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...
| Autores principales: | , , , , , , , , |
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| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
2023
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/130291 |
| _version_ | 1855517024629817344 |
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| author | 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 |
| author_browse | Bello-Bravo, Julia Harun-Ar-Rashid Krupnik, Timothy J. Lutomia, Anne Namatsi Medendorp, John William Pittendrigh, Barry Robert Ramadan, Ahmed Reeves, Norman Peter Sal y Rosas Celi, Victor Giancarlo |
| author_facet | 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 |
| author_sort | Reeves, Norman Peter |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| 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. |
| format | Journal Article |
| id | CGSpace130291 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| record_format | dspace |
| spelling | CGSpace1302912025-11-06T13:03:21Z Machine-supported decision-making to improve agricultural training participation and gender inclusivity 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 machine learning decision making agricultural training gender social inclusion 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. 2023 2023-05-09T17:33:24Z 2023-05-09T17:33:24Z Journal Article https://hdl.handle.net/10568/130291 en Open Access application/pdf 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://doi.org/10.1371/journal.pone.0281428 |
| spellingShingle | machine learning decision making agricultural training gender social inclusion 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 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 |
| 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_short | 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 |
| topic | machine learning decision making agricultural training gender social inclusion |
| url | https://hdl.handle.net/10568/130291 |
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