Tag: multivariate analysis

Food security in the world: Disparities and opportunities by country income levels

This article examines the performance of ten food security indicators across 91 countries in the world, categorised by their income levels, to identify differences and similarities. The variations and covariations observed in a multivariate way are outlined through Biplot plots that summarise the results of a Principal Component Analysis (PCA). The results show a direct link between the economic factors of the countries, food security, nutrition, and its derivatives. High-income countries are the best place for their populations to access a nutritious and quality food supply to meet the dietary energy needs needed for an active life. In contrast, low- and lower-middle-income countries still have critical indicators of the prevalence of severe or moderate food insecurity, malnutrition, and other related diseases, such as anaemia.

Sustainability levels in Irish dairy farming: a farm typology according to sustainable performance indicators

Feeding the world’s population in a sustainable manner is one of the key challenges facing the future of global agriculture. The recent removal of the milk quota regime in the European Union has prompted an expansionary phase in dairy farming, especially in Ireland. Achieving this expansion in a sustainable manner is crucial to the long-term survival and success of the Irish dairy sector. In this paper we examine the sustainability of Irish dairy farming, defining ‘sustainability’ as economically profitable, environmentally friendly and socially efficient. A typology of Irish dairy farms has been created using data on profitability, environmental efficiency and social integration derived from the Teagasc National Farm Survey. Economic, social and environmental performance indicators were determined and aggregated and then used in a multivariate analysis for the identification and classification of farm clusters. The purpose of this study to classify Irish dairy farms using performance indicators, thereby, assisting policy makers in identifying patterns in farm performance with a view to formulating more targeted policies. Two of the three clusters elicited from the analysis were similar in regards to their respective indicator scores. However, the remaining cluster was found to perform poorly in comparison. The results indicate a clear distinction ...

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  • Scopus SJR (2023): 0.29
  • Scopus CiteScore (2022): 2.0
  • WoS Journal Impact Factor (2023): 0.9
  • WoS Journal Citation Indicator (2023): 0.33
  • ISSN (electronic): 2063-0476
  • ISSN-L 1418-2106



Publisher Name: Institute of Agricultural Economics Nonprofit Kft. (AKI)

Publisher Headquarters: Zsil utca 3-5, 1093-Budapest, Hungary

Name of Responsible Person for Publishing:        Dr. Pal Goda

Name of Responsible Person for Editing:             Dr. Attila Jambor

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

The publication cost of the journal is supported by the Hungarian Academy of Sciences.

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