Machine-supported decision-making to improve agricultural training participation and gender inclusivity

Women comprise a significant portion of the agricultural workforce in developing countries but are often less likely to attend government sponsored training events. The objective of this study was to assess the feasibility of using machine-supported decision-making to increase overall training turno...

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Autores: Reeves, N.P., Ramadan, A., Sal y Rosas Celi, V.G., Medendorp, J.W., Harun-Ar-Rashid, Krupnik, T.J., Lutomia, A.N., Bello-Bravo, J., Pittendrigh, B.R.
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2023
País:México
Institución:Centro Internacional de Mejoramiento de Maíz y Trigo
Repositorio:Repositorio Institucional de Publicaciones Multimedia del CIMMYT
OAI Identifier:oai:repository.cimmyt.org:10883/22603
Acceso en línea:https://hdl.handle.net/10883/22603
Access Level:acceso abierto
Palabra clave:AGRICULTURAL SCIENCES AND BIOTECHNOLOGY
Machine-Supported Decision-Making
Training Turnout
Gender Inclusivity
MACHINE LEARNING
DECISION MAKING
AGRICULTURAL TRAINING
GENDER
SOCIAL INCLUSION
Sustainable Agrifood Systems
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spelling Machine-supported decision-making to improve agricultural training participation and gender inclusivityReeves, N.P.Ramadan, A.Sal y Rosas Celi, V.G.Medendorp, J.W.Harun-Ar-RashidKrupnik, T.J.Lutomia, A.N.Bello-Bravo, J.Pittendrigh, B.R.AGRICULTURAL SCIENCES AND BIOTECHNOLOGYMachine-Supported Decision-MakingTraining TurnoutGender InclusivityMACHINE LEARNINGDECISION MAKINGAGRICULTURAL TRAININGGENDERSOCIAL INCLUSIONSustainable Agrifood SystemsWomen comprise a significant portion of the agricultural workforce in developing countries but are often less likely to attend government sponsored training events. The objective of this study was to assess the feasibility of using machine-supported decision-making to increase overall training turnout while enhancing gender inclusivity. Using data obtained from 1,067 agricultural extension training events in Bangladesh (130,690 farmers), models were created to assess gender-based training patterns (e.g., preferences and availability for training). Using these models, simulations were performed to predict the top (most attended) training events for increasing total attendance (male and female combined) and female attendance, based on gender of the trainer, and when and where training took place. By selecting a mixture of the top training events for total attendance and female attendance, simulations indicate that total and female attendance can be concurrently increased. However, strongly emphasizing female participation can have negative consequences by reducing overall turnout, thus creating an ethical dilemma for policy makers. In addition to balancing the need for increasing overall training turnout with increased female representation, a balance between model performance and machine learning is needed. Model performance can be enhanced by reducing training variety to a few of the top training events. But given that models are early in development, more training variety is recommended to provide a larger solution space to find more optimal solutions that will lead to better future performance. Simulations show that selecting the top 25 training events for total attendance and the top 25 training events for female attendance can increase female participation by over 82% while at the same time increasing total turnout by 14%. In conclusion, this study supports the use of machine-supported decision-making when developing gender inclusivity policies in agriculture extension services and lays the foundation for future applications of machine learning in this area.Public Library of Science2023-05-09T00:30:16Z2023-05-09T00:30:16Z2023Published Versioninfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/10883/2260310.1371/journal.pone.028142851802814281932-6203PLoS ONEreponame:Repositorio Institucional de Publicaciones Multimedia del CIMMYTinstname:Centro Internacional de Mejoramiento de Maíz y Trigoinstacron:CIMMYTEnglishhttps://figshare.com/articles/journal_contribution/S1_File_-/22771490https://purr.purdue.edu/publications/3983/1Gender equality, youth & social inclusionTransforming Agrifood Systems in South AsiaResilient Agrifood SystemsUnited States Agency for International Development (USAID)Bill & Melinda Gates Foundation (BMGF)Consultative Group for International Agricultural Research (CGIAR)https://hdl.handle.net/10568/130291San Francisco, CA (USA)CIMMYT manages Intellectual Assets as International Public Goods. The user is free to download, print, store and share this work. In case you want to translate or create any other derivative work and share or distribute such translation/derivative work, please contact CIMMYT-Knowledge-Center@cgiar.org indicating the work you want to use and the kind of use you intend; CIMMYT will contact you with the suitable license for that purposeOpen Accessinfo:eu-repo/semantics/openAccessoai:repository.cimmyt.org:10883/226032024-10-11T19:56:45Z
dc.title.none.fl_str_mv Machine-supported decision-making to improve agricultural training participation and gender inclusivity
title Machine-supported decision-making to improve agricultural training participation and gender inclusivity
spellingShingle Machine-supported decision-making to improve agricultural training participation and gender inclusivity
Reeves, N.P.
