A Thesis Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor
of Philosophy in Information and Communication Science and Engineering of the
Nelson Mandela African Institution of Science and Technology
Farming as a business (FAAB) is currently acknowledged as the best route out of poverty for
the majority of rural poor farmers in developing countries like Tanzania. Supporting farmers
to participate in FAAB translates into assisting them to go through a farming life cycle of
five interrelated stages namely: Agricultural domains recognition, farm characterization,
simulation of predictive solutions, identification of limiting factors, and post production
evaluation. Managing FAAB processes, resources and products, requires benchmarking as its
analytical tool; hence, the concept of farming as a business via benchmarking (FAABB).
Supporting a farmer to achieve FAABB is the primary role of an Extension Officer (EO).
Since FAABB is a data-intensive activity, computational and cognitive limitations of an EO
decrease quality and efficiency and increase time spent as well as costs related to facilitating
smallholder farmers to achieve FAABB. Several research efforts have demonstrated that
mobile apps bring in significant capabilities for helping EOs deal with the
challenges associated with FAABB.
However, in Tanzania, data capture and codification are the two greater obstacles in
developing useful mobile applications, than gaps in conceptual theories or available methods
for FAABB. This research takes advantage of available technologies to develop a mobile
framework for FAABB that embeds data capture and codification services to support rapid
development of domain specific m-apps. The main objective of this research is, therefore, to
develop a mobile framework for FAABB (m-FFAABB) that facilitates knowledge capture
and codification for rapid development and use of m-apps that induce farmers‟ response to
FAABB.
The research adopted a Design Science Research (DSR) through Soft System Methodology
(SSM). In the reported work, the framework was designed and two corresponding prototypes
were developed and evaluated to show the applicability of m-FFAABB. The data collected
during the experiments show that the mobile apps developed through the m-FFAABB
are useful, well integrated and easy to use. Moreover, statistical analysis of the
results indicates that the framework reduces time, costs, and intellectual effort of the EOs.