Project No. 212117 FutureFarm D.3.1. Project no. Project title: 212117 Project acronym: FUTUREFARM Integration of Farm Management Information Systems to support real‐time management decisions and compliance of management standards Instrument: Collaborative project Start date of project: 1st January 2008 Duration: 36 months Thematic Priority: THEME 2 FOOD, AGRICULTURE AND FISHERIES, AND BIOTECHNOLOGY Deliverable 3.1 Revision: Final System analysis and definition of system boundaries Due date of deliverable: 31/12/2009 Actual submission date: 31/01/2009 Work package 3: “Analysis and specification of knowledge based farm management” Organization name of lead contractor for this deliverable: AU Authors: Sørensen, C. (AU), Bildsøe, P. (AU), Fountas, S. (CRTH), Pesonen (MTT), Pedersen, S. (UCPH), Basso, B. (UNIBAS), Nash, E. (Uni. Rostock) Accepted by Pavel Gnip, 25/2/2009 Accepted by Simon Blackmore, 26/2/2009 Project co‐funded by the European Commission within the Seven Framework Programme (2007‐2013) Dissemination Level PU Public PP Restricted to other programme participants (including the Commission Services) RE Restricted to a group specified by the consortium (including the Commission Services) CO Confidential, only for members of the consortium (including the Commission Services) FFD3.1_System_Analysis_FINAL X Page ‐ 1 ‐ of 19 Last printed 26/02/2009 15:29:00 Project No. 212177
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Summary The objectives of deliverable 3.1 are to identify the scope of a farm management information system (FMIS) and identifies the conceptual system framework, which will guide the evolutionary process of analysing the information flow; define the databases, the knowledge encoding, the requirements for on‐line control, etc. The specific output from deliverable 3.1 becomes the preliminary scope and system boundaries and will function as the starting point from which the expectations and constraining requirements to the system can be further explored. Deliverable 3.1 will lay the qualitative foundation for the requirements analysis in other subtasks. This report identifies the system boundaries for the entities which will be included in the subsequent analyses and developments. Formally, the system analysis will consist of narrowing down and further elaborate on the drivers and preliminary system identifications in WP1 and WP2 by conceptually modelling the actors, processes, information flows, etc. in the constrained arable farm system and selected external entities. This activity will also build on existing research on farmer’s decision making behaviour in information‐intensive agriculture. The results from the study have included the derivation of a baseline system involving crop and farm production, operations management and the use of farm management standards for production as well as monitoring and compliance. An analysis of the farm manager has identified the internal as well as external conflicts and problems that the farm manager currently faces. Based on this analysis, the boundary of the targeted system was defined and within the context of these boundaries a description of the proposed system were derived and a conceptual model were set up. The system analysis used a Soft System Methodology (SSM) approach, providing the guidelines for system identification and system design aimed at proposing an integrated FMIS that can support real‐time management decisions and support reporting as well as application generation and improvement of the monitoring of compliance to management standards. FFD3.1_System_Analysis_FINAL Page ‐ 2 ‐ of 19 Last printed 26/02/2009 15:29:00 Project No. 212177
1
1.1
1.2
2
2.1
2.2
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INTRODUCTION ......................................................................................................................................- 4 OBJECTIVES OF D3.1............................................................................................................................... - 5 CONCEPT OF MANAGEMENT INFORMATION SYSTEMS (MIS)............................................................... - 5 MATERIAL AND METHODS .................................................................................................................- 6 EMPIRICAL DATA AND INFORMATION ................................................................................................... - 6 ANALYSIS PHASE ..................................................................................................................................... - 7 -
2.2.1
Soft system methodology ..............................................................................................- 7 2.2.1.1 Analysis of the current situation .................................................................................- 8 2.2.1.2 Definition of the system..............................................................................................- 8 2.2.1.3 Conceptual model .......................................................................................................- 9 3
3.1
3.2
RESULT ......................................................................................................................................................- 9 THE CURRENT SITUATION ...................................................................................................................... - 9 DEFINITION OF THE SYSTEM ................................................................................................................ - 11 -
3.2.1
3.2.2
3.2.3
3.3
3.4
CATWOE ....................................................................................................................- 11 Root definition.............................................................................................................- 12 The 3Es.........................................................................................................................- 12 -
CONCEPTUAL MODEL ........................................................................................................................... - 12 DECISION MAKING AND THE MANAGING OF AGRICULTURAL OPERATIONS ...................................... - 16 -
4
CONCLUSION .........................................................................................................................................- 17 -
5
