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Developing the Smart Farming Index Across Countries

Received: 8 December 2022     Accepted: 21 December 2022     Published: 29 December 2022
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Abstract

As the current agricultural challenges, including climate change, population growth, and water availability, become more pronounced, regions that are highly dependent on agriculture are seeking new ways to track productivity in an effort to boost agricultural output. Hence, an emerging concept of data-driven agriculture, or "Smart Farming," is becoming increasingly relevant in these regions. However, due to variations in available resources and technology across countries, it is difficult to objectify the effectiveness of these methodologies. Therefore, this paper aims to evaluate the potential effectiveness of Smart Farming in countries across different regions of the world to determine which nations have the strongest potential for driving gain through the use of such technology. The potential effectiveness of Smart Farming is assessed by 1) creating and using an index from a selection of datasets that represents every nation's agricultural environment, economic status, and resources available for the application; and 2) running Principal Component Analysis (PCA) on the dataset to weigh each nation's relations to the application and determine their rankings. The top 5 nations for the applicability of Smart Farming are Iceland, New Zealand, Australia, Norway, and Finland. These countries present a viable model for other nations to follow in order to achieve sustainable growth through the adoption of data-driven farming techniques.

Published in American Journal of Environmental Protection (Volume 11, Issue 6)
DOI 10.11648/j.ajep.20221106.13
Page(s) 165-170
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2022. Published by Science Publishing Group

Keywords

Smart Farming, Agriculture, Cross-Country Analysis

References
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[3] Khanna, M., et al. (2021). Digital Transformation for Sustainable Agriculture in the US: Opportunities and Challenges, International Conference of Agricultural Economists.
[4] Wolfert, S., et al. (2017). Big Data in Smart Farming-A review, Agricultural Systems, Elsevier.
[5] Maru, A., et al. (2018). Digital and Data-Driven Agriculture: Harnessing the Power of Data for Smallholders, Global Open Data for Agriculture & Nutrition.
[6] Jouanjean, M. (2019). Digital Opportunities for Trade in the Agriculture and Food Sectors OECD Food, Agriculture and Fisheries Papers, No. 122, OECD Publishing, Paris. http://dx.doi.org/10.1787/91c40e07-en
[7] UN (2017). Food and Agriculture Organization of the United Nations and International Telecommunication Union (2017). E-Agriculture Strategy Guide.
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[9] Drepaul, N. A. (2020). Sustainable Cities and the Internet of Things (IOT) Technology: IOT technology improves the development of smart cities’ infrastructures and reduces over-population stresses. Consilience, 22, 39–47. https://www.jstor.org/stable/26924960
[10] Said Mohamed, E., Belal, AA., Kotb Abd-Elmabod, S., El-Shirbeny, M. A., Gad, A., & Zahran, M. B. (2021). Smart farming for improving agricultural management. The Egyptian Journal of Remote Sensing and Space Science, 24 (3). https://doi.org/10.1016/j.ejrs.2021.08.007
[11] Friedemann, A. J. (2021). Life After Fossil Fuels: A Reality Check on Alternative Energy. In Google Books. Springer Nature. https://books.google.co.kr/books?hl=en&lr=&id=uIYmEAAAQBAJ&oi=fnd&pg=PR5&ots=U-kc6665IG&sig=ezlQSkGID34D8A1VmtF9XywBRBc&redir_esc=y#v=onepage&q&f=false
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Cite This Article
  • APA Style

    Youngtak Seo. (2022). Developing the Smart Farming Index Across Countries. American Journal of Environmental Protection, 11(6), 165-170. https://doi.org/10.11648/j.ajep.20221106.13

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    ACS Style

    Youngtak Seo. Developing the Smart Farming Index Across Countries. Am. J. Environ. Prot. 2022, 11(6), 165-170. doi: 10.11648/j.ajep.20221106.13

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    AMA Style

    Youngtak Seo. Developing the Smart Farming Index Across Countries. Am J Environ Prot. 2022;11(6):165-170. doi: 10.11648/j.ajep.20221106.13

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  • @article{10.11648/j.ajep.20221106.13,
      author = {Youngtak Seo},
      title = {Developing the Smart Farming Index Across Countries},
      journal = {American Journal of Environmental Protection},
      volume = {11},
      number = {6},
      pages = {165-170},
      doi = {10.11648/j.ajep.20221106.13},
      url = {https://doi.org/10.11648/j.ajep.20221106.13},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajep.20221106.13},
      abstract = {As the current agricultural challenges, including climate change, population growth, and water availability, become more pronounced, regions that are highly dependent on agriculture are seeking new ways to track productivity in an effort to boost agricultural output. Hence, an emerging concept of data-driven agriculture, or "Smart Farming," is becoming increasingly relevant in these regions. However, due to variations in available resources and technology across countries, it is difficult to objectify the effectiveness of these methodologies. Therefore, this paper aims to evaluate the potential effectiveness of Smart Farming in countries across different regions of the world to determine which nations have the strongest potential for driving gain through the use of such technology. The potential effectiveness of Smart Farming is assessed by 1) creating and using an index from a selection of datasets that represents every nation's agricultural environment, economic status, and resources available for the application; and 2) running Principal Component Analysis (PCA) on the dataset to weigh each nation's relations to the application and determine their rankings. The top 5 nations for the applicability of Smart Farming are Iceland, New Zealand, Australia, Norway, and Finland. These countries present a viable model for other nations to follow in order to achieve sustainable growth through the adoption of data-driven farming techniques.},
     year = {2022}
    }
    

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    T2  - American Journal of Environmental Protection
    JF  - American Journal of Environmental Protection
    JO  - American Journal of Environmental Protection
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    PB  - Science Publishing Group
    SN  - 2328-5699
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    AB  - As the current agricultural challenges, including climate change, population growth, and water availability, become more pronounced, regions that are highly dependent on agriculture are seeking new ways to track productivity in an effort to boost agricultural output. Hence, an emerging concept of data-driven agriculture, or "Smart Farming," is becoming increasingly relevant in these regions. However, due to variations in available resources and technology across countries, it is difficult to objectify the effectiveness of these methodologies. Therefore, this paper aims to evaluate the potential effectiveness of Smart Farming in countries across different regions of the world to determine which nations have the strongest potential for driving gain through the use of such technology. The potential effectiveness of Smart Farming is assessed by 1) creating and using an index from a selection of datasets that represents every nation's agricultural environment, economic status, and resources available for the application; and 2) running Principal Component Analysis (PCA) on the dataset to weigh each nation's relations to the application and determine their rankings. The top 5 nations for the applicability of Smart Farming are Iceland, New Zealand, Australia, Norway, and Finland. These countries present a viable model for other nations to follow in order to achieve sustainable growth through the adoption of data-driven farming techniques.
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Author Information
  • Cranbrook Schools, Bloomfield Hills, USA

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