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

dc.creatorReeves, N.P.
dc.creatorRamadan, A.
dc.creatorSal y Rosas Celi, V.G.
dc.creatorMedendorp, J.W.
dc.creatorHarun-Ar-Rashid
dc.creatorKrupnik, T.J.
dc.creatorLutomia, A.N.
dc.creatorBello-Bravo, J.
dc.creatorPittendrigh, B.R.
dc.date2023-05-09T00:30:16Z
dc.date2023-05-09T00:30:16Z
dc.date2023
dc.date.accessioned2023-07-17T20:10:36Z
dc.date.available2023-07-17T20:10:36Z
dc.identifierhttps://hdl.handle.net/10883/22603
dc.identifier10.1371/journal.pone.0281428
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/7514346
dc.descriptionWomen 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.
dc.languageEnglish
dc.publisherPublic Library of Science
dc.relationhttps://figshare.com/articles/journal_contribution/S1_File_-/22771490
dc.relationhttps://purr.purdue.edu/publications/3983/1
dc.relationGender equality, youth & social inclusion
dc.relationTransforming Agrifood Systems in South Asia
dc.relationResilient Agrifood Systems
dc.relationUnited States Agency for International Development
dc.relationBill & Melinda Gates Foundation
dc.relationCGIAR
dc.relationhttps://hdl.handle.net/10568/130291
dc.rightsCIMMYT 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 purpose
dc.rightsOpen Access
dc.source5
dc.source18
dc.source0281428
dc.source1932-6203
dc.sourcePLoS ONE
dc.subjectAGRICULTURAL SCIENCES AND BIOTECHNOLOGY
dc.subjectMachine-Supported Decision-Making
dc.subjectTraining Turnout
dc.subjectGender Inclusivity
dc.subjectMACHINE LEARNING
dc.subjectDECISION MAKING
dc.subjectAGRICULTURAL TRAINING
dc.subjectGENDER
dc.subjectSOCIAL INCLUSION
dc.subjectSustainable Agrifood Systems
dc.titleMachine-supported decision-making to improve agricultural training participation and gender inclusivity
dc.typeArticle
dc.typePublished Version
dc.coverageSan Francisco, CA (USA)


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