When, staple crops or foodstuff crops are
grown on the new expanded lands, it helps to reduce food insecurity. However, when agricultural
land areas are dedicated to biofuel production it fosters food insecurity and hunger.

3.1.2.2. Agricultural land expansion and food security: Comparing large-scale monoculture versus small scale intercropping

Generally, large-scale firms are specialised in a single-crop culture while the small-scale farmers
grow more than 2 or 3 crops simultaneously in the same farm. Economists tend to use ‘yield’
measurements when calculating the productivity of farms. Yields can be defined as the production

<footnote n="4">⁴ HLPE means High Level Panel of Experts on Food Security and Nutrition of the Committee on World Food Security.</footnote>
per unit of a single crop. For example, beans farm will be judged by how many metric tons of beans are produced per hectare. It is true that the highest yield of a single crop is often achieved through industrial monocultures, and smaller farms can rarely compete with this monoculture single-crop yield (Kimbrell, 2003).

The large-scale plantation exacerbates food insecurity. Agricultural land acquisitions by large-scale farms support agriculture for exports. According to Olivier de Schutter, *'Small-scale family agriculture, on which most of the world's rural poor still depend, is threatened by large-scale plantations, export-led agriculture, and the production not of food but commodities.'* (Borras et al., 2011). The consequences of this process have been profound, and have threatened millions with starvation.

However, because small-scale farms use ‘intercropping’ methods to plant other crops where single-crop monocultures have empty ‘weed spaces’, and because small farmers are more likely to rotate or combine crops and livestock with the resulting manure replenishing soil fertility, these small-scale integrated farms produce far more per unit area than large farms. Though the yield per unit area of one crop – corn, for example – may be lower on small-scale farms, the total output per unit area (with small farms often producing more than a dozen crops and numerous animal products) is nearly always higher than that of larger farms (Kimbrell 2003).

This means that cultivated area expansion by small farmers offers at least two ways of solving food insecurity from both the availability and diversity of food. The latter dimension is not as much taken into account when expansion of agricultural areas benefits to large-scale farms for monoculture. Therefore, if land expansion for agriculture is initiated by local producers, as assumed in this essay, it can foster food security.

#### 3.1.2.3. Agricultural land expansion through grabbing and food security

The expansion of agricultural land can be caused by large-scale acquisitions and supported by foreign developed countries. This is termed as land grabbing and mostly has the aim of ensuring home food security than host food security. ‘Land grabbing’ is known as to the purchase or long-term lease of vast tracts of land from mostly poor, developing countries by wealthier, food-insecure nations as well as private entities to produce food for export. This concept is becoming gradually important since 2008, and raised deep concern over food security. Land grab represents a major shift from public to private sector control over agricultural investment, and from domestic to foreign control over crucial food-producing lands. Mechiche-Alami et al. (2021) concludes that it does not always lead to food security as land owners (foreign countries) either produce cash crops or food crops but for their population food security.
Because of the establishment of large farms through land grabbing, numerous are former small farmers who have been pushed off their land by a variety of economic forces. According to Olivier de Schutter, the UN Special Rapporteur on the Right to Food, ‘Small-scale family agriculture, on which most of the world’s rural poor still depend, is threatened by large-scale plantations, export-led agriculture, and the production not cash crops.’ The consequences of this process have been profound, and have threatened millions with starvation (Daniel, 2011).

In the same vein, it is argued that the spread of large-scale industrial agriculture in Africa and across the global South will ultimately lower food costs and benefit the poor. But surprisingly, recent price increases call this view into question (Schiffman, 2013). When local food prices levelled at highly unstable global commodity prices, it is often the people at the bottom of the economic ladder who are the first to suffer the pain. The poor, many of whom already spend more than half their income on food, can still afford to pay more for staple goods. This worsens food insecurity (Schiffman, 2013).

**3.1.3. Agricultural land expansion and food security: a demographic density approach**

The food security effects of agricultural land expansion are subject to population density specificities. Increased cultivated land area accounts for a significant portion of production increase which shrinks the level of food insecurity in the early stages of modernisation. Two divergent trends propose explanations: many small but widespread additions in already high population density areas; and, occasionally, large tracts of underutilised land in low population density areas.

**3.1.3.1. Additions in high population density areas**

Densely populated rural areas have low probability of possessing idle agricultural land, and can make good use of available technology. Subsequently, converting more land to agriculture augments agricultural output. As matter of fact, in highly populated Ethiopia, for more than 20 years, increased land accounted for about 40 percent of the rapid agricultural growth, while India experienced increasing returns as well (Mellor, 2017). Based on the Ethiopian and Indian experience, Mellor (2017) argues that ‘the logic is clear. Technological change brings increased returns to land, covering the costs of bringing additional land into cultivation.’ Although land is limited, increased land areas supported by technological change is expected to improve availability of food and its use to curb food insecurity.

**3.1.3.2. Additions in low population density areas**

The situation in the low population density areas is merely different to that of high densely populated areas. Those areas are commonly dry and inhabited sparsely by semi-nomadic families, and where land use is restricted by disease. Generally, cultivable areas there could have high potential
productivity, but may require substantial investments in roads and irrigation systems, although
developing it may be highly profitable (Laurance et al., 2017).

There are two major issues about investing and developing such lands. First is the issue of who will make the infrastructure investment and this should be considered in view of the investment made by those who will farm the land. That decision is in the context of small commercial farm areas has the potential of large increases in productivity that will hasten food security.

Second, is the irrigation issue. Irrigation increases land areas to be cultivated (Mellor, 2017). Returns from investment in irrigation are likely to be higher in existing areas that could benefit from an additional crop and ensuring production in the main cropping season, often with lower-cost water sources. Vast areas of Asia (e.g. in India and Pakistan) have been irrigated, long before modern yield-increasing technologies were on the horizon. Presumably, that investment paid off, at least where the underlying resource was dry (Mellor, 2017). Hence, expanding land in underpopulated areas can help to cure food insecurity if backed by irrigation. However, it depends on whether that land will be dedicated to food crops or biofuel.

This subsection has enabled us to show that they are different approaches in theoretical economic review relating agricultural land expansion to food security. It has been shown that within each of the approaches, many strands are conflicting one to the other. Therefore, the theoretical debate justifies the necessity to shed lights on the relationship between land expansion for agriculture and food security. More evidences are given with empirical studies in the next subsection.

**3.2. Agricultural land expansion and food security: Empirical review**

Although empirical literature on this topic is scanty, this subsection provides the restitution of empirical studies linking on one hand, agricultural land expansion and food security, and, on the other hand, studies relating other aspects of land and food security.

**3.2.1. Agricultural land expansion and food security**

**3.2.1.1. Expanding land for agriculture**

Ngoma et al. (2021) use national representative data and tobit model to conclude after their research that cropland expansion by smallholders into forests represents about 60% of the estimated 250,000 ha of forests lost per year in Zambia. Most households expanded cropland because they needed to meet subsistence food requirements and a few others in response to market opportunities. This goes in line with the findings of Keating et al. (2014). Using arable land as control variable, and find that in developing countries, expanding land significantly increases food availability and further reduces the rate of undernourished people.
Likewise, in India Kumar and Sharma (2020) investigate if agricultural expansion by restoring the degraded lands provokes food security in India. They use descriptive statistics and conclude that reclamation of 2.18 million ha of salt-affected soils has contributed more than 17 million tons of food grains per annum to the country's food basket, with additional annual income of Rs. 15.5 billion. Moreover, other technologies of management of salt-affected soils, have positively impacted food and nutritional security. On this basis, expansion of agriculture in sub-Saharan African countries, through the restoring degraded land, can also increase the food security indicators.

Then, Anriquez et al. (2013) proves at the micro level and using partial equilibrium approach and micro-simulations that access to agricultural land ensures food security taking nutritional attainments, as a measure of welfare. As secondary result, they discovered that access to agricultural land plays a significant role in ensuring adequate nutritional attainments in rural areas, and even in urban areas.

Then neatly, in their historical study, Ramankutty and Foley (1999) drew maps disclosing the positive evolution of cropland area from 1700 to 1992, showing both the spread of cropland and its intensification in all regions. Before 1900, agricultural expansion was the most prominent way to increase food production, as with food grain production during 1949–65 in India (Narain, 1977; Vaidyanathan, 1986). But after the advent of industrial agriculture, using revolutionary cultivation techniques could also increase productivity on the same land.

Conversely, Wise (2021) taking the case of Rwanda and Zambia, concludes that productivity does not increase overall production as extensification does. He uses a twofold decomposition methodology and find that production results more from agricultural land use than from intensification. This is in line with estimations of FAOSTAT (2013), which over the period 1961 to 2000 in Africa, the land footprint grew by only 11%, while agricultural output grew by 153%.

On the other hand, some studies on extensification of agricultural frontier can find mitigated links with food (in) security, because of impoverishment of lands, land disputes or food wastage. Zabala (2021) uses pure narrative and Galeana-Pizaña et al. (2018) use cartography and spatial estimations methodologies to show that increases in agricultural land cover have not led to improvements in food security in Mexico for almost four decades, except for the case of corn self-sufficiency. The trends of agricultural expansion in three ecoregions were related to increases in food production. However, livestock expansion, a land-use change also common in other regions in Latin America, was associated with diminishing food security in most ecoregions.

Analysing graphs of chronological data on food security, Ramankutty et al. (2018) find that despite the calorie production per capita due to agricultural land expansion, malnutrition still persists in 158
countries. Despite of using mapping and Landsat, Sauer (2018) draws similar conclusions, saying that agricultural modernisation and the expansion of the agricultural frontier, contributed to the emergence of new territorial disputes, creating additional obstacles and increasing the demand for land prices of land, increasing thus food insecurity in Pará's State in the Brazilian Amazon. The result is supported by Godfray et al. (2010) in his descriptive analysis. Findings suggest that output of food-producing lands should be more helpful against undernourishment if wastage were avoided. In the developing world, losses are mainly attributable to the absence of food-chain infrastructure and the lack of knowledge or investment in storage technologies on the farm. So, reducing wastage at relevant levels will optimise the food-producing lands' capacity to cure undernourishment.

However, the previous studies (except Kinda and Badolo, 2019; Godfray et al. 2010) analyse food security in terms of food production or availability. If the latter study is old, the more recent consider all developing countries. Although nutritional status is more crucial to cease food security (Rulli and d'Odorico, 2014), this essay is more attached to agricultural land expansion effects on social dimension of agricultural sustainability in sub-Saharan Africa.

Access to land is an important driver of land expansion. Muraoka et al. (2018) explore the link between access to land and food security. Land rental is found to be the main approach used by rural households in a given year to access additional land for cultivation. Econometric results of his frictional model indicate that land rental markets in Kenya do not allow for the reallocation of land in a way that would fully contribute to national food security and poverty reduction objectives. Though, with imperfections of land rental markets, land rental still contributes to rural household food security. Equally, van Wijk et al. (2019) with graphical and correlation analysis conclude that access to land can have damages on the food security of small holders in SSA. Indeed, they find that the link between food security and farm size in smallholder households is relatively weak and they further add that although unequal access to land translates into unequal food security.