REFERENCES .........................................................................................................................................- 17 -
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1 Introduction The managerial tasks for arable farming are currently transforming into a new paradigm, requiring more attention on the interaction with the surroundings (e.g. environmental impact, terms of delivery, and documentation of quality and growing conditions) (e.g., Sigrimis et al., 1999; Dalgaard et al., 2006). Among other things, this managerial change is caused by external entities (government, public) applying increasing pressure on the agricultural sector to change production from a focus on quantity to an alternate focus on quality and sustainability (Halberg, 2001). This change has been enforced by provisions and restrictions in the use of production input (e.g. fertilisers, agrochemicals, etc.) and with subsidies as an incentive for the farmer to engage in a sustainable production. In general, this change of conditions for the managerial tasks on the farm has necessitated the introduction of more advanced activities monitoring systems and information systems to secure compliance with the restrictions and standards in terms of specific production guidelines, provisions for environmental compliance, management standards as prerequisites for subsidies, etc. Until now, the farmers most often have dealt with this increased managerial load by trying to handle a bulk of information in order to make precise decisions. The increasing use of computers and the dramatic increase in the use of the internet have to some degree improved and eased the task of handling and processing of acquired external information but still, the acquisition and analysis of available information have proven a demanding task, since information can be scattered over many sites and not necessarily interrelated and collaborative. Specific attempts to improve this situation has included the launch of “web‐based collaborative information system” developments, combining different information components (models, data, text, graphics) from different but collaborating sources (Jensen et al., 2001). However, such systems still has to be enhanced in terms of collaboration with automated acquisition of operational farm data and integration with the overall Farm Management Information Systems (FMIS). Advances in information and telecommunication technologies (ICT), like positioning system and sensors for monitoring of machinery performance or biomass status, will allow farmers to acquire vast amount of site‐
specific data which ultimately can be used to enhance decision‐making (Blackmore, 2000; Fountas et al., 2006). Currently however, much information collected by use of sensors or by manual registrations is not used, due to data logistic problems, leaving a gab between the acquiring of such data and the efficient use of this in agricultural management decisions making (Atherton et al,. 1999; Pedersen et al., 2004; Reichardt & Jurgens, 2006). Costs of time managing the data in many cases outweigh the economical benefits using data, why, for example, future use of wireless communication is very much in the demand (Speckman, 2001; Jensen et al., 2007). In all a refined and integrated solution to analysing and transformation of the acquired data is needed to improve decision making in the future (Fountas et al., 2005). With the current transformation of the agricultural sector, additional demands on the precision and integration of the planning and control functions have occurred, requiring that the planning tasks needs to consider the dynamic interaction of machine, biological, and meteorological conditions. This resembles the industrial adoption of computer‐integrated manufacturing (CIM) and it’s embracing of customised production followed by dynamic operations planning and control of operations. The industry has demonstrated how effective an integrated control of work operations can be, based on on‐line measurements combined with database and decision support information. In this regard, it has been shown that the enhancement of FMIS is more influenced by common business factors and drivers than specific farming activities (Lewis, 1998). Plan generation and execution of farm operations must be linked with a system monitoring effects of actions, unexpected events and any new information that can attribute to a validation, refinement, or reconsideration of the plan or goal. Plans must be presented in a conditional way, such that supplementary knowledge from observations, databases, sensors, and tests can be incorporated and integrated to revise the plan in the light of new information. This involves an extended use of modelling and simulation as opposed to providing a generalized optimal solution (Attonaty et al. 1999; Ohlmer, 1998). FFD3.1_System_Analysis_FINAL Page ‐ 4 ‐ of 19 Last printed 26/02/2009 15:29:00 Project No. 212177
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A detailed structuring and formalisation of physical entities and the information, which surrounds the planning and control of farm operations efficient mobile working units in automated agricultural plant production systems is a decisive prerequisite for the development of comprehensive and effective ICT‐system supporting the task management efforts. An increase in the adoption of new information technologies require that the functional requirements surrounding the use of such technologies be explicitly specified (Sørensen et al., 2007). The task of optimised field data management can be seen as comprising the following steps or procedures:  creating planned field operations  transferring or delivering the plans to the field with specified tasks  setting up the mobile work units for executing the planned operation  managing, controlling and recording the field operation  documenting the executed field operation for recordkeeping and managerial purposes By specifying in detail the information provided and the information required for the information handling processes, the design and functionalities of the individual information system elements can be derived. That is the case both for on‐board machinery information systems as well as for supporting service information systems. The information flows may be contextualised on different levels and in different details (e.g. Fountas et al., 2006, Sørensen et al., 2007) .
