American Journal of Software Engineering and Applications

| Peer-Reviewed |

Towards a Framework for Enabling Operations of Livestock Information Systems in Poor Connectivity Areas

Received: 11 April 2015    Accepted: 24 April 2015    Published: 07 May 2015
Views:       Downloads:

Share This Article

Abstract

Livestock farming is one of the major agricultural activities in the country that is contributing towards achieving development goals of the national Growth and Reduction of Poverty (NSGRP). Smallholder livestock keepers depend on the information from the livestock field officers for sound decision making. Mobile application based solutions, which are currently widely proposed to facilitate the process, fail to perform in poor connectivity areas. This study proposes a machine learning based framework which will enhance the performance of mobile application based solutions in poor connectivity areas. The study used primary data, and secondary data. The primary data were collected through surveys, questionnaires, interviews, and direct observations. Secondary data were collected through books, articles, journals, and Internet searching. Open Data Kit (ODK) tool was used to collect responses from the respondents, and their geographical positions. We used Google earth to have smallholder livestock keepers’ distribution map. Results show that smallholder livestock keepers are geographically scattered and depend on the field livestock officers for exchange of information. Their means of communication are mainly face to face, and mobile phones. They do not use any Livestock Information System. The proposed framework will enable operations of Livestock Information System in poor connectivity area, where majority of smallholder livestock keepers live. This paper provides the requirements model necessary for designing and development of the machine learning-based application framework for enhancing performance of livestock mobile application systems, which will enable operations of livestock information systems in poor connectivity areas.

DOI 10.11648/j.ajsea.20150403.11
Published in American Journal of Software Engineering and Applications (Volume 4, Issue 3, June 2015)
Page(s) 42-49
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Livestock, Information System, Machine Learning, Mobile Application, Technology, Smartphone

References
[1] C. Rolland et al., “A proposal for a Scenario Classification Framework”, Journal of Requirements Engineering, Vol. 3 No. 1, 1998, Springer Verlag, pp. 23–47.
[2] Schneider, G. and J. Winters, “Applying Use Cases - A Practical Guide”, Object Technology Series, ed. J. Booch, Rumbaugh. 1998: Addison Wesley.
[3] Leffingwell, D. and Widrig, D. (2003) “Managing Software Requirements”. 2nd Edition, Addison-Wesley, Boston, MA, USA.
[4] Ministry of Livestock and Fisheries Development (2010), “Livestock Sector Development Strategy”, Tanzania.
[5] Robert J. Mcqueen (1994), “Applying Machine Learning to Agricultural Data”, Management Systems, University of Waikato, Hamilton, New Zealand.
[6] Joris Vanderschrick (2011), “System Requirements Analysis: The first step to value-based system development”, Verhaert – Embedded Systems Development, Belgium.
[7] Jaelson Castro and John Mylopoulos (2002), “Information Systems Analysis and Design”,
[8] S. Pfleeger and J. Atlee (2006), “Software Engineering - Theory and Practice”, Third Edition, Prentice Hall.
[9] Rai, A., Dubey, V., Chatuverdi, K. K., Malhotra, P. K., (2008). “Design and development of data mart for animal resources”, Journal of Computers and Electronics in Agriculture. 64 (2), p111-119.
[10] Defense Acquisition University (2001), “Systems Engineering Fundamentals”, Defense Acquisition University Press, Fort Belvoir, Virginia.
[11] Bhavnani, A A., Won-Wai Ciu, R., Janakiram, S. and Silarszky, P. (2008), “The Role of Mobile Phones in Sustainable Rural Poverty Reduction”, Report for the World Bank ICT Policy Division, Global Information and Communications Department, Washington DC.
[12] The Citizen, 2014, “Tanzania’s Internet users hit 9m”, published online, Wednesday October 1, 2014.
[13] De Vaus, D. A. (2001), “Research Design in Social Research”, London: SAGE.
[14] Yin, R. K. (2003), “Case Study Research: Design and Theory”, Applied Social Research Methods Series. CA: Thousand Oaks.
[15] Kothari, C. R. (2006), “Research Methodology, Methods and Techniques”, New Delhi, India: New Age International Publishers.
[16] Calculator, R. (2014, December 17), “Raosoft”, Retrieved December 17, 2014, from http://www.raosoft.com/samplesize.html:
[17] Bernard Mussa, Zaipuna Yonah, Charles Tarimo, (2014), “Towards a Mobile – Based DSS for Smallholder Livestock Keepers: Tanzania as a Case Study”, International Journal of Computer Science and Information Security, Vol. 12, No. 8, August 2014.
[18] Gladness Mwanga, Zaipuna Yonah, (2014), “ICT as a tool for improving information flow among livestock stakeholders: a case study of Tanzania”, International Journal of Computer Science and Information Security, Vol. 12, No. 8, August 2014.
Author Information
  • School of Computational and Communication Sciences and Engineering (CoCSE), Nelson Mandela African Institution of Science and Technology (NM-AIST), Arusha, Tanzania