Another interesting link concerns the agricultural production and nutritional status relationship. They are two contradictory strands of studies. To kick off, Mughal and Sers (2020) with an econometric model on macroeconomic data examine the role played by the increase in cereal production in improving the South Asia region's nutrition and food security. Estimates from the Generalised Method of Moments Findings suggest that a 1% increase in cereal production and yield is associated with up to 0.84% decrease in the prevalence of undernourishment significant over a period of 3 years. Ray (2007) investigates the changes in the nature and quantity of food consumption in India during the reform decade of the 1990s, and analyses their implications for calorie intake and
undernourishment. The author concludes that an increase in quantity of food consumed will reduce the ratio of people suffering from undernourishment.

However, Purwestri et al. (2017) analyse the paradox between high rice productivity and low nutritional status of children triggered in the Demak Regency and the prevalence of stunting of children under five. Using Independent T-test and MannWhitney U-test to conclude that, even though the area has high rice production, child malnutrition, was still high. Similarly, it is found uncertain that agricultural expansion reduces undernourishment, as the availability of food may not always lead to its access nor use (Zabala, 2021) in Africa (Ziem Bonye et al., 2021) or in Mexico (Galeana-Pizana et al., 2018).

However, none of these studies consider the role of agricultural land on prevalence of undernourishment, except Anriquez et al. (2013), whose study does not explore food security at national levels, to draw their conclusions, as the study dares to do. Moreover, few studies use econometric tools on macro data to assess the relationship between agricultural land expansion and food security.

### 3.2.1.2. Land expansion backed by large scale land acquisition

Several studies interested in land expansion from land acquisition and food security do not end up to a consensual result. Firstly, land acquisition has the real potential to alleviate food insecurity. As a matter of fact, Daniel (2011) in his theoretical paper, investigates with his narrative methodology under which conditions land grabbing in developing country land markets impacts land reform agendas and other policies to promote food security. Estimating the potential yield with land matrix data base, Rulli and d'Odorico (2014) analyse the effect of large-scale land acquisitions on food availability in developing countries and find that up to 300–550 million people could be fed by crops grown in the acquired land.

Secondly, several studies conclude that land grabbing has negative impacts on food security. For instance, Mechiche-Alami et al. (2021) apply mapping and simulations to conclude that Large-Scale Land Acquisitions (LSLA) in Africa meet identified food security needs only in a minority of countries. They find that the most productive land is most often allocated to flexible crops, while food crops are produced on more marginal land. Therefore, showing that even when their objective is agricultural production, most LSLA are unlikely to improve food security. Similarly, merging agricultural, remote sensing, and plot-level survey data of 11 African countries, georeferenced information on 160 land acquisitions in 39 countries, Müller et al. (2021) find that land expansion associated backed by land acquisition causes food security as energetic crops are cultivated at the expense of nutrient crops. Moreover, it is proved with mapping methodology that land grabbing
exacerbates food insecurity, decreases job opportunities, sparks migration in host country (Nyantakyi-Frimpong and Bezner Kerr, 2017), increase social conflicts between farmers and pastoralists (Oberlack et al., 2016; Soeters et al., 2017). In northern Sierra Leone, Yengoh and Armah (2015) use a mixture of quantitative and qualitative research methods with group comparisons of a program to conclude that expansion of land acquisition causes an increase in the severity of food insecurity and hunger.

Also, wages from employment by the company cannot meet the staple food needs of its employees. Furthermore, the results of Marselis et al. (2017), using computable general equilibrium model suggest that land acquisitions reduce the local availability of agricultural land per capita and could potentially affect local food security. Ahrends et al. (2015) mobilises mapping and principal component analysis to evaluate the spread of rubber between 2005 and 2010 in combination with environmental data. They conclude that expansion of monoculture rubber plantations into marginal areas creates a clear potential for loss-loss scenarios, such as the clearing of high-biodiversity value land for a crop that is poorly adapted to local conditions and, by altering landscape function while not producing long-term sustainable yields, may ultimately also compromise livelihoods.

Thirdly, a bunch of studies identify multiple relations between extensification through land acquisition and food security, depending on whether the dimension of food security or the type of country involved in the acquisition process. For instance, studying various regions of the world, Müller et al. (2021) that, controversially, land acquisition increases food production and exacerbates food insecurity. Indeed, agricultural land expansion from deals in these regions also shifts production away from local staples and coincides with a gradually decreasing dietary diversity among the surveyed households in sub-Saharan Africa.

In addition, Santangelo (2018) uses data on 65 developing countries from 2001-2011 and mediation analysis in his investigation. Findings suggest a differential impact depending on the investor's country of origin. When foreign investments in land come from developed-country investors, it causes a positive influence on food security by expanding land used for crop production as the but when it comes from developing country investors, it carries negative influence on food security by decreasing.

The previously cited studies focus only on land acquisition by foreigners with areas at least larger than 8 Ha (Mechiche-Alami et al., 2021). The purpose of this essay is to account the effects of the global phenomenon of land expansion on people's food security. Secondly, those studies use simulations and Input – Output methods of estimation, while this essay uses econometric techniques of estimation.
3.2.2. Land and agricultural reforms, tenure, rights and food security

The main purpose of Ceddia et al. (2019) is to assess how different land tenure regimes can direct the gains in agricultural productivity towards land sparing or Jevons paradox in ten Latin America countries from 1990-2010. The results of the dynamic panel approach confirm that agricultural expansion is indeed a dynamic process, which is characterised by temporal lags (Richards et al., 2014), and that improvements in agricultural productivity per se are land-sparing, and more importantly, the overall effect of increased productivity crucially depends on the institutional context.

Schoonbeck et al. (2013) conduct a systematic review which aims at reviewing the main potentials and challenges of organic agriculture approach when dealing with ‘undernourishment’ as a multifaceted concept in developing countries. It concludes that conventional and biotechnological approaches still produce higher yields than organic agriculture. However, considering the many advantages of organic agriculture, it can in a long run, be more conducive than now to meet food security. Implementation of organic agriculture calls for agricultural land expansion, to curb undernourishment.

Using a farm-level data from the 2005 Albania Living Standards Measurement Survey, Deininger et al. (2012) find no support for the argument that land fragmentation reduces productivity, nor does not worsen food availability. Moreover, Caian et al. (2018) investigate in the same country land fragmentation's implications. It appears that the land fragmentation contributed to food security improvement by increasing the variety of on-farm produced foods for household self-consumption, thus ensuring a higher likelihood of meeting nutrient requirements that can promote good health of rural population in Albania.

Rao (2006) examines the conceptual linkages between the issue of land rights for women, with household food security. The author uses a narrative methodology and argues that in a context of diversified rural livelihoods, the contribution of agricultural production to household subsistence has been declining. However, the renewed production and food security nexus in agricultural policy have meant allowing men not to have responsibility for household food security. Whereas a right to land for women often left behind for agriculture management, appears to be leading to an enhancement of work burdens and food security improvement.
# 4. Methodology of analysis of the effects agricultural expansion on food security

In this section of the study, the methodology to assess the effects of agricultural land expansion on food security in sub-Saharan Africa is presented. The starting point is data description, followed by the estimation strategy presentation.

## 4.1. Description of data and variables

This subsection provides more information on the data and scope of the study, the building and explanation of variables in the model, and the tools used to describe the links and stationarity tests.

### 4.1.1. Nature, data sources and scope of the study

In this essay, all the data are quantitative, from secondary sources and rely on the World Bank's World Development Indicators (WDI, 2021). The data on food security are from the Food and Agriculture Organization of the United Nations (FAO, 2020) and the variables on governance and institutions are from the World Governance Indicator (WGI, 2021). The study considers 28 countries in Sub-Saharan Africa, over the period 2000–2016. Both temporal scope and number of countries are based on data availability.

### 4.1.2. Description and construction of variables

#### 4.1.2.1. Measures of food security

Because of the multidimensional aspect of food security, several indicators have been used in the literature. They are subcategorised in simple and composite indicators.

##### a. Simple indicators of food security

Four dimensions of food security exit: availability, access, utilisation and stability. Several variables can capture each of these different dimensions. Availability is measured by production or livestock index, or by average dietary energy supply adequacy (Caccavale and Giuffrida, 2020; Mechiche-Alami et al., 2021) accessibility pillar is captured by the per capita GDP, GINI index (Eini-Zinab et al., 2020) or prevalence of undernourishment or FAO's domestic food price index (Mechiche-Alami et al., 2021; Mughal and Sers, 2020). The aspect of utilisation is sized by the under-five mortality rate or the use of improved sanitation and the stability dimension is measured by the variability of net per capita food production (Caccavale and Giuffrida, 2020; Mughal and Sers, 2020).

The proportion of undernourished people, estimated by the Food and Agriculture Organization of the United Nations (FAO), reflects the proportion of the population with inadequate dietary energy intake, i.e. the proportion of people who are energy deficient. Cultivation of more land is likely to bring both negative and positive changes in diets and challenges to food safety which can affect nutritional status in various ways.
## b. Composite indicators of food security

Two composite indicators are presented in the literature: the Global Hunger Index and the Food Security Index. Some studies (Wiesmann, 2006; Wiesmann et al., 2015) refer to the Global Hunger Index (GHI) as a measure of food insecurity. The Global Hunger Index (GHI) combines four indicators undernourishment, childhood wasting, child stunting, and infant mortality, into one. However, this indicator is not available over a long period of time, which disqualifies its use in this research.

The analysis of food security using this index is based on the WFP consolidated approach. It combines a suite of food security indicators (food consumption score, share of food spendings, people's livelihood strategies) into a composite indicator: the FSI. While interesting as an indicator, it is difficult to calculate because of its descriptive nature and does not allow for easy interpretation in research.

Food security is measured in this essay by the prevalence of undernourishment proposed by FAO. It is measured at household level by estimating the number of people whose dietary energy consumption is likely to fall below established thresholds referred to as dietary energy requirements.

Focusing on the FAO's prevalence of undernourishment indicator, one of its main criticised shortcomings is the way of measuring the quantity of food available for human consumption. It is the result of considering food production, trade, stock changes, non-food uses, and extra-household waste. The food quantity is converted into calories and divided by the population. As a result, the indicator may be understood as a measure of food availability and not food intake (de Haen et al., 2011; Wanner et al., 2016). However, FAO undernourishment prevalence is a known proxy of food energy consumption that complies the DES from food balance sheets (FBSs) (Eini-Zinab et al., 2020). Then, following Soriano and Garrido (2016), it remains the best known and simplest proxy of undernourishment for a macrolevel study and is the indicator commonly used in the literature to monitor severe or chronic food security (Mughal and Sers, 2020).

Another dimension of food security is food utilisation, or consuming a nutritious diet. This means that people make appropriate use of food, based on knowledge of basic nutrition and care, and have access to water and sanitation for preparing food and maintaining proper hygiene (FAO, 2021). To strengthen the analysis of the effects of agricultural expansion on food security, the study uses other food security variables such as improved water sources and improved sanitation facilities. Both are provided by the World Bank (WDI, 2021), and are indicators of the utilisation dimension of food security (Bhattacharjee and Sassi, 2021; Caccavale and Giuffrida, 2020).
This essay uses variables constructed from previous high-quality studies. For example, the institutional quality or governance (Gov) index from Totouom et al. (2019), the food price vulnerability (FPV) index following Combes et al. (2014), the production index (PI) composed of food, grain and livestock production indexes, as well as labour spillovers inspired by Baymul and Sen (2020).

