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dc.contributor.authorKyaruzi, John
dc.contributor.authorYonah, Zaipuna
dc.contributor.authorSwai, Hulda
dc.contributor.authorNyambo, Devotha
dc.date.accessioned2021-09-23T12:41:51Z
dc.date.available2021-09-23T12:41:51Z
dc.date.issued2020-10-30
dc.identifier.urihttps://dspace.nm-aist.ac.tz/handle/20.500.12479/1335
dc.descriptionThis research article published by TRANSACTIONS ON MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE, Vol 8, No. 5en_US
dc.description.abstractResearch 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.en_US
dc.language.isoenen_US
dc.publisherTRANSACTIONS ON MACHINE LEARNING AND ARTIFICIAL INTELLIGENCEen_US
dc.subjectAgricultural and rural development (ARD)en_US
dc.subjectFarming as a business via benchmarking (FAABB)en_US
dc.subjectM-apps frameworksen_US
dc.titleExternal Services and their Integration as a Requirement in Developing a Mobile Framework to Support Farming as a Business via Benchmarking: The Case of NM-AISTen_US
dc.typeArticleen_US


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