Towards a Framework for Enabling Operations of Livestock Information Systems in Poor Connectivity Areas

Abstract

Livestock farming is one of the major agricultural activities in the country that is contributing towards achieving development goals of the national Growth and Reduction of Poverty (NSGRP). Smallholder livestock keepers depend on the information from the livestock field officers for sound decision making. Mobile application based solutions, which are currently widely proposed to facilitate the process, fail to perform in poor connectivity areas. This study proposes a machine learning based framework which will enhance the performance of mobile application based solutions in poor connectivity areas. The study used primary data, and secondary data. The primary data were collected through surveys, questionnaires, interviews, and direct observations. Secondary data were collected through books, articles, journals, and Internet searching. Open Data Kit (ODK) tool was used to collect responses from the respondents, and their geographical positions. We used Google earth to have smallholder livestock keepers’ distribution map. Results show that smallholder livestock keepers are geographically scattered and depend on the field livestock officers for exchange of information. Their means of communication are mainly face to face, and mobile phones. They do not use any Livestock Information System. The proposed framework will enable operations of Livestock Information System in poor connectivity area, where majority of smallholder livestock keepers live. This paper provides the requirements model necessary for designing and development of the machine learning-based application framework for enhancing performance of livestock mobile application systems, which will enable operations of livestock information systems in poor connectivity areas.