AGRICULTURAL SCIENCES AND BIOTECHNOLOGY
Machine-Supported Decision-Making
Training Turnout
Gender Inclusivity
MACHINE LEARNING
DECISION MAKING
AGRICULTURAL TRAINING
GENDER
SOCIAL INCLUSION
Sustainable Agrifood Systems
title_short Machine-supported decision-making to improve agricultural training participation and gender inclusivity
title_full Machine-supported decision-making to improve agricultural training participation and gender inclusivity
title_fullStr Machine-supported decision-making to improve agricultural training participation and gender inclusivity
title_full_unstemmed Machine-supported decision-making to improve agricultural training participation and gender inclusivity
title_sort Machine-supported decision-making to improve agricultural training participation and gender inclusivity
dc.creator.none.fl_str_mv Reeves, N.P.
Ramadan, A.
Sal y Rosas Celi, V.G.
Medendorp, J.W.
Harun-Ar-Rashid
Krupnik, T.J.
Lutomia, A.N.
Bello-Bravo, J.
Pittendrigh, B.R.
author Reeves, N.P.
author_facet Reeves, N.P.
Ramadan, A.
Sal y Rosas Celi, V.G.
Medendorp, J.W.
Harun-Ar-Rashid
Krupnik, T.J.
Lutomia, A.N.
Bello-Bravo, J.
Pittendrigh, B.R.
author_role author
author2 Ramadan, A.
Sal y Rosas Celi, V.G.
Medendorp, J.W.
Harun-Ar-Rashid
Krupnik, T.J.
Lutomia, A.N.
Bello-Bravo, J.
Pittendrigh, B.R.
author2_role author
author
author
author
author
author
author
author
dc.subject.none.fl_str_mv AGRICULTURAL SCIENCES AND BIOTECHNOLOGY
Machine-Supported Decision-Making
Training Turnout
Gender Inclusivity
MACHINE LEARNING
DECISION MAKING
AGRICULTURAL TRAINING
GENDER
SOCIAL INCLUSION
Sustainable Agrifood Systems
topic AGRICULTURAL SCIENCES AND BIOTECHNOLOGY
Machine-Supported Decision-Making
Training Turnout
Gender Inclusivity
MACHINE LEARNING
DECISION MAKING
AGRICULTURAL TRAINING
GENDER
SOCIAL INCLUSION
Sustainable Agrifood Systems
description Women comprise a significant portion of the agricultural workforce in developing countries but are often less likely to attend government sponsored training events. The objective of this study was to assess the feasibility of using machine-supported decision-making to increase overall training turnout while enhancing gender inclusivity. Using data obtained from 1,067 agricultural extension training events in Bangladesh (130,690 farmers), models were created to assess gender-based training patterns (e.g., preferences and availability for training). Using these models, simulations were performed to predict the top (most attended) training events for increasing total attendance (male and female combined) and female attendance, based on gender of the trainer, and when and where training took place. By selecting a mixture of the top training events for total attendance and female attendance, simulations indicate that total and female attendance can be concurrently increased. However, strongly emphasizing female participation can have negative consequences by reducing overall turnout, thus creating an ethical dilemma for policy makers. In addition to balancing the need for increasing overall training turnout with increased female representation, a balance between model performance and machine learning is needed. Model performance can be enhanced by reducing training variety to a few of the top training events. But given that models are early in development, more training variety is recommended to provide a larger solution space to find more optimal solutions that will lead to better future performance. Simulations show that selecting the top 25 training events for total attendance and the top 25 training events for female attendance can increase female participation by over 82% while at the same time increasing total turnout by 14%. In conclusion, this study supports the use of machine-supported decision-making when developing gender inclusivity policies in agriculture extension services and lays the foundation for future applications of machine learning in this area.
publishDate 2023
dc.date.none.fl_str_mv 2023-05-09T00:30:16Z
2023-05-09T00:30:16Z
2023
dc.type.none.fl_str_mv Published Version
info:eu-repo/semantics/publishedVersion
info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/10883/22603
10.1371/journal.pone.0281428
url https://hdl.handle.net/10883/22603
identifier_str_mv 10.1371/journal.pone.0281428
dc.language.none.fl_str_mv English
language_invalid_str_mv English
dc.relation.none.fl_str_mv https://figshare.com/articles/journal_contribution/S1_File_-/22771490
https://purr.purdue.edu/publications/3983/1
Gender equality, youth & social inclusion
Transforming Agrifood Systems in South Asia
Resilient Agrifood Systems
United States Agency for International Development (USAID)
Bill & Melinda Gates Foundation (BMGF)
Consultative Group for International Agricultural Research (CGIAR)
https://hdl.handle.net/10568/130291
dc.rights.none.fl_str_mv Open Access
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Open Access
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.coverage.none.fl_str_mv San Francisco, CA (USA)
dc.publisher.none.fl_str_mv Public Library of Science
publisher.none.fl_str_mv Public Library of Science
dc.source.none.fl_str_mv 5
18
0281428
1932-6203
PLoS ONE
reponame:Repositorio Institucional de Publicaciones Multimedia del CIMMYT
instname:Centro Internacional de Mejoramiento de Maíz y Trigo
instacron:CIMMYT
instname_str Centro Internacional de Mejoramiento de Maíz y Trigo
instacron_str CIMMYT
institution CIMMYT
reponame_str Repositorio Institucional de Publicaciones Multimedia del CIMMYT
collection Repositorio Institucional de Publicaciones Multimedia del CIMMYT
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repository.mail.fl_str_mv
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