#### 4.1.2.2. Institutional quality index (Gov)

The institutional variable in this essay is the governance index, whose construction accounts for six indicators of institution quality. The index construction follows the methodology described by Totouom et al. (2019). Following its methodology, the institutional quality index constructed using the PCA is the first principal component of the vector of the six indicators of institution quality. This first principal component accounts for about 82% of the total variance in the original data. The building of the governance index has therefore reduced the dimension of the institutional quality indicator by five and preserving 82% of the information in the data. Table 24 in the annexe reports the PCA results.

The aggregate institutional quality index is normalised to obtain positive values that will allow us to have only positive values ranging from 0 to 10. This normalisation is based on the following formula:

$$Gov = \frac{Gov_g - \min(Gov_g)}{\max(Gov_g) - \min(Gov_g)} \qquad (eq1)$$

Where $Gov_g$ is the gross indicator of governance, and Gov is the normalised indicator.

#### 4.1.2.3. Food price vulnerability index (FPV)

This variable construction is based on Combes et al. (2014) and Kinda and Badolo (2019). These authors suggest that countries are vulnerable to price shocks if they meet three criteria: high food dependence, high food import weight and low income. High food dependency, as measured by the share of food imports in total household consumption, highlights the importance of food in the basket of goods consumed by a representative household in the country. The larger the household's basket of goods, the more food prices will affect it. A substantial level of food imports, measured by the ratio of food imports to total imports, underlines the country's high dependence on food imports. The level of income, measured by GDP per capita, highlights a country's ability to provide food safety nets for domestic consumers. The price vulnerability index is a weighted average of the share of food imports in total household consumption; the share of food imports in total imports of goods and services and the inverse of the level of GDP/head.
#### 4.1.2.4. Production index (PI)

In the literature review, it was established that dimensions of food security are sometimes interrelated (Kinda and Badolo, 2019; Mughal and Sers, 2020). To assess the effects of food availability on undernourishment, the study uses a production index. This index is constructed from three well-known indexes: (i) the food production index (ii) the cereal production index and (iii) the animal production index. A 'production index' is constructed using principal component analysis (PCA). This index is considered the wedge of the availability dimension of food security.

The composite food production index is the weighted arithmetic average of the three production indexes. This principal component accounts for 83.2% of the total variance of the original data. This means that the construction of the index has reduced the dimension of the food availability indicators by two while preserving 83% of the information in the data. Results of the PCA are reported in table 25 in annexe.

The aggregate production index is normalised to obtain positive values that will allow us to obtain only positive values ranging from 0 to 100. It is normalised using the following formula.

$$PI = \frac{PI_g - \min(PI_g)}{\max(PI_g) - \min(PI_g)} \qquad (eq2)$$

Where $PI_g$ is the gross indicator, and $PI_N$ is the normalised indicator.

In line with Kinda and Badolo (2019) which shows that cereal production index reduces the proportion of undernourished people in developing countries, the expectations are that production index (PI) and undernourishment variables to have an inverse relationship.

#### 4.1.2.5. Building the variable labour spill overs

Timmer (2017b) holds that food security and labour movements are mutually dependent. It is expected that the movement of labour out of agriculture will have an effect on food production and the incidence of undernutrition. These movements are of two types, according to Baymul and Sen (2020). From agriculture to industry and from agriculture to service sectors. They are calculated as follows:

$$Indspill_{it} = indsh_{it} - agrsh_{it} \qquad (eq3)$$

$$Scespill_{it} = sches_{it} - agrsh_{it} \qquad (eq4)$$

Where $agrsh$, $indsh$ and $schesh$ represent the shares of agricultural, industrial and service labour force respectively, in the total employment of country i in year t.
It is expected that movements from agriculture to industry (indspill) reduce the proportion of undernourishment people and the services spill over (scespill). Indeed, both are facing structural transformation, which should moderate undernourishment.

### 4.1.3. Descriptive statistics of variables and graphical analysis

The descriptive statistics provide the broad characteristics of the variables and their stationarity level. The graphical analysis shows the land expansion paths in sub-Saharan Africa and the link between undernourishment and agricultural land expansion. This is done by using scatterplots for within and between variation. After these presentations of preliminary relationships, the econometric models are estimated.

### 4.1.4. Stationarity test

This tool is used to analyse the presence of unit root in time series, using the Levin-Lin-Chu tests and the Fisher test when necessary. For example, when the series fails to have strongly balanced data, as required by the LLC test. The two tests are of first generation because they consider the absence of correlation between individuals. The Fisher test is a nonparametric test, whereas the LLC test is a parametric test.

Levin and Lin (Levin et al., 2002) are the first authors to use and analyse unit root tests. These tests are used to determine the integration order of variables and to check the existence of long-run relationship between variables. According to Blot and Cochard (2008), those tests are prior steps of the cointegration analysis, with the final objective to avoid fallacious regressions on panel data. Generally, the stationarity of a variable must be demonstrated by at least two tests because the stationarity results can vary according to the selected test. Following Hurlin and Mignon (2005), first-generation tests are used. The authors of the LLC test assume the simplest form of heterogeneity which is based on the existence of individual specific constant, but hold the hypothesis of homogeneity of other model parameters and the autoregressive root. The restrictions of this test have led to the creation of new tests accounting for more specificity.

These authors use the simplest form of heterogeneity based on the existence of constants specific to each individual. However, they retain the assumption of homogeneity of the other parameters of the model and, in particular, of the autoregressive root. The restrictive nature of these tests has led to the development of new tests which take into account a wider heterogeneity of the dynamics of the series studied (Im et al., 2003; Maddala and Wu, 1999). Under the alternative hypothesis, these tests allow not only heterogeneity in the autoregressive root but also heterogeneity in the presence of a unit root in the panel.
For small T the MW test slightly dominates the IPS test in terms of test power. For large T (T= 50 or 100), the powers are comparable, but the size distortions are smaller with MW. As with any nonparametric approach, the Maddala and Wu test is not a victim of specification error on the residual's distribution (assumed to be normally distributed in IPS). Maddala and Wu (1999) show that in all cases IPS and MW> LLC. In terms of power (corrected by T), the MW test performs better than the IPS test. The stationarity test results will guide the process of estimation strategy.

## 4.2. Estimation strategy

The estimation technique depends on the stationarity of variables, especially the dependent variable. This subsection a baseline model is presented in, and all variables and expected signs are justified.

### 4.2.1. The baseline model

Our baseline model has the following form:

$$Y_{it} = \alpha_0 + \alpha_1 \text{aland}_{it} + \beta_{it} X_{it} + \varepsilon_{it} \quad (eq5)$$

Where $\alpha_0, \alpha_1$ and $\beta_{it}$ are the vectors of parameters to estimate. $\varepsilon$ is the error term by assumption, normally distributed. $Y_{it}$, is the dependent variable capturing undernourishment. It refers to the proportion of people undernourished in country i at period t. The variable $\text{aland}_{it}$ is the variable of interest capturing agricultural land expansion. $X_{it}$ is the set of control variables, including governance, socio-economic variables, and other variables as justified in the next paragraph.

To estimate the model (eq5), this work uses methodologies used by other studies (Kinda and Badolo, 2019; Barbier, 2020; 2021). The studies one and two-way fixed and random effects panel regression, to assess the land expansion on food security. The choice of the best estimators to interpret is provided by the results of the Hausman test. However, the results obtained are inconsistent because of potential bias of endogeneity. The second methodology used in the study, takes into account the possible risk that endogenous variable can be explained by its previous values. To account for this new specification, the model is written as:

$$Y_{it} = \alpha_0 + \gamma Y_{i,t-1} + \alpha_1 \text{aland}_{it} + \beta_{it} X_{it} + \varepsilon_{it} \quad (eq6)$$

The estimates of this model in equation 6, by Ordinary Least Squares (OLS) and General Least Square (GLS) are both inconsistent. The endogeneity is caused by the introduction of the lagged dependent variable. Better alternatives are to use System Generalised Method of Moments (SGMM) following Arellano and Bover (1995) and Blundell and Bond (1998). Additional argument of using the GMM, includes that the important econometric condition of a short T (16) and a large N (28) is respected.
In the presence of a lagged dependent variable, the model is shifted from the static context to the dynamic panel model. Estimating the model through either OLS or GLS technique will create an important bias, known as the ‘Nickell Bias’. According to Nickell (1981) while OLS estimators may be biased upwards, fixed effects may be biased downwards. Consequently, to obtaining consistent and efficient estimators, the study opts to apply the GMM proposed by Arellano and Bond(1991), Arellano and Bover (1995) and Blundell and Bond (1998).

Several reasons motivated the choice of the GMM. The GMM method is regularly used to solve econometric problems such as heteroscedasticity, endogeneity, over identification and validity in the literature. Baum et al. (2003) hold that heteroscedasticity is always present in empirical studies and best issues to solve it is to use the GMM estimator. Similarly, GMM is used to gauge instrument strength (Bazzi and Clemens, 2013). Theoretically, the endogeneity issue may arise because of reverse causality, measurement errors or omitted variable bias. Arellano and Bover (1995) and Blundell and Bond (1998) then pose the use of GMM estimator to address the endogeneity problem. This relies on the ability of the GMM of treating the endogeneity of all explanatory variables by using their lagged values (in terms of level and first difference) as instrumental variables.

Moreover, the Sargan and Hansen tests of over-identifying restrictions and the test of autocorrelation are used in order to ensure the validity of the instruments used for the estimations. A GMM estimator can be constructed by using valid internal instruments and which are based on differencing the regressors. When lagged levels of the exogenous variables are used as instruments, this refers to the difference GMM. Nevertheless, due to the different shortcomings of the difference GMM (D-GMM), a System GMM (S-GMM) estimator which uses lagged differences of both the dependent and explanatory variables as instruments, is used. However, Windmeijer (2005) concludes from Monte Carlo simulations that the estimated asymptotic standard deviations of the two-step GMM estimator can be biased downwards in a finite sample. Using Windmeijer (2005)'s correction procedure, we eliminate such a bias. The two methods of estimating dynamic panel regression are used to see whether relationships between variables within the model are robust to different estimation methods.

The robustness analysis is in two steps. In the first step, equation 6 (eq6) is re-estimated with year averages. The long time dimension (2001-2018) allows to construct the observations as an aggregated five-year average, thus eliminating short-term fluctuations due to white noise in the analysis, which may appear substantially (Asongu and Nwachukwu, 2017; Mulindabigwi Iraduku, 2017). The second advantage of using five-year averages of data is to limit the proliferation of instruments that invalidate the results (Asongu and Nwachukwu, 2017; Islam, 1995). Following the reestimation using the five-year average, the impact of agricultural land expansion on food security is assessed by utilising other
variables of land expansion. Other indicators include arable land, cereal land, and forest area (Tan, Chen, et al., 2022). For simplicity, agricultural land is the land area dedicated to permanent crops, whereas arable land is dedicated to temporary crops. Cereal land refers to dry grain production land. It is critical to mention that forest area is linked to land-use expansion. According to findings, the latter is the first cause of the former (Hu et al., 2021).

