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

ABSTRACT: 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 p

SUBMITTER: Reeves NP 

PROVIDER: S-EPMC10162538 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature