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Towards a Mobile-Based DSS for Smallholder Livestock Keepers: Tanzania as a Case Study

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Building a useful and responsive Decision Support System (DSS) requires a deep understanding of the pertinent application domain before starting the system design. In this paper we report about an attempt to develop a mobile-based DSS for smallholder
    Towards a Mobile-Based DSS for Smallholder Livestock Keepers: Tanzania as a Case Study. Bernard Mussa * , Zaipuna Yonah, Computational and Communication Science and Engineering, The Nelson Mandela African Institution of Science and Technology, Arusha, Tanzania. Charles Tarimo, College of Engineering and Technology, University of Dar Es Salaam Dar Es Salaam, Tanzania.  Abstract  -- Building a useful and responsive Decision Support System (DSS) requires a deep understanding of the pertinent application domain before starting the system design. In this paper we report about an attempt to develop a mobile-based DSS for smallholder livestock keepers with Arusha region as a case study. The objective of the reported study is to provide an information tool for decision making to the smallholder livestock keepers. The development process involved: 1) employing information gathering techniques to understand smallholder livestock keepers’ information needs 2) studying the current methods that are used for information flow among livestock stakeholders. (i.e. smallholder livestock keepers, extension officers and livestock researchers) 3) analysis of the current situation within Arusha: located in the northern parts of Tanzania in terms of mobile phones penetration, with prospects of leveraging the high mobile phone penetration rate for enhanced information sharing among the smallholder livestock keepers and 4) exploration of options for the platform/model to be used for information access and delivery. The outputs of the above four activities were used to inform the requirements elicitation, and design phases of the mobile-based DSS system development. In addition, the mentioned four activities were supplemented by an extensive literature review of related works on requirements engineering in DSS development. It is anticipated that once the system has been developed, it will be of help to livestock keepers, improving farm-level productivity and decision making process. Findings from the study indicate that majority of smallholder livestock keepers in the selected area possess mobile phones and are in need of access to specific information to support their livestock related decision making. However, information access platforms/models that are currently in place do not cater for a satisfactory solution to their needs. Analysis of various options for designing a DSS platform has shown that a model that considers the administrative, organizational structure, as well as roles of relevant stakeholders in the livestock information flow will be useful for the studied context. The proposed Role-based Information Decision Support (RIDS) Model will facilitate data querying, analysis and information delivery based on users’ information requirements for the design of the DSS’s data marts. This will, in turn, be the basis for implementing a system of information sharing and delivery mechanism that will improve the decision making process and livestock management for smallholder livestock keepers in the studied geographical environment. Keywords: Decision Support System, Data Mart, Mobile phones, Smallholder livestock keepers. I.   INTRODUCTION A Decision Support System (DSS) for information retrieval, data analysis and decision making support can be a useful tool for enhancing the productivity of livestock sector as reported in [1]. In our case study, despite the fact that data exists from different livestock data sources, smallholder livestock keepers rarely access this data for their decision making. Obviously, information is required to support decisions making by individuals and organizations if they are to remain more competitive and productive. Data is a valuable asset and represents a tremendous investment of resources. There are unprecedented volumes of data today existing in a variety of places and different formats. The growing volume of data has sparked renewed interest in data analysis [2], thus making it imperative to have some techniques for data integration and analysis so as to provide a linkage  between data collection and potential use. (IJCSIS) International Journal of Computer Science and Information Security, Vol. 12, No. 8, August 201454 ISSN 1947-5500    In the quest of developing a software system such as a DSS, goals and users requirement must be identified as an initial step towards building a complete system.   The idea of user-centric approach is very pertinent towards development of any information system. As for developing an effective DSS using data warehousing techniques, it is pointed out by Rai et al.  in [3,20]  ,  that, user requirements play a fundamental role in restricting the area of interest for data analysis and in choosing facts, dimensions, and measures for data marts that are to be designed and implemented. The objective of the study reported in this is to develop an effective mobile-based DSS that is responsive to its intended users  —  smallholder livestock keepers with those in Arusha as a case study. Apparently this endeavor calls for the identification of proper information needs of the said smallholder livestock keepers and availability of an adequate  platform/model for access and delivery of such relevant and in-demand information. Using our case study area, which is the Meru District in Northern Tanzania, we have identified four (4) key user groups, namely: district livestock officers, ward livestock field officers, smallholder livestock keepers and livestock researchers that play important roles in the livestock data and information exchange within the district, and on which the requirements elicitation process has been focused. Weibelzahl et al. in [4] remarked that, involving users from the very beginning can help to discover their mental models and expectations, to identify and analyze