In the second step of the robustness analysis, the study switches the response variable of food security to water and sanitation. They are essential for both availability and utilisation pillars of food security. Moreover, food utilisation goes beyond having the right food, but requires changing behaviours related to feeding and child care. It also requires access to and correctly using safe drinking water, hygiene, and sanitation services. Safe drinking water and hygienic sanitation are the requionuirements for the effective utilisation of food (Bhattacharjee and Sassi, 2021). Though water is an essential input to food systems activities, cooking, drinking and food processing (Linderhof et al., 2021), insufficient access to water and sanitation facilities is strongly linked to a significant risk for diarrheal disease and other illnesses, which can result in the inadequate absorption of vital nutrients (Soriano and Garrido, 2016). Furthermore, poor quality of drinking water can result in water-borne diseases that hamper the proper food use (FAO, 2016; FAO et al., 2015). Following several studies (Bhattacharjee and Sassi, 2021; Caccavale and Giuffrida, 2020) the essay uses the share of the population using at least basic sanitation and drinking water services to capture the utilisation dimension of food security.

Thus, the study is a pioneer as it uses water and sanitation to proxy food security at macro level and its influence due to agricultural land expansion. Later on, we provide an analysis for different milieus for the total population and for both urban and rural milieus.

### 4.2.2. Justification of variables in the model and expected signs

Table 1 summarises the expected signs of different variables on undernourishment prevalence and the justification follows. Agricultural land (aland) is the main interest variable and refers to the share of land area that is arable, under permanent crops, and under permanent pastures. Following theoretical analysis of Keating et al. (2014), and empirical analysis of Kinda and Badolo (2019) it is expected that agricultural land positively affects food security.

For robustness the study uses other variables related to agricultural expansion. For instance, arable land, cereal land, forest area and permanent crop land. Arable land includes land defined by the FAO as land under temporary crops, temporary meadows for mowing or pasture, land under market or kitchen gardens, and land temporarily fallow. Land under cereal production refers to harvested area for dry grain such as wheat, rice, maize, barley, oats, rye, millet, sorghum, buckwheat, and mixed
grains, in percentage of land area. Forest area is land under natural or planted stands of trees, whether productive or not, and excludes tree stands in agricultural production systems as fruit plantations and agroforestry systems, trees in urban parks and gardens. Permanent cropland is land cultivated with crops that occupy the land for long periods and need not be replanted after each harvest, such as cocoa, coffee, and rubber. It includes land under flowering shrubs, fruit trees, nut trees and vines (WDI, 2021).

The previous subsection described how the production index (pi) is constructed. This methodology extends beyond what the literature (Mughal and Sers, 2020; Kinda and Badolo 2019) actually provides. The study assumes that the production index has a negative relationship with the undernourishment variable. Intuitively, food security is enhanced by an increase in food production and livestock. The higher the quantity of food produced, the more people will have access to it, which decreases the proportion of undernourished people.

<table><caption>Table 1: Expected signs of variables explaining undernourishment</caption><thead><tr><td>Names</td><td>Labels</td><td>Expected signs</td></tr></thead><tbody><tr><td>aland</td><td>Agricultural land</td><td>–</td></tr><tr><td>Pi</td><td>Production Index</td><td>–</td></tr><tr><td>lnco2</td><td>CO2 emissions (kt)</td><td>–/+</td></tr><tr><td>lngdpcap</td><td>Gross domestic product per capita, constant</td><td>–</td></tr><tr><td>Fpv</td><td>Food Price vulnerability index</td><td>–</td></tr><tr><td>popg</td><td>Population growth</td><td>–</td></tr><tr><td>rurpop</td><td>Rural population (% of total population)</td><td>–/+</td></tr><tr><td>indspill</td><td>Spillover from agriculture to industry</td><td></td></tr><tr><td>Scespill</td><td>Spillover from agriculture to service</td><td></td></tr><tr><td>Tb</td><td>Incidence of tuberculosis (per 100,000 people)</td><td>–</td></tr><tr><td>Phiv</td><td>Prevalence of HIV, total (% of population ages 15-49)</td><td>–</td></tr><tr><td>Water total</td><td>Improved water source (% of population with access)</td><td></td></tr><tr><td>Sanitation total</td><td>Improved sanitation facilities (% of population with access)</td><td></td></tr><tr><td>Water rural</td><td>water source, rural (% of rural population with access)</td><td></td></tr><tr><td>Water urban</td><td>water source, urban (% of urban population with access)</td><td></td></tr><tr><td>Sanitation urban</td><td>sanitation facilities, urban (% of urban population with access)</td><td></td></tr><tr><td>Sanitation rural</td><td>sanitation facilities, rural (% of rural population with access)</td><td></td></tr><tr><td>gov</td><td>Governance index</td><td>–</td></tr></tbody></table>

Source: author

The variable CO2 emissions in kilometric tons (kt) is used to proxy environmental quality (Asumadu-Sarkodie and Owusu, 2016; Avom et al., 2020). Following those studies, CO2 emissions and undernourishment are expected to disclose a direct relationship. Theoretically, as the environment deteriorates, food production should decline causing an increase in the number of people undernourishment prevalence.

To capture institutional effects on food security studies have used the civil conflicts (Kinda and Badolo, 2019) or the civil freedom index (Burke et al., 2009; Tankeu, 2021). In this study the a priori
relation is that there is a negative relationship between governance index and undernourishment. This implies that improving quality of institutions helps to alleviate undernourishment.

Food price vulnerability: Sub-Saharan Africa is the most vulnerable region to price shocks in the world (Combes et al., 2014) and this vulnerability increases food availability (Kinda and Badolo, 2019). As shown in figure 6, the built food price vulnerability index (FPV) has a negative slope, with its highest values recorded between 2001 and 2008. A negative vulnerability shock can increase household food consumption. It is in this way that sensitivity to price shocks for food creates and accelerates food insecurity. Then one expects FPV to have a positive effect on undernourishment.

Economic growth (lngdpcap) influences food demand and consumption. Soriano and Garrino (2016) find that income growth increases the purchasing power, and accelerate the effects of food policies aimed at reducing undernourishment. Analysing the food security effects of agricultural land expansion, this study foresees a negative relationship between per capita income and undernourishment.

<figure><img src="image_8.png" /><figcaption>Figure 6: Food price vulnerability-built index
Source: author</figcaption></figure>

A simple justification is owed to Keynes' psychological fundamental law, which stipulates that 'as income increases, consumption increases but at a slower rate'. When people's consumption increases as a result of increased income, the proportion of undernourished decreases.

Meddeb (2011) says factors influencing demand for food products can either promote food security or aggravate food insecurity. Molotoks et al. (2020) hold that population growth is one of the dominant drivers of undernourishment prevalence. Introduction of population growth allows to understand how demographic changes can alter the food security of Sub-Saharan Africans. Thus, the expected relationship between population growth and undernourishment is negative.

Rural population is the second demographic proxy introduced in the baseline model. Despite rural residency can positively influence undernourishment (Desyibelew and Dadi, 2019), two possible
relationships can happen. First, rural population growth can alleviate population undernourishment if it stimulates production and food availability. On the other hand, growth of the population in remote areas can depict a direct relationship with undernourishment if the population is not producing enough food to survive.

Bloem (2019) guides the present reflection on the labour movements' effects on food security. According to Timmer (2017a), there is a relationship between sectoral shifts and food security. Hypothetically, movements from agriculture to industry (indspill) reduce the proportion of undernourishment people as well as the spillover services (scespill). Indeed, movements from the agricultural sector to the service sector can worsen undernourishment, as fewer people produce.

In this pandemic context, it can be interesting to consider how infectious diseases influence the proportion of undernourished people. To proxy pandemic and epidemics, the study mobilises the prevalence of HIV (phiv) as the percentage of the population aged 15–49 years and incidence of tuberculosis (tb). Following Nchanji and Lutomia (2021). Both variables should show positive results.

# 5. Results and Discussion

The section comprises the descriptive statistics, the estimated results and robustness check.

## 5.1. Descriptive statistics

The features of all the variables are given in table 2. The panel is unbalanced as the number of observations differs from one variable to the other. On average, the prevalence of undernourishment is 21% in the study population. The lowest rate observed in South Africa between 2004 and 2011, which shows increased food security, is 3.5%. In contrast, the lowest levels of food security are observed in Angola and Sierra Leone, with undernourishment rates of almost 67.5%.

Agricultural land (aland) is approximately 50% across the sample. There is relative variability around the average. Some countries such as Nigeria in 2007 showed 81% agricultural land percentage. South Africa showed a 80% between 2001 and 2005. Based on this particular case, a direct relationship between agricultural land expansion and the prevalence of undernourishment can be expected.

The analysis of the aggregate production index variable shows an average value of 58.75. Hence, food production over the whole period and for the countries in the study's sample is just average. Looking ahead, the environment variable (CO2) in logarithm, shows an average of 8.04 kilotons (kt). The variable appears to have a small dispersion centred around this same mean, justified by a standard deviation of 1.6.
<table><caption>Table 2: Descriptive statistics</caption><thead><tr><th></th><th>N</th><th>Mean</th><th>SD</th><th>Min</th><th>Max</th></tr></thead><tbody><tr><td>Prevalence of undernourishment</td><td>594</td><td>20.70993</td><td>11.39919</td><td>3.5</td><td>67.5</td></tr><tr><td>Sanitation total</td><td>593</td><td>32.746</td><td>20.58</td><td>3.087</td><td>95.508</td></tr><tr><td>Sanitation urban</td><td>594</td><td>44.021</td><td>19.543</td><td>10.086</td><td>95.969</td></tr><tr><td>Sanitation rural</td><td>593</td><td>24.19</td><td>20.894</td><td>.892</td><td>95.185</td></tr><tr><td>Water total</td><td>594</td><td>63.383</td><td>16.087</td><td>19.713</td><td>99.867</td></tr><tr><td>Water urban</td><td>594</td><td>83.183</td><td>9.047</td><td>59.53</td><td>99.922</td></tr><tr><td>Water rural</td><td>594</td><td>49.977</td><td>17.711</td><td>6.916</td><td>99.828</td></tr><tr><td>Agricultural land</td><td>512</td><td>49.4987</td><td>17.56528</td><td>18.11414</td><td>80.92054</td></tr><tr><td>Production Index</td><td>594</td><td>58.75448</td><td>17.73957</td><td>7.88e-15</td><td>100</td></tr><tr><td>CO2 emissions</td><td>528</td><td>8.044507</td><td>1.61853</td><td>3.938431</td><td>13.12857</td></tr><tr><td>Governance Index</td><td>493</td><td>4.396999</td><td>2.259467</td><td>5.02e-16</td><td>10</td></tr><tr><td>GDP p. capita</td><td>448</td><td>7.118705</td><td>.9947559</td><td>5.272348</td><td>9.224504</td></tr><tr><td>Food price vulnerability</td><td>432</td><td>2.573883</td><td>3.198748</td><td>0</td><td>10</td></tr><tr><td>Population growth</td><td>583</td><td>0.0240085</td><td>.00988</td><td>-.0933025</td><td>0.0462968</td></tr><tr><td>Rural population</td><td>594</td><td>58.51334</td><td>16.65792</td><td>10.63</td><td>85.302</td></tr><tr><td>Industry spillover</td><td>528</td><td>-0.3830777</td><td>.2873063</td><td>-0.85659</td><td>0.29822</td></tr><tr><td>Service spillover</td><td>528</td><td>-0.1644641</td><td>.3864358</td><td>-0.80326</td><td>.6727833</td></tr><tr><td>Incidence of tuberculosis</td><td>594</td><td>328.9276</td><td>315.3675</td><td>11</td><td>1590</td></tr><tr><td>Prevalence of HIV</td><td>576</td><td>5.647396</td><td>7.392179</td><td>0.1</td><td>28.9</td></tr></tbody></table>

Source: author.

