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

• Published in: PloS one 2023-05, Vol.18 (5), p.e0281428-e0281428
• Main Authors: Reeves, Norman Peter, Ramadan, Ahmed, Sal Y Rosas Celi, Victor Giancarlo, Medendorp, John William, Ar-Rashid, Harun, Krupnik, Timothy Joseph, Lutomia, Anne Namatsi, Bello-Bravo, Julia Maria, Pittendrigh, Barry Robert
• Format: Article
• Language: English
• Subjects: Agricultural industry; Agricultural management; Agriculture; Analysis; Bangladesh; Biology and Life Sciences; Computer and Information Sciences; Corn; Datasets; Decision making; Developing countries; Educational films; Estimates; Farmers; Female; Females; Gender; Generalized linear models; Government; Humans; Information dissemination; LDCs; Learning algorithms; Literacy; Machine learning; Male; Males; Management; Methods; Modelling; Participation; People and Places; Research and Analysis Methods; Science Policy; Simulation; Simulation methods; Social Sciences; Solution space; Training; Venue
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0281428

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.