  • School of Computational and Communication Sciences and Engineering (CoCSE), Nelson Mandela African Institution of Science and Technology (NM-AIST), Arusha, Tanzania

Cite This Article
  • APA Style

    Herbert Peter Wanga, Khamisi Kalegele. (2015). Towards a Framework for Enabling Operations of Livestock Information Systems in Poor Connectivity Areas. American Journal of Software Engineering and Applications, 4(3), 42-49. https://doi.org/10.11648/j.ajsea.20150403.11

    Copy | Download

    ACS Style

    Herbert Peter Wanga; Khamisi Kalegele. Towards a Framework for Enabling Operations of Livestock Information Systems in Poor Connectivity Areas. Am. J. Softw. Eng. Appl. 2015, 4(3), 42-49. doi: 10.11648/j.ajsea.20150403.11

    Copy | Download

    AMA Style

    Herbert Peter Wanga, Khamisi Kalegele. Towards a Framework for Enabling Operations of Livestock Information Systems in Poor Connectivity Areas. Am J Softw Eng Appl. 2015;4(3):42-49. doi: 10.11648/j.ajsea.20150403.11

    Copy | Download

  • @article{10.11648/j.ajsea.20150403.11,
      author = {Herbert Peter Wanga and Khamisi Kalegele},
      title = {Towards a Framework for Enabling Operations of Livestock Information Systems in Poor Connectivity Areas},
      journal = {American Journal of Software Engineering and Applications},
      volume = {4},
      number = {3},
      pages = {42-49},
      doi = {10.11648/j.ajsea.20150403.11},
      url = {https://doi.org/10.11648/j.ajsea.20150403.11},
      eprint = {https://download.sciencepg.com/pdf/10.11648.j.ajsea.20150403.11},
      abstract = {Livestock farming is one of the major agricultural activities in the country that is contributing towards achieving development goals of the national Growth and Reduction of Poverty (NSGRP). Smallholder livestock keepers depend on the information from the livestock field officers for sound decision making. Mobile application based solutions, which are currently widely proposed to facilitate the process, fail to perform in poor connectivity areas. This study proposes a machine learning based framework which will enhance the performance of mobile application based solutions in poor connectivity areas. The study used primary data, and secondary data. The primary data were collected through surveys, questionnaires, interviews, and direct observations. Secondary data were collected through books, articles, journals, and Internet searching. Open Data Kit (ODK) tool was used to collect responses from the respondents, and their geographical positions. We used Google earth to have smallholder livestock keepers’ distribution map. Results show that smallholder livestock keepers are geographically scattered and depend on the field livestock officers for exchange of information. Their means of communication are mainly face to face, and mobile phones. They do not use any Livestock Information System. The proposed framework will enable operations of Livestock Information System in poor connectivity area, where majority of smallholder livestock keepers live. This paper provides the requirements model necessary for designing and development of the machine learning-based application framework for enhancing performance of livestock mobile application systems, which will enable operations of livestock information systems in poor connectivity areas.},
     year = {2015}
    }
    

    Copy | Download

  • TY  - JOUR
    T1  - Towards a Framework for Enabling Operations of Livestock Information Systems in Poor Connectivity Areas
    AU  - Herbert Peter Wanga
    AU  - Khamisi Kalegele
    Y1  - 2015/05/07
    PY  - 2015
    N1  - https://doi.org/10.11648/j.ajsea.20150403.11
    DO  - 10.11648/j.ajsea.20150403.11
    T2  - American Journal of Software Engineering and Applications
    JF  - American Journal of Software Engineering and Applications
    JO  - American Journal of Software Engineering and Applications
    SP  - 42
    EP  - 49
    PB  - Science Publishing Group
    SN  - 2327-249X
    UR  - https://doi.org/10.11648/j.ajsea.20150403.11
    AB  - Livestock farming is one of the major agricultural activities in the country that is contributing towards achieving development goals of the national Growth and Reduction of Poverty (NSGRP). Smallholder livestock keepers depend on the information from the livestock field officers for sound decision making. Mobile application based solutions, which are currently widely proposed to facilitate the process, fail to perform in poor connectivity areas. This study proposes a machine learning based framework which will enhance the performance of mobile application based solutions in poor connectivity areas. The study used primary data, and secondary data. The primary data were collected through surveys, questionnaires, interviews, and direct observations. Secondary data were collected through books, articles, journals, and Internet searching. Open Data Kit (ODK) tool was used to collect responses from the respondents, and their geographical positions. We used Google earth to have smallholder livestock keepers’ distribution map. Results show that smallholder livestock keepers are geographically scattered and depend on the field livestock officers for exchange of information. Their means of communication are mainly face to face, and mobile phones. They do not use any Livestock Information System. The proposed framework will enable operations of Livestock Information System in poor connectivity area, where majority of smallholder livestock keepers live. This paper provides the requirements model necessary for designing and development of the machine learning-based application framework for enhancing performance of livestock mobile application systems, which will enable operations of livestock information systems in poor connectivity areas.
    VL  - 4
    IS  - 3
    ER  - 

    Copy | Download

  • Sections