Economic growth per capita is taken as a logarithm and has a mean of 7.12 and a low standard deviation of about 1. In the sample, the logarithm of the lowest standard of living is $5.27 while the logarithm of the highest standard of living is $9.22.

Concerning demographic variables, the population growth rate (*popg*) has an arithmetic mean of 2%. The minimum shows a negative growth rate equal to (-9%) and the maximum growth rate is 4.6%. Furthermore, it is noted that the rural population is 58.51% of the total population. However, it may hide strong disparities between countries in the whole sample.

Finally, the health variables (tb and phiv) have averages of 329 per 100,000 people and 6 per 100 people respectively. Tuberculosis incidence is 1590 per 100,000 people, and the highest HIV prevalence is 29% of the population aged 15–49.

### 5.1.1. Stationarity Results

Table 3 below presents for each variable, the probability value associated with the Levin–Lin and Chu (LLC) statistic, and that associated with the Fisher (Z or L) statistics. When the P-value is less than 10%, 5% or 1%, then the variable is stationary at the chosen threshold; otherwise, the null hypothesis of non-stationarity of the variable is accepted

The table provides the results on stationarity. It reveals that all variables are stationary at level. The Levin-Lin and Chu test, which is a parametric test, does not admit variables whose data are strictly balanced. However, this test informs that undernourishment variable (*undnrsh*) as well as the variables of interest (*aland*) and control variables are all stationary at level. A more powerful
stationarity diagnosis with the Maddala and Wu test provides the same results. Indeed, this test is based on the hypothesis of a nonparametric distribution of the variables to be tested. Thus, the results obtained corroborate the LLC test results. In conclusion, all variables are stationary at level.

<table><caption>Table 3: Stationarity results</caption><thead><tr><th>Variables</th><th>LLC<br>T-stat</th><th>Fisher<br>Z or L-stat</th><th>Decision</th></tr></thead><tbody><tr><td>Undernourishment</td><td>-3.485***</td><td>-3.19***</td><td>I(0)***</td></tr><tr><td>Sanitation total</td><td>-3.52***</td><td>-6.692***</td><td>I(0)***</td></tr><tr><td>Sanitation urban</td><td>-3.34***</td><td>-1.498**</td><td>I(0)***</td></tr><tr><td>Sanitation rural</td><td>-2.04**</td><td>-1.976**</td><td>I(0)***</td></tr><tr><td>Water total</td><td>-7.012***</td><td>-4.370***</td><td>I(0)**</td></tr><tr><td>Water urban</td><td>1.873**</td><td>-2.534***</td><td>I(0)***</td></tr><tr><td>Water rural</td><td>-3.428***</td><td>-2.431**</td><td>I(0)***</td></tr><tr><td>Agricultural land</td><td>-6.037***</td><td>-2.319**</td><td>I(0)***</td></tr><tr><td>Governance Index</td><td>-5.370***</td><td>-2.642**</td><td>I(0)***</td></tr><tr><td>GDP per capita (log)</td><td>-3.534***</td><td>2.01**</td><td>I(0)***</td></tr><tr><td>Cereal land</td><td>-2.031**</td><td>-3.00**</td><td>I(0)**</td></tr><tr><td>Population growth</td><td>-15.69***</td><td>-5.52***</td><td>I(0)***</td></tr><tr><td>CO2 Emissions</td><td>0.0315***</td><td>-2.34***</td><td>I(0)***</td></tr><tr><td>Population growth</td><td></td><td>-2.04**</td><td>I(0)**</td></tr><tr><td>Rural population</td><td>-7.692***</td><td>-7.12***</td><td>I(0)***</td></tr><tr><td>Industrial spillovers</td><td>-2.498***</td><td>1.73**</td><td>I(0)***</td></tr><tr><td>Service spillovers</td><td>-1.976**</td><td>-3.28***</td><td>I(0)**</td></tr><tr><td>Food price volatility</td><td></td><td>-15.01***</td><td>I(0)***</td></tr><tr><td>Incidence of TB</td><td>-9.983***</td><td>-3.52***</td><td>I(0)***</td></tr><tr><td>Prevalence HIV</td><td>-6.388***</td><td>-11.69***</td><td>I(0)***</td></tr></tbody></table>

Source: author.

## 5.1.2. Analysis of undernourishment trends in sub-Saharan Africa

This subsection presents a graphical analysis and visually explores the links between undernourishment and the evolution of agricultural land. The panel structure of the data allows to look at these relationships from two perspectives: first, through the prism of total variation and second, through the prism of within-country variation.

### 5.1.2.1. Undernourishment and agricultural land: Overall variation

With the aid of figure 7 (next page) which focuses on the observation of the total variation, the figure shows an inverse relationship between the variables arable land and undernourishment.

<footnote>***p<0.01, **p<0.05, *p<0.1</footnote>
<figure><img src="image_9.png" /><figcaption>Figure 7: Undernourishment prevalence against land
Source: author.</figcaption></figure>

Indeed, agricultural land expansion accompanies a reduction in undernourishment in Sub-Saharan African countries (figure 7a). The slope of the fitting line, albeit flat, attests to an existing relationship. This raises doubts about agricultural expansion's ability to curb undernourishment in sub-Saharan Africa. Similarly, the second graph (Figure 7b) shows an analogous relationship between undernourishment prevalence and cereal land increase. Indeed, as more land is converted to cereal production, the dietary status of populations improves as explained by Ngoma et al. (2021). This second result supports the first, which shows that agricultural land expansion can reduce undernourishment in sub-Saharan Africa. The perception of this relationship may differ if one changes the angle of observation or analyses the intra-country variation.

5.1.2.2. Undernourishment and land expansion: Within variation

Referring to figure 8, the findings highlight a direct relationship between the two variables. The increase in agricultural land is inversely related to undernourishment. Indeed, agricultural extensification within each country is accompanied by a reduction in the number of undernourished people in these countries (figure 8a).
<figure><img src="image_10.png" /><figcaption>Figure 8: Undernourishment prevalence, within variation
Source: author.</figcaption></figure>

Equally, an increase in the area of land devoted to cereal crops is negatively associated with the number of undernourished individuals (figure 8b). This result is intuitive and similar to the one highlighted earlier in the total variation analysis. It is also supported by previous studies (Keating et al., 2014; Mughal and Sers, 2020). However, the slope appears to be steeper in the analysis of within-country variance than the slope observed in the analysis of total variation.

**5.2. Results of the estimation of the econometric model**

This subsection interprets the results of the basic model, enriched with several control variables. To assess the robustness of the parameters estimated, fixed effects (FE), random effects (RE) and the generalised method of moments (GMM) are employed. The first two methods can provide biased estimates, as they are inappropriate for the introduction of the lagged endogenous variable as an explanatory variable in the model. This is considered in the third method (GMM). The Hausman test helps choose between FE and RE estimates. Later, the two-step GMM method provides relevant parameters to shed more light on the investigated relationship. Finally, the relationship goes through numerous robustness checks.

**5.2.1. Estimation of the model with fixed and random effects and Hausman test**

The table 4 provides couples of four models, estimated with fixed and random effects respectively, to analyse how expansion of agricultural land relates to the prevalence of undernourishment in Sub-Saharan Africa.
The fixed effects (FE) estimated coefficients show a negative relationship between our dependent and main independent variables. At the outset, the expansion of agricultural land reduces undernourishment in Africa. Referring to the models (FE1 – FE4), a one-percent increase in agricultural land, relative to the total area of the country, reduces the prevalence of undernourishment by 0.35 at least. The results are significant at the threshold of 1%.

<table><caption>Table 4: Effects of agricultural land expansion on undernourishment, FE and RE</caption><caption>Dependent Variable : Prevalence of undernourishment</caption><thead><tr><th rowspan="2">Variable</th><th colspan="4">Fixed Effects (FE)</th><th colspan="4">Random Effects (RE)</th></tr><tr><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th></tr></thead><tbody><tr><td>Agricultural land</td><td>-0.415***<br>(0.115)</td><td>-0.358***<br>(0.130)</td><td>-0.398***<br>(0.126)</td><td>-0.432***<br>(0.121)</td><td>-0.194**<br>(0.0791)</td><td>-0.138<br>(0.0873)</td><td>-0.180**<br>(0.0872)</td><td>-0.229***<br>(0.0873)</td></tr><tr><td>Production Index</td><td>-0.127***<br>(0.0192)</td><td>-0.135***<br>(0.0242)</td><td>-0.0869***<br>(0.0242)</td><td>-0.0700***<br>(0.0230)</td><td>-0.148***<br>(0.0161)</td><td>-0.139***<br>(0.0212)</td><td>-0.0951***<br>(0.0213)</td><td>-0.0945***<br>(0.0210)</td></tr><tr><td>CO2 emissions</td><td>-1.633**<br>(0.820)</td><td>-1.435<br>(1.020)</td><td>-0.328<br>(1.088)</td><td>1.118<br>(1.046)</td><td>-1.319**<br>(0.673)</td><td>-1.484*<br>(0.851)</td><td>-0.699<br>(0.856)</td><td>-0.205<br>(0.845)</td></tr><tr><td>Governance index</td><td></td><td>-1.237***<br>(0.352)</td><td>-1.712***<br>(0.360)</td><td>-1.682***<br>(0.349)</td><td></td><td>-1.198***<br>(0.333)</td><td>-1.744***<br>(0.343)</td><td>-1.628***<br>(0.332)</td></tr><tr><td>GDP p capita</td><td></td><td>2.767<br>(2.590)</td><td>-1.409<br>(2.721)</td><td>-6.591**<br>(2.683)</td><td></td><td>0.201<br>(1.765)</td><td>-0.880<br>(2.039)</td><td>-1.918<br>(2.054)</td></tr><tr><td>Food price index</td><td></td><td>0.0908<br>(0.110)</td><td>0.240**<br>(0.112)</td><td>0.0712<br>(0.110)</td><td></td><td>0.0432<br>(0.106)</td><td>0.206*<br>(0.106)</td><td>0.153<br>(0.104)</td></tr><tr><td>Population Growth</td><td></td><td></td><td>298.7***<br>(65.95)</td><td>248.4***<br>(62.60)</td><td></td><td></td><td>292.5***<br>(63.82)</td><td>257.7***<br>(61.97)</td></tr><tr><td>Rural population</td><td></td><td></td><td>0.180<br>(0.126)</td><td>0.686***<br>(0.139)</td><td></td><td></td><td>0.231**<br>(0.0974)</td><td>0.442***<br>(0.107)</td></tr><tr><td>Industrial spillover</td><td></td><td></td><td>-19.49***<br>(6.678)</td><td>-14.58**<br>(6.337)</td><td></td><td></td><td>-20.02***<br>(6.260)</td><td>-14.74**<br>(6.147)</td></tr><tr><td>Services spillover</td><td></td><td></td><td>25.13***<br>(5.761)</td><td>23.53***<br>(5.436)</td><td></td><td></td><td>24.91***<br>(5.394)</td><td>22.04***<br>(5.260)</td></tr><tr><td>Incidence of tb</td><td></td><td></td><td></td><td>-0.0209***<br>(0.00320)</td><td></td><td></td><td></td><td>-0.0164***<br>(0.00282)</td></tr><tr><td>Prevalence of VIH</td><td></td><td></td><td></td><td>0.0666<br>(0.375)</td><td></td><td></td><td></td><td>0.667**<br>(0.269)</td></tr><tr><td>1.landl<br>(1 if landlocked)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>-5.020<br>(5.030)</td></tr><tr><td>Constant</td><td>61.75***<br>(8.048)</td><td>44.07**<br>(18.77)</td><td>44.19*<br>(23.97)</td><td>50.08**<br>(22.67)</td><td>49.47***<br>(6.052)</td><td>52.33***<br>(12.23)</td><td>30.35<br>(18.59)</td><td>28.86<br>(18.43)</td></tr><tr><td>Observations</td><td>512</td><td>406</td><td>406</td><td>406</td><td>512</td><td>406</td><td>406</td><td>406</td></tr><tr><td>Number of id</td><td>32</td><td>28</td><td>28</td><td>28</td><td>32</td><td>28</td><td>28</td><td>28</td></tr><tr><td>Adj R2</td><td>0.315</td><td>0.311</td><td>0.379</td><td>0.449</td><td></td><td></td><td></td><td></td></tr><tr><td>F-Stat</td><td>89.56***</td><td>35.98***</td><td>28.43***</td><td>30.74***</td><td></td><td></td><td></td><td></td></tr></tbody></table>

