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

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Title Machine-supported decision-making to improve agricultural training participation and gender inclusivity
 
Creator Reeves, Norman Peter
Ramadan, Ahmed
Sal y Rosas Celi, Victor Giancarlo
Medendorp, John William
Harun-Ar-Rashid
Krupnik, Timothy J.
Lutomia, Anne Namatsi
Bello-Bravo, Julia
Pittendrigh, Barry Robert
 
Subject machine learning
decision making
agricultural training
gender
social inclusion
 
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.
 
Date 2023
2023-05-09T17:33:24Z
2023-05-09T17:33:24Z
 
Type Journal Article
 
Identifier Reeves, N. P., Ramadan, A., Sal Y Rosas Celi, V. G., Medendorp, J. W., Ar-Rashid, H., Krupnik, T. J., Lutomia, A. N., Bello-Bravo, J. M., & Pittendrigh, B. R. (2023). Machine-supported decision-making to improve agricultural training participation and gender inclusivity. PLOS ONE, 18(5), e0281428. https://hdl.handle.net/10883/22603
1932-6203
https://hdl.handle.net/10568/130291
https://hdl.handle.net/10883/22603
https://doi.org/10.1371/journal.pone.0281428
 
Language en
 
Rights CC-BY-4.0
Open Access
 
Format application/pdf
 
Publisher Public Library of Science (PLoS)
 
Source PLoS ONE