Associations between global indices of risk management and agricultural development

Elesandro Bornhofen, Thiago Gentil Ramires*, Tábata Bergonci, Luiz Ricardo Nakamura, Ana Julia Righetto

*Corresponding author for this work

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review

19 Citations (Scopus)

Abstract

Different countries around the globe have different levels of vulnerability to risks because of several factors, e.g. degree of development, governance, infrastructure, among others. The probability of occurrence of certain risks as drought and unfavorable tax policies have a direct impact on the development of the agribusiness in a given country. Hence, the aim of this study is to combine a set of risk management indices in a global scale with agribusiness performance indicators, focusing on the 96 most relevant countries regarding the agribusiness GDP (Gross domestic product). We selected 27 indicators for risk management collected from the InfoRM database and seven for agribusiness performance from the FAOSTAT database. All data used in this research are public available. The data were scaled, and then analyzed through multivariate techniques, specifically using principal component analysis (displayed in biplots) and unsupervised K-means clustering in R software. The results suggest that monitoring the indicators of risk management (InfoRM) and the establishment of strategies to shrink them may have a positive effect on the agribusiness performance of a given country. For the agribusiness improvement, nations should elaborate strategies for the joint enhancement of the indicators discussed here, observing the existing associations. The implications of the use of risk management indexes and agricultural performance indicators are discussed.

Original languageEnglish
JournalAgricultural Systems
Volume173
Pages (from-to)281-288
Number of pages8
ISSN0308-521X
DOIs
Publication statusPublished - Jul 2019

Keywords

  • Agribusiness
  • Biplot
  • Cluster analysis
  • FAOSTAT
  • InfoRM
  • Principal component analysis

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