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

Herbert Peter Wanga; Khamisi Kalegele

doi:doi:10.11648/j.ajsea.20150403.11

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

Herbert Peter Wanga, Khamisi Kalegele

Published in American Journal of Software Engineering and Applications (Volume 4, Issue 3)

Received: 11 April 2015 Accepted: 24 April 2015 Published: 7 May 2015

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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.

Published in	American Journal of Software Engineering and Applications (Volume 4, Issue 3)
DOI	10.11648/j.ajsea.20150403.11
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), 2015. Published by Science Publishing Group

Keywords

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

References

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[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.
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[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.
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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

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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

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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

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@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://article.sciencepublishinggroup.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}
}

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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 -

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Author Information

Herbert Peter Wanga

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

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

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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

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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

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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

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@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://article.sciencepublishinggroup.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}
}

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