Standard errors in parentheses

***p<0.01, **p<0.05, *p<0.1

Source: author.

The interpretation remains the same for random effects (RE) estimates. According to the coefficients obtained by the random effects estimation technique, each model (RE1 – RE4) shows a negative relationship between agricultural land expansion and undernourishment
The Hausman test (table 5) invalidates one of the two estimators obtained from fixed or random effects. A quick comparison of the different coefficients obtained by the two estimation techniques shows that the coefficients of the variables remain essentially the same. Under the null hypothesis of the Hausman test, the difference in coefficients is not systematic.

<table><caption>Table 5: Hausman Test</caption><tr><td>b = consistent under Ho and Ha; obtained from xtreg</td></tr><tr><td>B = inconsistent under Ha, efficient under Ho;</td></tr><tr><td>Test: Ho: difference in coefficients not systematic</td></tr><tr><td>chi2(10) = 23.01</td></tr><tr><td>Prob&gt;chi2 = 0.0107</td></tr></table>

Source: author.

Statistically, the chi2(10) = 23.01 is significant at a 1% level. The result suggests that the null hypothesis of an unsystematic difference in estimators (b and B corresponding to FE and RE respectively) cannot be accepted. Therefore, there is just a 1% chance of being wrong to argue that the difference in estimators is systematic. The test validates the alternative hypothesis, which means that coefficients B obtained by the random effects (RE), are efficient and under the null hypothesis.

Although Hausman's test chose random effects rather than fixed effects (OLS), neither of them can be interpreted as they both suffer from two main limitations, related to endogeneity. Firstly, it is the relationship between the dependent variable and itself, and secondly it is the relationship between the independent variable and the dependent variable. Indeed, the use of the ordinary least squares technique and the random effects method makes it possible to identify pure correlations between the variables of a model. However, these techniques provide biased estimators because they do not address endogeneity in the model. Consequently, these estimation techniques are incompatible with dynamic model specifications.

The level of food security in the current year can be determined by its values in previous periods. This leads to the introduction of a lagged dependent variable in the model. According to Nickell (1981), in the presence of a lagged dependent variable, while OLS estimators may be biased upwards, fixed effects may be biased downwards. Anderson and Hsiao (1982) suggest a first-differenced transformation to eliminate fixed effects.

In addition to this problem, the most prominent source of endogeneity concerns the possibility of bidirectional causality between the explained variable and one or more of the explanatory variables. This work explores the effects of agricultural land expansion on food security. However, other studies
show an inverse causal link. For example, food security indicators support agricultural land expansion (Keating et al., 2014; Qi, 2014; Rulli and d'Orico, 2014; Wood et al., 2004). This reverse causality can be a source of bias. However, it is not addressed by the fixed effects model, nor the random effect model.

To take into account these partial sources of bias, the final econometric specification is based on a GMM approach (Blundell and Bond, 1998). This approach instruments the independent variables with their lagged differences and lagged levels. The GMM system was specifically developed for the estimation of dynamic panel data equations with persistent dependent variables and potentially endogenous independent variables (Bond, 2002; Arellano, 2003; and Bond et al., 2001). Considering Anderson and Hsiao (1982), we employ both difference and system GMM estimators.

### 5.2.2. Estimation of the model parameters with Generalised Method of Moments

Table 6 provides couples of four models, estimated with difference and system Generalised Method of Moment (GMM). They constitute the main results of this essay, and help to assess the undernourishment effects of agricultural land expansion in Sub-Saharan Africa.

Global assessment of the quality of the estimated model is statistically satisfactory for four reasons. Firstly, the dependent lagged variable introduced in all model turns out to be positive and lower than unit. Moreover, all the coefficients are significant at the threshold of 1%. It indicates that the model and all specifications are convergent to the estimated parameters. Secondly, the number of instruments is lower than the number of observations as required. This means there is no proliferation of instruments. Thirdly, the AR1 coefficients are significant while the AR2 are not, confirming the presence of first order autocorrelation but absence of second and subsequent orders of correlation. Fourthly, the model specifications are not overidentified, both Sargan and Hansen Over Identification Restriction (OIR) reject the null hypothesis of Over Identification.

The coefficients of our estimates are good in general. The GMM estimates show that agricultural land expansion and undernourishment are negative. This relationship is robust to the different specifications of the basic model, and to the estimation technique. In the models estimated by the GMM method in difference (1 - 4), a 1% increase in agricultural land leads to a reduction in the undernourishment prevalence in the order of 11 - 16 percentage points. The results are statistically significant at the 1% level.

A projection into the other specifications estimated using system GMM (columns 5 - 8) shows that the variable keeps its negative sign. Moreover, the significance increases. The expected effect of a one percent increase in agricultural land on undernourishment prevalence is between 6.7 and 12
percentage points. This relationship converges with studies in the literature, which argue that expanding agricultural land improves access to food (Keating et al., 2014; Rulli and D'Orico, 2014; Marselis et al., 2017). Thus, exploiting the latent land potential for agricultural purposes in Africa is a major way of combating undernourishment in the region.

<table><caption>Table 6: Effects of agricultural land expansion on undernourishment, D-GMM and S-GMM</caption><thead><tr><th rowspan="2"></th><th colspan="4">D-GMM</th><th colspan="4">S-GMM</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th><th>(8)</th></tr></thead><tbody><tr><td>Prevalence of Undernourishment</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Undernourishment, (t-1)</td><td>0.711***<br>(0.0714)</td><td>0.664***<br>(0.0802)</td><td>0.777***<br>(0.118)</td><td>0.785***<br>(0.110)</td><td>0.957***<br>(0.0665)</td><td>0.855***<br>(0.0940)</td><td>0.893***<br>(0.0710)</td><td>0.831***<br>(0.103)</td></tr><tr><td>Agricultural land</td><td>-0.119**<br>(0.0516)</td><td>-0.162*<br>(0.0881)</td><td>-0.135*<br>(0.0702)</td><td>-0.126*<br>(0.0728)</td><td>-0.108**<br>(0.0606)</td><td>-0.0678**<br>(0.0370)</td><td>-0.104**<br>(0.0503)</td><td>-0.116**<br>(0.0464)</td></tr><tr><td>Production Index</td><td>-0.0427<br>(0.053)</td><td>-0.0464***<br>(0.0147)</td><td>-0.0283*<br>(0.0171)</td><td>-0.168***<br>(0.0175)</td><td>-0.0307**<br>(0.0157)</td><td>-0.0302*<br>(0.0163)</td><td>-0.0475**<br>(0.0124)</td><td>-0.0329**<br>(0.0140)</td></tr><tr><td>CO2 emissions (log)</td><td>3.081<br>(2.864)</td><td>0.103<br>(0.854)</td><td>0.866<br>(1.139)</td><td>1.057*<br>(0.642)</td><td>0.842<br>(0.661)</td><td>1.208*<br>(0.674)</td><td>0.963<br>(0.750)</td><td>1.566**<br>(0.766)</td></tr><tr><td>Governance index</td><td></td><td>-0.454*<br>(0.285)</td><td>-0.404*<br>(0.236)</td><td>-0.346*<br>(0.209)</td><td></td><td>-0.0778<br>(0.250)</td><td>-0.465*<br>(0.258)</td><td>-0.613**<br>(0.285)</td></tr><tr><td>GDP per capita</td><td></td><td>2.467<br>(2.200)</td><td>0.848<br>(2.148)</td><td>0.437<br>(1.660)</td><td></td><td>0.797<br>(1.192)</td><td>2.060<br>(1.731)</td><td>1.038<br>(1.325)</td></tr><tr><td>Food price vulnerability</td><td></td><td>0.133**<br>(0.0723)</td><td>0.135**<br>(0.0592)</td><td>0.119**<br>(0.0503)</td><td></td><td>0.116***<br>(0.0323)</td><td>0.125**<br>(0.0541)</td><td>0.105**<br>(0.0490)</td></tr><tr><td>Population growth</td><td></td><td></td><td>6.30***<br>(2.05)</td><td>6.64*<br>(4.35)</td><td></td><td></td><td>5.760***<br>(2.42)</td><td>3.102**<br>(1.466)</td></tr><tr><td>Rural population growth</td><td></td><td></td><td>0.0478*<br>(0.0316)</td><td>0.0413**<br>(0.0236)</td><td></td><td></td><td>0.0929*<br>(0.0525)</td><td>0.166*<br>(0.101)</td></tr><tr><td>Industrial spillovers</td><td></td><td></td><td>-0.127***<br>(0.0346)</td><td>-0.526*<br>(0.315)</td><td></td><td></td><td>-0.428**<br>(0.214)</td><td>-0.253**<br>(0.107)</td></tr><tr><td>Services spillovers</td><td></td><td></td><td>0.308**<br>(0.1187)</td><td>1.890<br>(1.256)</td><td></td><td></td><td>0.2741**<br>(0.1246)</td><td>0.140***<br>(1.354)</td></tr><tr><td>Incidence of tb</td><td></td><td></td><td></td><td>-0.0128*<br>(0.00843)</td><td></td><td></td><td></td><td>-0.00929*<br>(0.00547)</td></tr><tr><td>Prevalence of hiv</td><td></td><td></td><td></td><td>0.514**<br>(0.207)</td><td></td><td></td><td></td><td>0.730***<br>(0.258)</td></tr><tr><td>Constant</td><td></td><td></td><td></td><td></td><td>-0.524<br>(4.861)</td><td>-8.362<br>(10.55)</td><td>-21.41**<br>(10.58)</td><td>-17.10*<br>(9.801)</td></tr><tr><td>Observations</td><td>448</td><td>378</td><td>378</td><td>378</td><td>480</td><td>406</td><td>406</td><td>406</td></tr><tr><td>Number of id</td><td>32</td><td>28</td><td>28</td><td>28</td><td>32</td><td>28</td><td>28</td><td>28</td></tr><tr><td>Instruments</td><td>8</td><td>11</td><td>13</td><td>15</td><td>10</td><td>12</td><td>16</td><td>19</td></tr><tr><td>AR1</td><td>0.094</td><td>0.087</td><td>0.053</td><td>0.099</td><td>0.0397</td><td>0.0799</td><td>0.0688</td><td>0.037</td></tr><tr><td>AR2</td><td>0.501</td><td>0.591</td><td>0.913</td><td>0.882</td><td>0.549</td><td>0.763</td><td>0.0763</td><td>0.754</td></tr><tr><td>Sargan OIR</td><td>0.2142</td><td>0.2389</td><td>0.214</td><td>0.1109</td><td>0.198</td><td>0.1806</td><td>0.1285</td><td>0.1152</td></tr><tr><td>Hansen OIR</td><td>0.1674</td><td>0.2395</td><td>0.1746</td><td>0.1785</td><td>0.102</td><td>0.204</td><td>0.2463</td><td>0.1439</td></tr><tr><td>chi2</td><td>162.9</td><td>427.6</td><td>1636</td><td>1561</td><td>1644</td><td>1630</td><td>2625</td><td>1675</td></tr></tbody></table>

Robust standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

According to the physiocracy, the expansion of agricultural land increases support for agriculture and livestock. The output of these products can easily reach the hands of the population through various channels. Our knowledge of the relevant channels remains limited.

Several countries resort to the acquisition of foreign land to fight undernourishment, thanks to the export of food products to the country of origin (Marselis et al., 2017). Acquiring such land for food sovereignty purposes is therefore necessary, as this coefficient indicates.
Besides, the expansion of land is accompanied by an increase in other inputs and factors of production, which ultimately leads to higher production than in the past. This is known as constant or increasing returns to scale. Meaning that, the increase in production, in turn, improves food availability at the local, national or sub-regional levels. This result corroborates the evidence from developing countries that expanding agricultural land reduces the prevalence of undernourishment. (Mughal and Sers, 2020; Kinda and Badolo, 2019; Ramankutty et al., 2018; Marselis et al. 2017) but contradicts others like (Mechiche-Alami et al., 2021; Zabala, 2021; Galeana-Pizaña et al., 2018; Sauer; 2018). Indeed, an increase in agricultural land allows for more agricultural production, thus increasing calories intake for people (Santangelo, 2018), thereby leading to a decrease in undernourishment.

The analysis of the food production index makes it possible to empirically validate the existence of an inverse relationship between the said variable and the prevalence of undernourishment in sub-Saharan Africa. Indeed, an increase in the production index (agricultural and agro-pastoral) lowers the undernourishment rate in Africa. This is supported by all model specifications (1 – 8). The increase of the production index by one unit, will reduce the prevalence of undernourishment by 4 – 16 percentage points with the D-GMM and by 3.02 – 4.7 percentage points, with the S-GMM. Majority of these coefficients have a significance lower or equal to 10%. Although more general, this result is similar to that of the literature (Mughal and Sers, 2021; Ray, 2007), which show that cereal production reduces the proportion of undernourished. Possible explanation is that agricultural production is supported by both land fragmentation and diversified food crop production (Caian et al., 2018) which improve productivity (Deininger et al., 2012) and people nutritional status (Keating et al., 2014). However, it contradicts the findings of Purwestri et al. (2016) which concluded that increases in food production increase sometimes result in high levels of undernourishment, when food is exported to foreign markets.

Variable CO2 emissions provides a better understanding of the environmental factors that affect the prevalence of undernourishment in Africa. The results show that environmental quality increases undernutrition rates. Significance is found in models 4, 6 and 8, while the rest maintain positive signs. The results demonstrated that an increase of kiloton of carbon by one percent is accompanied by an increase in the number of undernourished people in the range of 0.01 to 0.016 percentage points. This result corroborates those obtained by Sarcodie and Owusu (2016) and Baker and Antilla-Huges (2020). They find that climate variability and CO2 emissions raise temperatures.. In consequence, the number of droughts and floods increases with three main outcomes: fewer pollinators, higher rate of microbial growth and lower nutritional value of zinc and iron-rich foods. They respectively shrink
food availability, and disturbs food access patterns (Baker and Anttila-Hughes, 2020; Eckelman and Sherman, 2018). This extra trapped heat disrupts many of the interconnected systems in our environment and justify the increased proportion of undernourished people in sub-Saharan Africa. However, this relationship is contrary to that found in Kinda and Badolo (2019).

The introduction of the institutional quality variable (Gov) is done in models 2–4 and models 5–8. These models show that governance varies in the opposite direction with undernourishment. Indeed, an improvement in the governance quality significantly reduces the prevalence of undernourishment by about 0.35 to 0.45 percentage points (with D-GMM) or by about 0.47 to 0.61 percentage points (with S-GMM). This reduction is significant at 10%. Moreover, the relationship is robust to several specifications. These results are consistent with literature (Ecker and Breisinger, 2012; Rodrik et al., 2004). What justifies this is the centrality of government programming, and the fight against corruption, incivism and the rule of law. They are all catalysts and key regulatory factors that ensure security of people and their investments. In addition, good governance ensures that state enablers (such as finance) are allocated efficiently and timely manner to producers. As a result, the agricultural calendar can be followed and production increased.

The analysis considers two economic variables: growth in gross domestic product per capita and vulnerability to food prices. The estimated coefficients show that the relationship between economic growth and undernourishment is robust in sign. However, the coefficients in all models are not significant. As a result of the increase in GDP/head, there is no effect on the prevalence of undernourishment.

This result shows that the increase in per capita income is not accompanied by an improvement in malnutrition. This can be justified by low economic growth. Malthus' theory maintains that economic growth at an arithmetical rate is incapable of satisfying the resource needs of a population whose growth follows a geometric rate. On the other hand, on the operational level, the result may be the poor redistribution of growth fruits. An allocation of resources that allocates an insignificant share to agriculture finances very little agriculture development. Insufficient public funding hinders research into improved crops adapted to different agro-ecological zones. The productivity of old crops can be reduced by diseases (local or imported). Thus, the improvement in growth, which does not benefit producers, does not improve production either quantitatively or qualitatively, hence the malnutrition. The relationship detected is different from the positive relationship identified in the literature (Soriano and Garrino, 2016; Kinda and Badolo, 2019).

To assess the effect of food price vulnerability on undernourishment in Africa, the study uses the price vulnerability index. The estimated coefficients show a direct relationship between the two
variables. An increase in the food price vulnerability index is accompanied by an increase in the number of undernourished people from 0.12 to 0.14 percentage points with the D-GMM or from 0.11 to 0.13 with the S-GMM. One justification for this relationship is that individuals may lose access to food if its price changes. Such a change may distort food consumption when prices are highly sensitive as in sub-Saharan Africa. All coefficients are significant at the 5% threshold. This result is similar to Combes et al. (2014) who show that a negative shock to price vulnerability increases household consumption.

The third model specification accounts for demographic variables: population growth and rural population growth rates as well as labour movement (industrial and services spillovers).

The estimated parameters of the demographic variables are in line with Malthusian theory. An in population growth exacerbates undernutrition. As the population grows, the proportion of undernourished people rises. This result is significant at the 5% level, and can be justified by the precariousness of agricultural production in sub-Saharan Africa, and the low yields obtained from producers. Thus, states high population growth in Africa face important food challenges. This result is similar to that obtained by Tankeu (2021) who shows that population growth reduces food availability in Cameroon. However, the high values of the coefficients show that they should be interpreted with caution.

For the rural population growth variable, it is noted that when it increases by one percentage point, the prevalence of undernourishment increases in a range of 0.047 and 0.16 percentage points. The relationship is robust and only the result of model 4 is significant at 1% level, the others being significant at the 5% threshold. The results are justified because poverty in Africa is mainly a rural phenomenon. Desyibelew and Dadi (2019) show in their microeconomic analysis that living in rural areas increases the risk of undernourishment. A growing rural population increases the number of undernourished people, further destabilizing a rural population with fragile undernourishment status and thus increasing the size of undernourished communities. This result is similar to Tankeu (2021) who shows that in Cameroon, population growth exacerbates food insecurity, captured by price vulnerability.

The observation of structural change variables once again highlights their importance to undernourishment. It emerges that the structural change variables are significant, with a negative sign for virtuous structural change and a positive sign for vicious structural change. The flow of labour from the agricultural sector to the industrial sector (industrial spillover) reduces the prevalence of undernourishment by between 0.13 and 0.53 percentage points. These results are robust and significant at the 1% level. Indeed, the development of agribusiness and agro-industries is essentially
based on agricultural commodities. Justification lies in the reciprocal stimulation of agricultural and industrial products. Thus, industrial activities stimulates agricultural production and contributes to the population's access to manufactured or raw products.

However, when labour shifts from the agricultural sector to the service sector (services spillovers), undernourishment prevalence increases between 0.14 and 0.30 percentage points. All these results are robust and significant at the 5% level. The service sector in sub-Saharan Africa is mainly made up of informal activities, with low productivity, and without spillover effects on agricultural productivity. Food insecurity is caused by weak service sector development. This sector is generally consumer and informal, rather than productive and formal. It is said to be consumer-based because it is not productive, and is predominantly informal, with activities centred on redistribution, small-scale retailing and street vending. As a result, agricultural productivity decreases and increase the number of undernourished people.

The plausible explanation is that there is a lack of integration and connection between the agricultural sector and the service sector. The latter is mainly made up of informal activities, with low productivity, and without spillovers effects on the agricultural sector productivity. As a result, agricultural productivity decreases and causing food scarcity and increasing undernourishment. This lack of integration between the primary and tertiary sectors is rather natural for the primary and secondary sectors (Timmer, 2017a, 2017b). This is evidenced by the emergence of small start-ups and agribusiness firms that process agricultural products and stimulate production in the agricultural sector. Moreover, the dissemination of manufactured agricultural products is a major asset in the fight against undernourishment.

This result sheds light on the imprecise conclusion of Bloem (2019). It would be beneficial to promote structural change towards the industrial sector, towards the emergence of agri-industries and the full development of value chains. It is this strategy that would significantly reduce under-indebtedness in sub-Saharan Africa. These results extend and bring clarity to the study of Bloem (2019) as it specifies which type of structural change leads to the reduction of food security.

In columns 4 and 8, the epidemiological factor is taken into account. The variable tuberculosis incidence and HIV prevalence are epidemiological situations that can account, even approximately, for the effect of the COVID-19 pandemic on undernourishment in sub-Saharan Africa. Two significant and contradictory results emerge from the estimated parameters.

