INTERPRETATION OF INDIGENOUS AGRICULTURAL TERMINOLOGIES BY ALGORITHMIC TOOLS AMONG YOUNG DIGITAL AGRICULTURAL USERS IN NIGERIA

Abstract

This study analysed interpreted indigenous agricultural terminologies used in Nigeria by algorithmic tools from youth and gender perspectives. Data were collected from 396 respondents and analysed using descriptive statistics and regression analysis. Results revealed dominant male respondents (60.4%). Youths aged 21–40 years represented 59.3% of the sample, while adults aged 41 years and above accounted for 40.7%. The majority (33.0%) possessed Master’s degrees, Bachelor’s degrees (24.3%), PhDs (22.0%), and professorial ranks (12.9%). Results further showed that misinterpretation of indigenous agricultural terminologies recorded an average occurrence of 10.3%–16.2% across all age groups. Indigenous food names (14.9%) and local farming tools (14.9%) were frequently distorted. Lack of localized NLP training data (18.2% youth males; 12.6% adult males; 12.1% youth females) was among the key challenges exacerbating algorithmic misinterpretation. The study concluded that algorithmic tools consistently misinterpreted indigenous agricultural terminologies across all age groups of users, hence, the need to prioritize the creation of inclusive, gender and youth-responsive NLP datasets that integrate indigenous agricultural knowledge, local languages, and contextual variations in Nigeria.

Keywords: Gender, Youth, Algorithmic tools, indigenous agricultural terminologies, Digital Agricultural Users