1.1 Objectives of D3.1
The objectives of deliverable 3.1 are to create a basic outline for a novel integrated information system framework for the production and operations management of arable farming. The identified framework will guide the evolutionary process of analysing the information flow; define the databases, knowledge encoding, requirements for on‐line control, etc. The specific output from deliverable 3.1 becomes the preliminary scope and will function as the starting point from which the expectations and constraining requirements to the system can be further explored. 3.1 will lay the qualitative foundation for the requirements analysis. The pursued steps include an investigation of the current management situation for crop production together with an identification of the system boundaries for the entities which will be included in the subsequent analyses and developments. The system analysis will be based on the Soft System Methodology (SSM) approach providing the guidelines for system identification, system design and the proposal of an integrated FMIS. The proposed system intends to support comprehensive operations management decisions, report and application generation as well as compliance to management standards. 1.2 Concept of management information systems (MIS)
Management information systems (MIS) is an integral part of the overall management system in an purposeful organisation comprising tolls like enterprise resource planning (ERP), overall information systems (IS), etc. ERP is an industry notion for a wide set of management activities which support all essential business processes within the enterprise. The management system support management activities on all levels as well as provide for the identification of key performance indicators (KPI’s) (Folinas, 2007). Typically, ERP is integrated with a database system and will often include applications for the finance and human resources aspects of a business. As a part of the ERP, the information system (IS) refers to data records and activities that process the data and information in an organization, and it includes the organization's manual and automated processes supporting the business processes (e.g. Buckland, 1991; Bidgoli, 2002). Information systems are the software and hardware systems that support data‐intensive applications. Especially, information systems provide the possibility to obtain more information in “real‐time” enabling a close monitoring of the operations performance and enhance the connection between executed operations and the strategic targets of the enterprise (Lyons, FFD3.1_System_Analysis_FINAL Page ‐ 5 ‐ of 19 Last printed 26/02/2009 15:29:00 Project No. 212177
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2005; Folinas, 2007). However, in terms of deriving the requirements for the information system design, often targeted information systems lack a definitive formulation. Different stakeholders have different perspectives on what is and what is not the most important to be included in the design of an information system. MIS differ from regular information systems because the primary objectives of these systems are to analyse other systems dealing with the operational activities in the organization. In this way, MIS is a subset of the overall planning and control activities covering the application of humans, technologies, and procedures of the organisation. Within the field of scientific management, MIS is most of ten tailored to the automation or support of human decision making (O’Brien, 1999). Figure 1 shows the conceptually decomposing of the different management systems in an organisation. Farm system
Enterprise Resource Planning (ERP):
‐ enterprise‐wide and cross‐functional system aimed at coordinating all the resources, information, and activities required to complete business processes Information systems (IS):
‐ the application of people, documents, technologies and procedures
‐ solving business processes comprising related, structured activities or tasks that produce a specific service or product
Management information system (MIS):
‐refer to the group of information management methods
tied to the automation or support of human decision making
• decision support system
• expert system
• executive decision support
•…
Figure 1: Concept of management information systems.
