Developing the Smart Farming Index Across Countries

Youngtak Seo

doi:doi:10.11648/j.ajep.20221106.13

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Developing the Smart Farming Index Across Countries

Youngtak Seo

Published in American Journal of Environmental Protection (Volume 11, Issue 6)

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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[4]	Wolfert, S., et al. (2017). Big Data in Smart Farming-A review, Agricultural Systems, Elsevier.
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[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.
[8]	TIBBETTS, J. H. (2019). Agricultural Disruption. BioScience, 69 (4), 237–243. https://www.jstor.org/stable/26661283
[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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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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TY  - JOUR
T1  - Developing the Smart Farming Index Across Countries
AU  - Youngtak Seo
Y1  - 2022/12/29
PY  - 2022
N1  - https://doi.org/10.11648/j.ajep.20221106.13
DO  - 10.11648/j.ajep.20221106.13
T2  - American Journal of Environmental Protection
JF  - American Journal of Environmental Protection
JO  - American Journal of Environmental Protection
SP  - 165
EP  - 170
PB  - Science Publishing Group
SN  - 2328-5699
UR  - https://doi.org/10.11648/j.ajep.20221106.13
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.
VL  - 11
IS  - 6
ER  -

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Author Information

Youngtak Seo

Cranbrook Schools, Bloomfield Hills, USA

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Plain Text BibTeX RIS

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

Copy | Download

@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}
}

Copy | Download

TY  - JOUR
T1  - Developing the Smart Farming Index Across Countries
AU  - Youngtak Seo
Y1  - 2022/12/29
PY  - 2022
N1  - https://doi.org/10.11648/j.ajep.20221106.13
DO  - 10.11648/j.ajep.20221106.13
T2  - American Journal of Environmental Protection
JF  - American Journal of Environmental Protection
JO  - American Journal of Environmental Protection
SP  - 165
EP  - 170
PB  - Science Publishing Group
SN  - 2328-5699
UR  - https://doi.org/10.11648/j.ajep.20221106.13
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.
VL  - 11
IS  - 6
ER  -

Copy | Download