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The parable of arable land: Characterizing large scale land acquisitions through network analysis.

Authors
  • Interdonato, Roberto1, 2
  • Bourgoin, Jeremy1, 2
  • Grislain, Quentin1, 2
  • Zignani, Matteo3
  • Gaito, Sabrina3
  • Giger, Markus4
  • 1 Cirad, TETIS, Montpellier, France. , (France)
  • 2 TETIS, Univ. of Montpellier, APT, Cirad, CNRS, INRAE, Montpellier, France. , (France)
  • 3 Dipartimento di Informatica, Università degli Studi di Milano, Milan, Italy. , (Italy)
  • 4 Centre for Development and Environment (CDE), University of Bern, Bern, Switzerland. , (Switzerland)
Type
Published Article
Journal
PLoS ONE
Publisher
Public Library of Science
Publication Date
Jan 01, 2020
Volume
15
Issue
10
Identifiers
DOI: 10.1371/journal.pone.0240051
PMID: 33048955
Source
Medline
Language
English
License
Unknown

Abstract

Land is a scarce resource and its depletion is related to a combination of demographic and economic factors. Hence, the changes in dietary habits and increase in world population that upturn the food demand, are intertwined with a context of increasing oil prices and rise of green capitalism that in turn impacts the demand in biofuel. A visible indicator of these phenomena is the increase, in recent years, of Large Scale Land Acquisitions (LSLAs) by private companies or states. Such land investments often lead to conflicts with local population and have raised issues regarding people's rights, the role of different production models and land governance. The aim of this work is to show how publicly available data about LSLAs can be modeled into complex network structures, thus showing how the application of advanced network analysis techniques can be used to better understand land trade dynamics. We use data collected by the Land Matrix Initiative on LSLAs to model three land trade networks: a multi-sector network, a network centered on the mining sector and a network centered on the agriculture one. Then we provide an extended analysis of such networks which includes: (i) a structural analysis, (ii) the definition of a score, namely LSLA-score, which allows to rank the countries based on their investing/target role in the land trade network, (iii) an analysis of the land trade context which takes into account the LSLA-score ranking and the correlation between network features and several country development indicators, (iv) an analysis centered on the discover and analysis of network motifs (i.e., recurring patterns in the land trade network), which provides insights into complex and diverse relations between countries. Our analyses showed how the land trade market is massively characterized by a Global North-Global South dynamic, even if the investing power of emerging economies also has a major impact in creating relations between different sub-regions of the world. Moreover, the analyses on the mining and agriculture sectors highlighted how the role of several countries in the trade network may drastically change depending of the investment sector, showing diverse hierarchies between investor, intermediate and target countries.

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