By following this conceptual framework and notation, a FMIS is depicted as a planned system of the collecting, processing, storing and disseminating of data in the form of information needed to carry out the operations functions of the farm. 2 Material and methods
The boundaries and scope of a system can be described in terms of users and use‐cases, where users are entities interfacing with the system (e.g. managers, software, databases). The use‐cases describe the functionality of the system or what the users want the system to do. In order to analyse the complex and soft‐
systems situations of how to develop an effective FMIS, the soft systems methodology (SSM) was used (Wilson, 2001). This approach has been successful used in many fields of applications (e.g. Macadam et al., 1990; Kasimin et al., 1996) and involves identification of the scope of the system, identification of user requirements, conceptual modelling, etc. 2.1 Empirical data and information
The system analysis and boundary specification was based on extracted data and information from the pilot farms involved in FutureFarm. These involved the WIMEX farm located in Germany, MESPOL MEDLOV farm in Czech Republic, the Markinos Farm in Greece, and the Bramstrup farm in Denmark. The selected farms represent wide ranges of regional characteristics in Europe as well as farm types. In the updated description it is addressed, that these ranges are necessary to allow sufficient coverage of relevant FFD3.1_System_Analysis_FINAL Page ‐ 6 ‐ of 19 Last printed 26/02/2009 15:29:00 Project No. 212177
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patterns of information demand and flow as well as their technical handling on the so widely different European farms. The four pilot‐farms represent (i) large conventional cash‐crop farming systems as being typical for main‐
stream farming, having specific problems in managing huge information clusters, internally as well as externally [WIMEX, DE], (ii) cash‐crop farming systems under transformation conditions and growing in size with their specificity in a still very dynamic information environment and internal management [MESPOL MEDLOV, A.S., Cz], (iii) organic farming systems with special information management related to long‐term planning, ecology and consumer‐relations [BRAMSTRUP ESTATE], (iv) family farming systems with cash‐crop production plus specialisation in a perennial crop and thus mixed information characteristics [MARKINOS, Gr]. 2.2 Analysis phase
The analysis phase involves the identification of the system components applied to farm management and operations and the consideration of processes, conflicts and thoughts of an organization as the basic outset for the design of a dedicated farm information system. The topics that should be addressed are:  What are the main processes the farm is dealing with (e.g. public entities, commercial companies, etc)  What is working well in the farm (regarding processes) and what is not working well and what would the farmer like to do about it.  Regarding information processing, what would the farmer like to have to make their daily working life easier and to run the farm more effectively? 2.2.1
Soft system methodology
In order to propose an ICT system that can help the farm manager in decision‐making, a system analysis have been carried out. Initially the problems and conflicts within farming both internal and external where found. Soft system methodology (SSM), where used as guidelines to perform the analysis and conceptual modelling. Figure 2: An outline of the process of soft system methodology (Checkland 1988).
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Figure 2 illustrates the process of SSM, basically comprising of the modelling of human activity systems, which can then be compared with the real‐world in order to facilitate and structure further discussions and elaborations. The outcome from such process is models widely agreed to be relevant in the particular situation. SSM are divided into three main parts, exploring the current situation, building models and taking action (Checkland, 1988). As illustrated in Figure 2, the approach is based on a comparison between real‐world problem situations and conceptual models of proposed relevant system for dealing with these problems (Nidumolu et al. 2006). In summary, the applied approach is concerned with expressing the current status quo of the system including inherent problems within the perceived system, derive the actual goal of the targeted system (“root definition”), and next, construct a plausible conceptual model containing the proposed system amendments. The continuous and incremental elaborations and modifications of these procedural steps will provide an action plan for the system design. 2.2.1.1 Analysis of the current situation
In order to achieve a better understanding of the current situation of the farm manager, the managers of the pilot farms were asked to give their opinion which then formed the basis for developing a rich picture produced to describe soft and hard facts and the conflicts and problems that a farm manager currently are faced with, both from an external and as well as an internal point of view. The term ‘rich picture’ as used in soft systems methodology originates from recommendations made by Checkland & Scholes (1999), where this concept sums up the results of what the analyst undertake, as one of the first stages in the analysis of a problem situation and one as rich as can be assembled in the time available. In this sense, a rich picture is an appreciation of the problem situation rather than a diagram as such, and the real utility of the picture is not in the picture itself, but in the process of constructing the picture. Rich pictures are a compilation of drawings, pictures, symbols and text that represent a particular situation or issue from the viewpoint(s) of the person or people who drew them. They can show relationships, connections, influences, cause and effect but also more subjective elements such as character and characteristics as well as points of view, prejudices, spirit and human nature. Rich pictures can both record and evoke insight into a situation and may be regarded as pictorial 'summaries' of the physical, conceptual and emotional aspects of the situation at a given time. They are often used to depict complicated situations or issues and can be drawn both prior to analysing a situation, when it is unclear which parts of a situation are important and as an inquiry proceeds, as a mean to monitor what, if anything has changed. They help show which parts should be regarded as structure and which as process. 2.2.1.2 Definition of the system