theoretical tasks, workflow and goals, and in general to validate t he developers’ assumptions about the users. Users’ and system requirements for DSS design that are presented in this paper are results of analysis of data obtained from interviews, questionnaires, document reviews and group discussions with key user groups identified in this study. These will serve as a guide to business, functional and non-functional information requirements for the developer of the mobile-based DSS for livestock keepers in Tanzania. From the analysis of data collected, majority of smallholder livestock keepers depend on information delivered by livestock experts around and within their administrative locality. Based on this fact, we propose a Role-based Information Decision Support (RIDS) Model as suitable for meeting information needs of smallholder livestock keepers. The proposed model will dictate the design of specified data marts for enhancing data analysis and information delivery  based on users’ information needs and mobile capabilities. The model considers the administrative, organizational structure as well as roles of relevant stakeholders in the livestock information flow in the Tanzanian context and is designed according to flow of information from the source of information (source systems), granular analysis of data and information dissemination agents/middleware to end-user of information for decision making support. This paper is organized in 5 sections. Section one covers general introduction of the research topic and objective of the study. Literature review and related works are discussed in section two. Section three covers Methodology employed in this research work. Results and Discussion of analysis of the data collected are covered in section four and the paper ends with a conclusion in section five.   II.   LITERATURE REVIEW Recent experiences in building Decision Support Systems (DSS) point out the need of a deep understanding of the application domain before starting a system design. The application domain under consideration has to be characterized in terms of stakeholders’  roles and of their requirements and in terms of the decision making processes these stakeholders are involved in [5]. In designing a DSS using Data Warehousing (DW) techniques it is necessary to distinguish between supply- and demand-driven approaches. Inmon [6] describes a supply driven approach in development of DSS as opposed to requirement  –  driven development (IJCSIS) International Journal of Computer Science and Information Security, Vol. 12, No. 8, August 201455 ISSN 1947-5500    of operational systems. In demand driven, users’ information needs are given more relevance. A goal oriented approach to requirement analysis is  proposed by Giorgini et al  . [7], in which two  perspectives are integrated for requirement analysis: organizational modeling, centered on stakeholders, and decisional modeling, focused on decision makers. The approach used is similar to proposed one but differs in the design in which the former relies on the organizational modelling while ours is centered on end-users who are the targeted decision makers. DSS requirements are identified in terms of goal and  plan delegation from stakeholders (the users) to the system-to-be in an agent-oriented software engineering methodology proposed by Perini et al. in [5]. In this, early requirement analysis process is analogous to the one used this paper but there is a significant difference when it comes to mapping of available supply information to users informational requirements. Anton [8] suggested a goal-based requirement analysis whereby the goal analysis to identify requirements is more on the organizational goals as compared to users’ goals. Here, the approach described favors the information needs of organizations rather that the end-users of the DSS which is the main focus employed in the proposed user-driven approach. An interesting case-based comparison of supply- and demand-driven approaches that is worthy to mention can be found in [9, 14]. Extraordinarily, it is concluded that data-oriented and goal-oriented techniques are complementary, and may be used in parallel to achieve optimal design. Finally, it is worth to mention that related works in requirements analysis have all stressed on the user involvement in the early stages of DSS development. Studies have also shown that 40% of all DW projects are never completed, and 85% fail to meet business objectives [10] reasons behind being failure to accurately collect and analyses requirements. The new proposed approach to requirement elicitation is mainly user-driven. Also adopted is the mixed demand/supply mechanism in the requirement analysis whereby information needs of users are mapped and fulfilled as per supply of available data in the operational system databases. This approach is  both cost effective as well as saves system development time. III.   METHODOLOGY The research methodology employed in the reported study was based on qualitative research methods such as interviews, observations, questionnaires, documents analysis, participating in group discussions related to the research topics, literature review and analysis of existing systems. By the term qualitative research, Strauss and Corbin [11] defines it as, "any kind of research that produces findings not arrived at by means of statistical  procedures or other means of quantification".  A.    Demographics of Respondents A total of 210 smallholder livestock keepers were  purposely and conveniently sampled to represent the  population in Meru District in Arusha Region-Tanzania. The location was selected due to concentration of smallholder livestock keepers in the region. The study sample comprised of 108 (51.43%) female and 102 (48.57%) males and respondents were distributed across the administrative wards in the district as shown in Fig. 1. Figure. 