The tuberculosis incidence variable has a negative effect on undernourishment in Africa. This effect is significant at the 10% level. The result is contrary to both theory and intuition. The explanation
provided is that tuberculosis is a chronic disease with a high mortality rate. The first result shows that an increase in the TB epidemic contributes to a reduction in undernourishment. One reason for this is that tuberculosis is the single most common cause of death among single infectious diseases just before HIV/AIDS (World Health Organization, 2022). Approximately 500,000 people die of TB per year in Africa. The negative coefficient can be explained if the TB mortality rate is higher than the prevalence rate of undernourishment. Mortality due to this disease, considered as 'positive checks' in the Malthusian analysis, can explain the demographic regression. This can explain the improvement of undernourishment statistics.

The second result establishes that HIV prevalence produces an intuitive relationship. Indeed, precarious health of individuals makes them unfit for agricultural production, which still relies on physical strength in Africa. It can be seen that in a pandemic situation, the prevalence of undernourishment will increase, due to the drop in productivity, which is itself due to the psychological shock that individuals undergo. In addition to this, confinement over several months also causes the abandonment of crops or significant farming operations, ultimately reducing agricultural production and undernourishment. These results are significant at a level of 1% and corroborate those of Nchanji and Lutomia (2021) and Arouna et al. (2020). The authors show that the COVID-19 pandemic generates disturbances at the field level. These disruptions are transmitted along agri-food chains such as market access, logistics and confinements that impose border restrictions. Ultimately, the impact on the household comes from reduced economic activity, household income, food instability and access, and poverty.

In this sub-section, the suitable tools mobilised for estimation reveal that our theoretically assumption is confirmed. The expansion of agricultural land can help curb food insecurity in Africa, as well as good institutions. This result may change if other assumptions are relaxed. Therefore, other estimation techniques are employed to assess the effect of agricultural land expansion on food security in the next subsections.

The robustness subsection provides robustness analyses by giving on the one hand, estimations of five-year average data and using additional variables of agricultural land expansion and on the other, analysis of the nexus between agricultural land expansion and food security, using additional variables to capture food security.

### 5.2.3. Robustness check: averaged data and other agricultural land variables

The robustness subsection allows to improve the statistical adequation of the previous estimated parameters. It starts by giving the five-years averaged data estimations, then, estimations with other agricultural land expansion variables.
### 5.2.3.1. Effects of agricultural land expansion on undernourishment averaged year data

The present results aim to verify the robustness of the previous estimates. Indeed, they show the robustness of the latter. Two observations stand out in table 7, despite the consistent negative relationship between agricultural land expansion and undernourishment.

<table><caption>Table 7: Effects of agricultural land expansion on undernourishment with 5 years average</caption><thead><tr><th rowspan="2">Variable</th><th colspan="8">Dependent variable: Prevalence of undernourishment</th></tr><tr><th colspan="4">D-GMM</th><th colspan="4">S-GMM</th></tr></thead><tbody><tr><td>Undernourishment (t-1)</td><td>0.382***<br>(0.107)</td><td>0.392***<br>(0.137)</td><td>0.426***<br>(0.131)</td><td>0.385***<br>(0.145)</td><td>0.977***<br>(0.279)</td><td>0.409**<br>(0.203)</td><td>0.872***<br>(0.128)</td><td>0.437**<br>(0.0978)</td></tr><tr><td>Agricultural land</td><td>-0.603***<br>(0.0429)</td><td>-0.345***<br>(0.0324)</td><td>-0.459***<br>(0.104)</td><td>-0.531***<br>(0.165)</td><td>-0.615*<br>(0.331)</td><td>-0.337**<br>(0.0561)</td><td>-0.106***<br>(0.0389)</td><td>-0.345**<br>(0.0778)</td></tr><tr><td>Production Index</td><td>-0.0649***<br>(0.0150)</td><td>-0.064***<br>(0.00429)</td><td>-0.0901***<br>(0.0105)</td><td>-0.01564**<br>(0.00665)</td><td>-0.0881<br>(0.0646)</td><td>-0.0627***<br>(0.00436)</td><td>0.0108<br>(0.0602)</td><td>-0.0432*<br>(0.0103)</td></tr><tr><td>CO2 emissions</td><td>2.205<br>(2.618)</td><td>-1.475<br>(1.764)</td><td>3.024*<br>(1.814)</td><td>4.446**<br>(1.837)</td><td>1.952<br>(3.310)</td><td>-1.370<br>(2.488)</td><td>2.684***<br>(0.579)</td><td>5.995<br>(5.058)</td></tr><tr><td>Governance index</td><td></td><td>-2.346***<br>(0.0664)</td><td>-2.022***<br>(0.0127)</td><td>-1.912***<br>(0.235)</td><td></td><td>-2.321***<br>(0.129)</td><td>-1.687***<br>(0.478)</td><td>-1.351***<br>(0.487)</td></tr><tr><td>GDP per capita</td><td></td><td>18.67***<br>(0.284)</td><td>12.67***<br>(2.393)</td><td>7.269**<br>(2.878)</td><td></td><td>18.69***<br>(0.356)</td><td>3.167***<br>(1.126)</td><td>0.652<br>(3.574)</td></tr><tr><td>Food price vulnerability</td><td></td><td>1.243***<br>(0.171)</td><td>1.300**<br>(0.595)</td><td>1.185**<br>(0.603)</td><td></td><td>1.262**<br>(0.247)</td><td>0.724<br>(1.103)</td><td>1.194<br>(0.716)</td></tr><tr><td>Population growth</td><td></td><td></td><td>630.5***<br>(28.79)</td><td>600.1***<br>(68.69)</td><td></td><td></td><td>233.3*<br>(135.9)</td><td>408.3**<br>(63.74)</td></tr><tr><td>Rural pop. growth</td><td></td><td></td><td>0.379***<br>(0.0515)</td><td>0.582***<br>(0.0452)</td><td></td><td></td><td>0.207***<br>(0.0641)</td><td>0.450<br>(0.396)</td></tr><tr><td>Industrial spillovers</td><td></td><td></td><td>1.341***<br>(0.497)</td><td>-1.304<br>(1.021)</td><td></td><td></td><td>-12.62<br>(9.184)</td><td>-6.881<br>(3.852)</td></tr><tr><td>Services spillovers</td><td></td><td></td><td>3.672<br>(3.575)</td><td>7.391<br>(5.170)</td><td></td><td></td><td>11.97<br>(7.285)</td><td>6.646<br>(2.361)</td></tr><tr><td>Incidence of TB</td><td></td><td></td><td></td><td>-0.0067***<br>(0.00159)</td><td></td><td></td><td></td><td>-0.0121*<br>(0.00328)</td></tr><tr><td>Prevalence of HIV</td><td></td><td></td><td></td><td>-0.335<br>(0.751)</td><td></td><td></td><td></td><td>-0.163<br>(0.953)</td></tr><tr><td>Constant</td><td></td><td></td><td></td><td></td><td>33.94<br>(23.45)</td><td>-81.70<br>(31.13)</td><td>-4.756<br>(12.96)</td><td>-58.49<br>(105.4)</td></tr><tr><td>Observations</td><td>64</td><td>55</td><td>55</td><td>55</td><td>96</td><td>83</td><td>83</td><td>83</td></tr><tr><td>Number of id</td><td>32</td><td>28</td><td>28</td><td>28</td><td>32</td><td>28</td><td>28</td><td>28</td></tr><tr><td>Instruments</td><td>6</td><td>9</td><td>13</td><td>15</td><td>6</td><td>8</td><td>15</td><td>13</td></tr><tr><td>AR1</td><td>0.0564</td><td>0.0584</td><td>0.0349</td><td>0.0665</td><td>0.0897</td><td>0.0858</td><td>0.0779</td><td>0.0895</td></tr><tr><td>AR2</td><td>0.658</td><td>0.365</td><td>0.549</td><td>0.744</td><td>0.141</td><td>0.149</td><td>0.145</td><td>0.605</td></tr><tr><td>Sargan OIR</td><td>0.405</td><td>0.642</td><td>0.792</td><td>0.785</td><td>0.335</td><td>0.652</td><td>0.338</td><td>0.642</td></tr><tr><td>Hansen OIR</td><td>0.1261</td><td>0.2472</td><td>0.1427</td><td>0.2015</td><td>0.1179</td><td>0.2212</td><td>0.1333</td><td>0.1492</td></tr><tr><td>F</td><td></td><td></td><td></td><td></td><td>7.799***</td><td>670***</td><td>15.98***</td><td>11300***</td></tr><tr><td>chi2</td><td>709***</td><td>701***</td><td>479.6***</td><td>76.31***</td><td></td><td></td><td></td><td></td></tr></tbody></table>

Robust standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

First, the coefficients are larger (in absolute values) than in the previous table. Thus, a one percent expansion of agricultural land leads to a reduction in undernourishment prevalence of between 1 and 6 percentage points. This effect is significant over five (5) years. Secondly, it is important to note that the significance of the agricultural land expansion variable is increased when short-term
fluctuations are eliminated. This outcome confirms the theory of land expansion for agricultural activities as a mean of reducing food insecurity.

For other variables such as the production index, the relationship remains negative. This indicates that agricultural and pastoral production continue to significantly affect undernourishment. As for CO2 emissions, there is a positive relationship with undernourishment. This relationship is justified by the risk that global warming poses to food security. The main consequence of greenhouse gases spread in the atmosphere is global warming. This is followed by season disruption, which reduces agricultural and agro-pastoral production and exacerbates food insecurity for populations.

Other variables such as governance, the price vulnerability index and the incidence of tuberculosis maintain the same relationship with undernourishment prevalence.

First, the coefficients are larger (in absolute values) than in the previous table. Thus, a one percent expansion of agricultural land leads to a reduction in malnutrition of between 1 and 6 percentage points. This effect is significant over five (5) years. Secondly, the significance of the agricultural land expansion variable is increased when short-term fluctuations are eliminated. This finding supports the theory of land expansion for agricultural activities to reduce food insecurity.

Similarly, variables such as governance, the price vulnerability index and the incidence of tuberculosis maintain the same relationship with the prevalence of undernourishment.

#### 5.2.3.2. Robustness check: alternative measures of agricultural land expansion

This subsection assesses the undernourishment effects of agricultural land expansion, measured alternatively. For instance, arable land, cereal land, forest land area and land under permanent crop. Table 8 shows that all variables previously cited have a negative impact on food insecurity.

Forest area and arable land have the greatest coefficients. Forest areas are still a source of food production and pastoral production. By contrast, forests help prevent global warming by reducing CO2 and greenhouse gas emissions. This improves food production and the reduce food insecurity.

Arable land is a source of crop production consumed daily by populations independently of their milieus. Almost all crop products are raised more than once a year. Therefore, expanding arable land is a way of providing markets and populations with food crops and stuffs to ensure nutrition. This is the reason why expanding land for cereal production has a positive effect and may reduce undernourishment, as in most of the Sahelian part of the continent (Senegal, Mali, Niger, North Cameroon, Chad...) grains are a favourite ingredient of traditional meals, as found by Mughal and Sers (2021).