In order to be able to model the purposed changes to the analysed situation, the proposed system need to be clearly outlined and defined. This definition is called the root definition and this concept plays a central roll in the analyses and modelling as it defines the goal of the system and brings forth various perspectives on a system and the inherent assumptions (Bergvall‐Kareborn et al. 2004). The root definition is devised in the form PQR. A system to do P, by means of Q to achieve R or “What to do (P), How to do it (Q), and Why do it (R)” (Checkland & Scholes, 1999). Special attention should be paid to the elements of CATWOE, a mnemonic word representing the terms Customers (C), Actors (A), Transformation process (T), Weltanschauung (W), Ownership (O) and Environmental constraints (E). The core of CATWOE is the T and the W, where the weltanschauung depicts the world view for which the system has meaning and the transformation depicts functionality on system level. Customers are the ones influenced by the transformation as they benefits and suffer from it and the actors are the ones that carry out the system activity and the Ownership belongs to the ones with the power to initiate or terminate the activity system. Finally, the Environmental constraints represent elements, which are taken as outside the system and imposed on the system. Checkland & Scholes (1999) argue that the CATWOE transformation is more elaborated since it includes additional and related elements and when included, will lead FFD3.1_System_Analysis_FINAL Page ‐ 8 ‐ of 19 Last printed 26/02/2009 15:29:00 Project No. 212177
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to transformation is more elaborated since it includes additional and related elements and when included will lead to enriched root definitions and hence, better models. Experience shows that omitting from any of the elements in the CATWOE definition will cause the analysis to suffer. Since it is agued that the purposed changes are purposeful, the performance of the system can and must be evaluated. This is attained by employing the 3Es, efficacy, efficiency and effectiveness, which encompass check of availability of system output, check on minimised resource input as well as a check on whether the system output supports the customer. 2.2.1.3 Conceptual model
Conceptual models of the system are built on the premises laid down in the definition of the system. The derivation of a conceptual model is the first step, indicating the concept of the information system to be designed. Although the analyse and model process can be elaborated in rather clear points, the outcome of each point is actually continuously changing as more knowledge is generated as a consequence of the improved understanding of the system. 3 Result
3.1 The current situation
The rich picture shown in Figure 3 illustrates the current situation with its problems and conflicts. As can be seen, system structure is very complex and many external as well as internal entities and partners have an interest in farming system. Figure 3: The current situation with internal and external conflicts and problems. The drawing is based on general elaborations and answers to questions posed to farm mangers and the point of view of external partners involved in the FFD3.1_System_Analysis_FINAL Page ‐ 9 ‐ of 19 Last printed 26/02/2009 15:29:00 Project No. 212177
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study. SW = soft ware, the dark clouds symbolise conflict or problems, whereas the think bobbles represent wants or need for the future. In terms of information handling, the farmer needs to manage a lot of information in order to make economical and environmental sound decisions. Currently, this process is very labour intensive and for most parts, executed manually. The important concerns and problems voiced by the farm manager include the time consuming tasks of monitoring field operations, manage the finances and application for subsidies which is further complicated by the lack of integrated soft and hardware to manage this work and the lack of coordination when such programs do exists. Also, the farmer voice a need for additional information and advanced technologies to manage monitoring and data acquisition on‐line in the field. When looking at the external concerns, it is seen that this mostly concerns the need for sustainable production of farm products, which is further pursued by regulations and the possibility to receive subsidies when more sustainable management practises are abided by. Table 1 lists some of the voiced concerns.
Table 1: Voiced concerns
System components
Farm manager
Description
Central decision maker at the farm
Performance monitoring
Tasks or tools capable of collecting data/
information on activities and processes at
the farm
External entities
Administration, district office, farmers'
association, local affairs, customers (e.g.
direct marketing), press and media,
research
facilities, producers of agricultural
equipment (direct or through distributer),
supplier of operating materials (like
diesel, fertiliser, pesticides, etc.), EU
Acquisition and
marketing
The acquisition of auxiliary materials and
the marketing of farm products
Employment
management
Counselling
Planning and control of farm employee
External component
The surroundings
Extension services, etc.
the environment and relation with
neighbours etc.
Problem, considerations
- often engulfed in routine tasks
- no time to concentrate on strategic
issues
- data/ information overload
- no cross-linking of information
- needs information in an automated
and summarised fashion
- lack of synergy effects
- understanding of farming is missing
- markets very dynamic causing harm
- communication with external entities
not optimal
- very complicated regulatory
framework imposed
- good communication with
commercial partners
- positive experience with direct
marketing
- lack of information on market
- acquisition of auxiliary material
confusing
- lack of easy accessible information
employee performance, etc.
