A novel framework for the use of mobile applications in inducing smallholder-farmers’ response to farming as a business via benchmarking

Abstract

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.