This research article published by TRANSACTIONS ON MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE, Vol 8, No. 5Research on agricultural and rural development (ARD) systems in general, and farming as a business (FAAB) in particular, face the limitation of availability of credible and reliable benchmarking data, both for on-farm support for farm management decision making and off-farm support for research, investment and policy decision making. One of the main part of this limitation is to obtain reliable benchmarking data for decision making, both for current conditions and under scenarios of changed bio-physical and socioeconomic conditions. This paper presents a framework for mobile application development to support farming as a business via benchmarking (FAABB). This is done with a model that distinguishes between internal and external sources of data and between codified and computed information. Also, the paper demonstrates and emphasizes how integration should be considered as a requirement when developing a typical mobile application for ARD. The paper ends with a description of an ongoing research project at Nelson Mandela African Institution of Technology (NM-AIST) in Tanzania that aims to develop a new framework to facilitate development of mobile applications for FAABB.