1. Respondents Distribution by Ward 1013151511171212151411111323180510152025     a     k     h    e    r     i     k     i     k    a    t     i    t     i     k     i     k    w    e     k     i    n    g     '    o    r     i    m    a     j     i_    c     h    a     i    m    a     k     i     b    a    m    a    r    o    r    o    n     i    m     b    u    g    u    n     i    n     k    o    a    n    r    u    a    n     k    o    a    r    a    n    g    a    n     k    o    a    r     i    s    a    m     b    u    p    o     l     i    s     i    n    g     '     i    s     i_    s    e    e     l    a    s    o    n    g    o    r    o    u    s    a_    r     i    v    e    r (IJCSIS) International Journal of Computer Science and Information Security, Vol. 12, No. 8, August 201456 ISSN 1947-5500    In addition, Thirty (30) livestock experts comprising of Fifteen (15) Ward Livestock Field Officers from respective wards, Ten (10) District Livestock Officers from Meru District Council Office and Five (5) Livestock Researchers from livestock training and research institutions namely the Nelson Mandela African Institution of Science and Technology (NM-AIST) and Livestock Training Agency (LITA-Tengeru) were also key stakeholders involved in the reported study.    B.   Case Study Area Mapping The use of ODK tool for data collection enabled the gathering of GPS location information of respondents during the process of requirement collection. Geographical locations of the interviewed livestock keepers were, with their consent, recorded and plotted on Google App Engine Maps Visualizer as shown in Fig. 2. The dispersed location distribution of livestock keepers highlights the importance of exploiting mobile technology for information dissemination to targeted users since one of the challenges pointed by the wards’ livestock field officers in relation to information dissemination is the geographical remoteness of livestock keepers. Figure. 2. A map showing the study site C.    Requirements Elicitation Methods a.    Information gathering process (identification of information needs) Questionnaires, Group discussions and Interviews were used in gathering requirements with regards to information and decision making support needs for the development of a Decision Support System (DSS) for the targeted user groups namely smallholder livestock keepers , district livestock officers, ward livestock field officers and livestock researchers. The questionnaires were specifically set to investigate information needs of the respondents, current situation with regards to information flow among livestock stakeholders as well as mobile phones penetration rate in the studied area. Guided and self-response questionnaires were administered to the targeted groups. For smallholder livestock keepers, structured questionnaires were designed and administered using a guided interview through an Open Data Kit (ODK) tool. ODK is free and open source suite of tools that allow data collection using mobile devices and data submission to an online server, even without an Internet connection or mobile carrier service at the time of data collection [12]. Facilitated group discussions involving wards livestock field officers and livestock keepers were also conducted for the purpose of understanding stakeholders’ roles, interactions and information flow among them. Interviews were conducted with livestock researchers and some livestock officers in order understand from the experts’  point of view on how research findings and relevant information deemed essential could be exploited to directly serve information needs of smallholder livestock keepers through the proposed DSS. (IJCSIS) International Journal of Computer Science and Information Security, Vol. 12, No. 8, August 201457 ISSN 1947-5500    b.    Analysis of existing data flow and source  systems Detailed review and analysis of relevant documents obtained from wards’ and district council offices was carried out in order to identify data, processes and tools that are currently being employed at various levels and understand the organizational structure with respect to data collection, information exchange as well as identifying existing operational source systems that could serve as data sources for the DSS to be developed. A demand/supply driven approach was adopted whereby users’ information demands obtained through analysis of data collected were analyzed in parallel with identified existing operational systems (i.e. Livestock Database System) in order to find the availability of data that can be used to address the information requirements of smallholder livestock keepers. This method was used because information demands of the users could mainly be fulfilled by data that were provided by the existing operational source systems at the district office. Fig. 3 below shows the  processes involved in the Demand /Supply model adopted. Supply Chain Demand Chain Figure. 3. Demand/ supply driven requirement gathering and analysis TABLE I: TASKS AND TECHNIQUES USED IN THE SUPPLY/DEMAND DRIVEN APPROACH Resources Technique Task Identify source systems Interview, Observation Apply derivation  process Reverse Engineering of existing schema(Livestock Database System) Identify users Interview, Documents Review, Group Discussions Determine analysis needs Documents Review, Literature review Mapping requirements Design and Literature Review DSS requirements specification Use cases, Documentation IV.   RESULTS AND DISCUSSIONS  A.   Smallholder livestock keepers’ information needs a.    Information requirements from smallholder livestock keepers In order to capture information needs of livestock keepers, who are the primary target user group for the DSS to be developed, a total of 210 questionnaires were administered. These were focused on the kind of information needed to support decision making. Table 2 summarizes respondents’ information needs. These results showed a significant number of livestock keepers are in need of information on disease outbreaks, vaccinations and treatment, markets and weather information and modern methods of livestock husbandry. This variety of targeted information will support their daily decision making regarding their livestock and the overall livestock keeping process . Identify source systems Apply derivation  process Mapping Requirements Identify users Determine analysis needs DSS Requirements (IJCSIS) International Journal of Computer Science and Information Security, Vol. 12, No. 8, August 201458 ISSN 1947-5500
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