الزامات عملکردی برای سیستم اطلاعات مدیریت مزرعه در آینده

As a subsequent step of the conceptual modelling and the information modelling involving the specification of the knowledge content of the decision processes and the involved data imbedded in the information entities, a derivation of the functional requirements was carried out to support and guide the selection of the technological infrastructure of a dedicated farm management information system (FMIS). The study employed the core-task analysis (CTA) method involving a combination of science-based modelling, practice-based modelling, and integrated information modelling. The “process” entities of the information flow model which represent the usage processes of the information, and of the “information” entities which represent the data elements were identified for the specific case of fertilising. This identification of the usage processes as well as the associated data elements showed the complexity of the decision making process within the domain of field operations. In a fully structured and formalised information flow decomposition, many actors are required to deliver information to the decision processes in order to fully emulate the tacit knowledge that the farmer are currently using. Especially, the concept of assisting services has to evolve in order to sustain the need of more automated decision processes in the future. New information management concepts and designs mean that farmers have to be ready to adopt new working habits and perhaps also undergo further training.

The agricultural production sector faces increased pressure in terms of reduced margins of earnings. Farmers are forced to reduce production cost, maximise their physical output while maintaining the highest product quality (e.g., Andersen et al., 2007 and Sorensen and Bochtis, 2010). These requirements go hand in hand with adherence to strict environmental, social, health, and safety regulations (e.g., certification schemes such as International Food Standard (IFS) and GLOBALGAP, Albersmeier et al., 2009). The use of information and communication technology (ICT) and farm management information systems (FMIS) and decision support has shown great promise for achieving the above goals, especially in the context of precision agriculture (Godwin et al., 2003 and Nikkila et al., 2010). Murakami et al. (2007) found that the most important requirements for a farm management information system (FMIS) include: (a) a design aimed at the specific needs of the farmers, (b) a simple user-interface, (c) automated and simple-to-use methods for data processing, (d) a user controlled interface allowing access to processing and analysis functions, (e) integration of expert knowledge and user preferences, (f) improved integration of standardised computer systems, (g) enhanced integration and interoperability, (h) scalability, (i) interchange-ability between applications, and (k) low cost. A dedicated design of a FMIS complying with the above requirements involves an identification and specification of the scope and boundaries, an identification of system components (actors, decision processes, etc.) combined with information modelling, and finally, as part of the overall knowledge management, an identification of knowledge content in decision processes and functional requirements. An outline of the essential entities and boundaries of a dedicated FMIS were defined by Sørensen et al. (2010a). The boundaries and scope of the system were analysed and described in terms of actors (entities interfacing with the system such as managers, software, and databases) and functionalities. The soft systems methodology (SSM) (Checkland and Scholes, 1999) was used for the development of a conceptual model for an effective FMIS based on information derived from pilot farms representing diverse conditions across the EU. The conceptual model was divided into four sections: internal data collection, external information collection, plan generation and report generation. The data collection and processing are an automated monitoring system, whereas the report and plan subsystems are to be initiated by the farm manager. The external repository contains information on standards, rules, all types of guidelines for farm activities etc., made available for the FMIS. This conceptual model is the first step towards the actual design of a novel FMIS. The second step of the specification of the FMIS concerned the material and information flows (Sørensen et al., 2010b). More specifically, the information flows and relevant input data were given for the strategic, tactical, and operational planning levels for field operations, together with the execution and evaluation phases. The information flow definitions included the actors and decision processes involved in the overall operation. Additionally, it was specified which information provided by designated partners and actors or produced by the system must be encoded. A user-centric approach to model the information flows for targeted field operations was used. From the six field operations (tillage, seeding, fertilising, spraying, irrigation, harvesting) analysed in the FutureFarm1 project, the information model for the fertilisation case was selected for analysis. By specifying in detail the information provided and the information required for the information handling processes, the design and specification of the information flows was derived. This involved explicitly specifying tacit knowledge of the farmer as a way to extend the FMIS design into automated decision-making. Core-task analysis and core-task demands were utilised as premises for the modelling of information flow from the farmers’ point of view (Norros, 2004). The information models were centred on the farmer as the principal decision maker and involved external entities as well as mobile unit entities as the main information producers. The next step following the conceptual modelling and the information modelling involves the specification of the knowledge content of the decision processes and the involved data imbedded in the information entities. This finalizing step in the design process together with the derivation of the functional requirements is the objective of the study presented here, and the results are further intended to support and guide the selection of the technological infrastructure of a generalised FMIS. In this way, the current study covers the third part of the FMIS design phases and is a continuation of previously published studies covering the first phase (Sørensen et al., 2010a) and the second phase (Sørensen et al., 2010b) as part of the EU research project FutureFarm.1

Information flow models present the information usage processes, so called decision processes, the information inputs needed and also the actors responsible for delivering or using the information. The content of each usage process and information input were described accurately, either in a way the reality is at the moment or in a way the reality is expected to be in the future. Each actor can determine the needs and requirements and act accordingly. This study identified the content of the “process” entities of the information flow model which represent the usage processes of the information, and of the “information” entities which represent the data elements. This identification of the usage processes as well as the data elements showed the complexity of the decision making process within the domain of arable farming. In fully structured and formalised information flow decomposition, many actors are required to deliver information to the decision processes in order to fully emulate the tacit knowledge that farmers and decision makers are currently using. Especially, the concept of assisting services has to evolve in order to sustain the need of more automated decision processes. New information management concepts and designs mean that farmers have to be ready to adopt new working habits and perhaps also undergo further training. Farmers can utilise different services more efficiently and they are able to outsource some of the tasks they had previously performed themselves. This indicates that active use of external services will increase.