Gladness develops information processing models to assist small scale dairy farmers in decision making
Gladness’s main interest is in Data Science. Currently, her research focuses on identifying factors that influence farmers to make decisions. She tries to predict the probability of a farmer to make certain decision based on the given set of information. This is a continuation of her master’s study.
Gladness obtained her masters at NM-AIST in which she developed a web and a mobile application for enhancing online extension service, record keeping, linking of livestock stakeholders in one forum, access to marketing, weather and auctioning information. From this research she got an award from UN-FAO, she was the 1st winner out of 15 countries from Africa participated.
Apart from being a student she is working as research assistance in a Program for Emerging Agricultural Research Leaders. This project is missioned to optimize smallholder dairy productivity in East Africa.
Also Gladness has a background of working as an External auditor which she acquired from working with PricewaterhouseCoopers Limited, assigned with different roles including Conduct audits in accordance with IS audit standards, guidelines and best practices to meet planned audit objectives
Information processing model for enhancing decision making by small scale dairy farmers
Thesis Research Project
Her study aims at identifying factors that influence farmers to make decisions. The identified factors will be used to develop a quantitative model to measure the probability of a farmer to make a certain decision based on the given set of information. The developed model will be used to test the prevailing information sharing models, including farmers’ groups to identify what are the gaps in those models based on the information that they were supposed to share.
Her study will also help improve the decision making process in farming and for farmers, who will be guided to know what information they are supposed to record and share. Equally, policy makers and other livestock stakeholders’ will be guided to know what are farmers needs in regards to information and be able to predict the acceptance of certain technology based on a given controlling factor.