Evidence map›Paper›PMID 33048955›Full record

ArticlePloS one2020

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

Roberto Interdonato, Jeremy Bourgoin, Quentin Grislain, Matteo Zignani, Sabrina Gaito, Markus Giger

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
3.7field-weighted citation impact, top 6% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 14 citations in OpenAlex.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 3 institutions in 3 countries.

Roberto InterdonatoCirad, TETIS, Montpellier, France.ORCID 0000-0002-0536-6277
Jeremy BourgoinCirad, TETIS, Montpellier, France.
Quentin GrislainCirad, TETIS, Montpellier, France.
Matteo ZignaniDipartimento di Informatica, Università degli Studi di Milano, Milan, Italy.
Sabrina GaitoDipartimento di Informatica, Università degli Studi di Milano, Milan, Italy.
Markus GigerCentre for Development and Environment (CDE), University of Bern, Bern, Switzerland.ORCID 0000-0002-2284-8032
Centre National de la Recherche Scientifique · FRUniversity of Milan · ITUniversity of Bern · CH

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Neural Networks, ComputerAgricultureDatabases, FactualInternationalityNatural Resources

Identifiers

PMID33048955
PMCPMC7553289
OpenAlexW3092628181

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.