How AI Transforms Barriers to Organic Arable Farming Adoption

Negin Salimi; Thomas Bokdam

doi:10.1007/978-3-031-76766-1_5

How AI Transforms Barriers to Organic Arable Farming Adoption

Negin Salimi^*, Thomas Bokdam

^*Corresponding author for this work

Business Management and Organisation

Research output: Chapter in Book/Report/Conference proceeding › Chapter › Academic › peer-review

Abstract

While AI's impact on conventional arable farming is well-studied, its potential in organic farming remains underexplored. As farmers transition from conventional to organic practices, they face numerous hurdles. This study aims to investigate how AI can mitigate these challenges. Interviews with 16 experts in organic farming and AI, along with a Best–Worst Method and importance-performance analysis, revealed economic and environmental challenges as top priorities for farmers. Current AI performance in addressing these challenges is low, yet its potential is high. The findings suggest ample opportunities to enhance AI utilization in Dutch organic agriculture, guiding policymakers and technology companies in supporting and prioritizing initiatives.

Original language	English
Title of host publication	Advances in Best–Worst Method
Subtitle of host publication	Proceedings of the Fifth International Workshop on Best–Worst Method (BWM2024)
Editors	Jafar Rezaei, Matteo Brunelli, Majid Mohammadi
Publisher	Springer
Pages	77-102
Number of pages	26
ISBN (Electronic)	9783031767661
ISBN (Print)	9783031767685
DOIs	https://doi.org/10.1007/978-3-031-76766-1_5
Publication status	Published - 21 Mar 2025

Publication series

Name	Lecture Notes in Operations Research
Volume	Part F214
ISSN (Print)	2731-040X
ISSN (Electronic)	2731-0418

Keywords

Artificial Intelligence
Multi-criteria-decision making method
Organic farming

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10.1007/978-3-031-76766-1_5

https://edepot.wur.nl/692022Licence: Taverne

Cite this

Salimi, N., & Bokdam, T. (2025). How AI Transforms Barriers to Organic Arable Farming Adoption. In J. Rezaei, M. Brunelli, & M. Mohammadi (Eds.), Advances in Best–Worst Method: Proceedings of the Fifth International Workshop on Best–Worst Method (BWM2024) (pp. 77-102). (Lecture Notes in Operations Research; Vol. Part F214). Springer. https://doi.org/10.1007/978-3-031-76766-1_5

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abstract = "While AI's impact on conventional arable farming is well-studied, its potential in organic farming remains underexplored. As farmers transition from conventional to organic practices, they face numerous hurdles. This study aims to investigate how AI can mitigate these challenges. Interviews with 16 experts in organic farming and AI, along with a Best–Worst Method and importance-performance analysis, revealed economic and environmental challenges as top priorities for farmers. Current AI performance in addressing these challenges is low, yet its potential is high. The findings suggest ample opportunities to enhance AI utilization in Dutch organic agriculture, guiding policymakers and technology companies in supporting and prioritizing initiatives.",

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Salimi, N & Bokdam, T 2025, How AI Transforms Barriers to Organic Arable Farming Adoption. in J Rezaei, M Brunelli & M Mohammadi (eds), Advances in Best–Worst Method: Proceedings of the Fifth International Workshop on Best–Worst Method (BWM2024). Lecture Notes in Operations Research, vol. Part F214, Springer, pp. 77-102. https://doi.org/10.1007/978-3-031-76766-1_5

How AI Transforms Barriers to Organic Arable Farming Adoption. / Salimi, Negin; Bokdam, Thomas.
Advances in Best–Worst Method: Proceedings of the Fifth International Workshop on Best–Worst Method (BWM2024). ed. / Jafar Rezaei; Matteo Brunelli; Majid Mohammadi. Springer, 2025. p. 77-102 (Lecture Notes in Operations Research; Vol. Part F214).

Research output: Chapter in Book/Report/Conference proceeding › Chapter › Academic › peer-review

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AU - Bokdam, Thomas

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N2 - While AI's impact on conventional arable farming is well-studied, its potential in organic farming remains underexplored. As farmers transition from conventional to organic practices, they face numerous hurdles. This study aims to investigate how AI can mitigate these challenges. Interviews with 16 experts in organic farming and AI, along with a Best–Worst Method and importance-performance analysis, revealed economic and environmental challenges as top priorities for farmers. Current AI performance in addressing these challenges is low, yet its potential is high. The findings suggest ample opportunities to enhance AI utilization in Dutch organic agriculture, guiding policymakers and technology companies in supporting and prioritizing initiatives.

AB - While AI's impact on conventional arable farming is well-studied, its potential in organic farming remains underexplored. As farmers transition from conventional to organic practices, they face numerous hurdles. This study aims to investigate how AI can mitigate these challenges. Interviews with 16 experts in organic farming and AI, along with a Best–Worst Method and importance-performance analysis, revealed economic and environmental challenges as top priorities for farmers. Current AI performance in addressing these challenges is low, yet its potential is high. The findings suggest ample opportunities to enhance AI utilization in Dutch organic agriculture, guiding policymakers and technology companies in supporting and prioritizing initiatives.

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KW - Multi-criteria-decision making method

KW - Organic farming

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T3 - Lecture Notes in Operations Research

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A2 - Mohammadi, Majid

PB - Springer

ER -

How AI Transforms Barriers to Organic Arable Farming Adoption

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