Clustering countries of the world according to their business practices in agriculture

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The study aims to cluster countries worldwide by business practices in the agrosector to reveal trends and specifics in applying sustainable methods in agrobusiness management. The analysis covers 26 countries from the OECD database as of 2021. The Word and k-means clustering methods are based on General Services Support Estimate indicators from the OECD: share of agricultural knowledge and innovation system, share of inspection and control, share of development and maintenance of infrastructure, share of cost of public stockholding, which has a determining, statistically significant influence on the formation of clusters. The first cluster included three Asian countries; China is the leader (share of agricultural knowledge and innovation system – 6,529.7 million USD, share of inspection and control – 3177.9 million USD, share of development and maintenance of infrastructure – 12,874.7 million USD, share of cost of public stockholding – 14,668.5 million USD). The second cluster comprised six countries, with the USA as the leader (share of agricultural knowledge and innovation system – 2,908.4 million USD, share of inspection and control – 1,298.0 million USD, share of development and maintenance of infrastructure – 2,392.5 million USD). The third cluster has 17 countries, with Canada being singled out (share of inspection and control – 631.8 million USD and share of agricultural knowledge and innovation system – 683.1 million USD). The results indicate the diversity of countries’ approaches to support and develop their agrosector. Advanced Asian countries and the US invest significant resources in innovation, infrastructure development, and quality control, underscoring their commitment to food security, efficiency, and sustainability.

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    • Figure 1. Dendrogram of redistribution of countries between clusters according to Ward’s method
    • Figure 2. Average values of indicators GSSE1-GSSE6 within selected clusters by k-means clustering method
    • Figure 3. Dendrogram of the redistribution of countries between clusters according to the Ward method with GSSE4 and GSSE6 indicators excluded
    • Figure 4. Average values of indicators GSSE1-GSSE3 and GSSE5 within selected clusters by k-means clustering method
    • Figure 5. Clustering GSSE1-GSSE3 and GSSE5 indicators for the countries included in the first cluster
    • Figure 6. Clustering GSSE1-GSSE3 and GSSE5 indicators for countries included in the second cluster
    • Figure 7. Clustering GSSE1-GSSE3 and GSSE5 indicators for countries included in the third cluster
    • Table 1. Input data
    • Table 2. Variance analysis
    • Table 3. Variance analysis excluding GSSE4 and GSSE6
    • Conceptualization
      Olena Dobrovolska, Knut Schmidtke, Pavlo Lastovchenko, Olga Odnoshevna, Oleksandr Tkachenko
    • Data curation
      Olena Dobrovolska, Knut Schmidtke, Pavlo Lastovchenko
    • Formal Analysis
      Olena Dobrovolska, Knut Schmidtke, Pavlo Lastovchenko, Oleksandr Tkachenko
    • Investigation
      Olena Dobrovolska, Knut Schmidtke, Pavlo Lastovchenko, Olga Odnoshevna
    • Methodology
      Olena Dobrovolska, Knut Schmidtke, Pavlo Lastovchenko
    • Project administration
      Olena Dobrovolska, Knut Schmidtke, Pavlo Lastovchenko
    • Supervision
      Olena Dobrovolska, Knut Schmidtke, Pavlo Lastovchenko, Olga Odnoshevna, Oleksandr Tkachenko
    • Validation
      Olena Dobrovolska, Knut Schmidtke, Pavlo Lastovchenko
    • Visualization
      Olena Dobrovolska, Knut Schmidtke, Pavlo Lastovchenko, Olga Odnoshevna, Oleksandr Tkachenko
    • Writing – original draft
      Olena Dobrovolska, Knut Schmidtke, Pavlo Lastovchenko, Olga Odnoshevna, Oleksandr Tkachenko
    • Writing – review & editing
      Olena Dobrovolska, Knut Schmidtke, Pavlo Lastovchenko, Olga Odnoshevna, Oleksandr Tkachenko