A Mobile-based Tea Farming Information System

Abstract

Current trends in information and communication technology have led to emergence of smartphones with faster wireless speeds, faster graphics, better screens, and faster processors. Tapping into this technology can lead to development of mobile information systems that promote sustainable farming through timely information access, especially in tea farming. The traditional practice in tea farming has been dissemination of information through extension officers, Farmer Field Schools, mass media or farmer's sharing own experience. Consequently, this vital information is either accessed too late or when obsolete and/or it is sometimes completely lacking. Limited access to information has led to an information gap hence farmers practice poor planning and farming resulting to environmental degradation and reduced revenues. This paper focused on developing a tea farming information system that meets the information needs of tea farmers. The nature of the study required the researcher to adopt deductive approach so as to compare the data findings against the existing literature through logical reasoning. A case study strategy was employed to gain a rich understanding of the research perspective with the focus being the tea farmers of Mungania Tea Factory Company Limited. Through qualitative and quantitative analysis, the research findings can be generalised to all the tea farmers. Agile system development methodology was employed due to its sustainable development having testing integrated throughout the lifecycle. The contribution of this research concerns adoption of a tea farming information system to improve on productivity, service delivery, profitability, decision-making and farmer protection from fraud and losses. Moreover, it facilitates the communication between the factory and the farmers giving easier access to extension services. It further enhances processing of more accurate and comprehensive information and generation of useful reports.

Post-Covid-19 pandemic extension delivery: A systematic review of E-extension services prospects in Ghana

“…They both use call centre model to disseminate agricultural information to contracted farmers [ 51 , 52 ]. Their extension services include sending bulk text messages to subscribed farmers on regular intervals to update them on weather information, pests and diseases projections, market information updates, agricultural credit services, inputs and commodities delivery services [ 19 , 52 ]. It was also found that between the two service providers, ESOKO has larger coverage and more subscribers than Farmerline [ 20 , 53 ].…”

“…Their services are offered in few local languages and are limited to specific areas and agricultural enterprises [ 51 ]. For instance, both ESOKO and Farmerline services do not cover livestock production [ 19 , 51 ]. The private sector electronic agricultural services are not accessible to all farmers due to it limitedness in services and subscription requirements.…”

“…Moreover, present research findings show that about 91 percent of adults in South Africa own mobile phones; 70 percent in Tanzania and more than 75 percent of adults in Kenya [ 18 ]. The increase in mobile phone and internet access has inspired the development of mobile apps like M-Shamba in Kenya and “UPTAKE” in Tanzania to provide agricultural support services [ 19 , 20 ]. However, as research findings show an increase in mobile phone and internet access and the possibility of implementing digital agricultural extension programs across Africa, little is known about the case of Ghana.…”

Determinants of Rulindo tea farmers’ perception towards organic farming: a MASS::stepAIC() approach

“…To draw an accurate sample size to represent the tea farmers' population in the district, this study used DANIEL's methods (1999) andDANIEL et al, (2018). This method of sample size determination was previously used by other researchers (NAING et al, 2006;MUNENE AND KASAMANI, 2018). Using a 95% confidence level, 7.69% margin of error (ɛ), and 60% proportion of respondents (Ṗ), the minimum sample size was calculated as 156.…”