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Energy and land use in worldwide agriculture: an application of life cycle energy and cluster analysis

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Abstract

Agriculture is expected to provide food in a sustainable manner while also partially contributing to the energy problem as well as to bio-material supply. Moreover, fossil fuels scarcity calls for an increase of energy efficiency in agricultural processes. This study evaluates patterns, trends, driving factors and trade-offs of energy use in selected agricultural systems and aims at grouping them into clusters with similar energy and social performances. Results show that in 2010 the highest power densities and energy intensities of production are found by crop sector of cluster 5 (China: 59.19 GJ/ha, 15.29 MJ/kg dm) and cluster 3 (Japan: 50.11 GJ/ha, 12.32 MJ/kg dm) as well as by livestock sector of cluster 3 (Japan: 328.47 GJ/ha, 103.08 MJ/kg dm), while the lowest values in clusters 2 and 4, including selected developing countries and USA. Cluster 3 (Japan) also shows the lowest energy intensity of economic value of crops (2.75 MJ/$), while cluster 5 (China) the highest one (23.96 MJ/$). Cluster analysis also sheds light on trends, identifying two groups: cluster 1*, gathering most European countries, USA and Japan, characterized by a decreasing trend of all energy indicators; and cluster 2*, including developing countries, the Netherlands and Spain, characterized by an increasing trend of indicators. Results highlight the importance of an integrated framework for evaluating energy use as well as of a multi-criteria approach to understand the trade-offs and interplay of performance indicators.

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Notes

  1. http://legroupindustries.com/top-10-exporters-of-soybeans-and-soybean-meals-by-country/.

  2. It has obtained from FAOSTAT data, and it is the sum of harvest area for each crop and permanent meadows and pastures area.

  3. Directive 0676/1991.

  4. Directive 0060/2000.

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Ghisellini, P., Setti, M. & Ulgiati, S. Energy and land use in worldwide agriculture: an application of life cycle energy and cluster analysis. Environ Dev Sustain 18, 799–837 (2016). https://doi.org/10.1007/s10668-015-9678-2

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