- lack of user-friendly software tools
- low environmental impact
- limited pollution
- limit odour
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3.2 Definition of the system
To define the outline of the system a rich picture tuning in on the farm manager and the subsequent everyday management problems were derived making the system easier to comprehend – see Figure 4. This figure shows the perceived boundaries of the proposed system. Figure 4: The current situation seen from the farm manger’s point of view and the farm activity system for production of cops (the dotted circle). The stippled line, define the farm system boundaries. The central entities in the proposed system include the farm manager, the fields, the products and production input. The Ministry of Agriculture and the consumer as external components is added since the system is very much influenced by these entities. Based on the boundaries identified above, the system where defined. The derived situational elements of CATWOE are listed below. 3.2.1
CATWOE
Customers: The primary customer of the proposed information system is the farm manager and the management system as the demanders of data for production and operations management. The secondary customers is the EU, certification bodies, retailers, etc., understood as entities setting up and imposing standards and other regulatory frameworks for the farm production, and benefitting from the improved crop production. Actors: The actor is the one operating the information system, which in this case is the farm manager or other farm staffs. FFD3.1_System_Analysis_FINAL Page ‐ 11 ‐ of 19 Last printed 26/02/2009 15:29:00 Project No. 212177
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Transformation process: The transformation process involves the transformation of operational field data into a form, which can provide the foundation for decision making in crop production. Weltanschauung: The weltanschauung is the hypothesis that drives the information system development. In this case, the view is that operational data is easily acquired and can be use to improve management decision‐making though out the production cycle. Ownership: The Farm manager is the owner in the way that he has every day decision maker responsibility, and decides whether the system is of use or not. Environmental constraints: The constraints influencing the usability and performance of the information system includes the expectations of the entities imposing the standards and regulations on the farm, the requirements of the standards, the required data quality, the reliability and regulations of the information technology (communication devices, server, databases, etc.) 3.2.2
Root definition
The root definition of the purposeful activity handled here is: “a farm management information system (owned and operated on farm level) to support real‐time management decision‐making and compliance of management standards, by means of automated acquiring and contextualising operations data and external parameters (e.g. regulations, best management practises (BMP’s)), market information, etc.) to form a foundation for decision‐making. In order to improve the quality of decision‐making and reduce the time efforts”. 3.2.3
The 3Es
In order to evaluate proposed system upon implementation, the following indices are used: Efficacy – Efficiency – Effectiveness – Is the data collected and analysed? How much effort is used for obtaining the data (input) and transforming it and is it all used? Does it improve decision‐making? 3.3 Conceptual model
A rich picture of the concept of the proposed FMIS is illustrated in Figure 5 below. It can be seen that the system comprises a large number of sensors, etc. for data collection on the farm in the course of the crop production cycle. The automatically acquired data alleviate the monitoring of the operational activities. Furthermore, the structure of the FMIS should enable the interrelation with external systems (e.g. financial, marked, EU, etc.). FFD3.1_System_Analysis_FINAL Page ‐ 12 ‐ of 19 Last printed 26/02/2009 15:29:00 Project No. 212177
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Figure 5: The conceptual model FFD3.1_System_Analysis_FINAL Page ‐ 13 ‐ of 19 Last printed 26/02/2009 15:29:00 Project No. 212177
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Figure 6 show the processes in the conceptual model as derived from the definition of the system. It is depicted how the operational field data needs to be collected and transformed in an automatic way. The filtration of information (external as well as internal field operation data) is initiated by the manager according to the operational activity, which is to be planed. Based on this, an execution plan can be generated and send to the executer (e.g. the equipment or staff that is to carry out the operation) and finally, a record of the executed operations will be prepared. This reporting subsystem can furthermore be used in documentation of farm practice toward government or consumers. The external repository contains information on standards, rules, all types of guidelines for farm activities, etc. made available for the FMIS through a form of Service Oriented Architecture (SOA). The decision making and plan generation performed by the manager are influenced by a number of factors, such as experience, preferences, the availability of best management practises (BMP’s), the social context surrounding the manager, etc. On a more aggregated level, the manager as well as the operations management system is influenced by external drivers. These drivers affect the available resources, the environmental considerations, the preferences and behaviour of the customers, etc. – see WP1. Farm Activity Monitoring (FAM)
Data
acquisition
Data
transfer
Search external
information
External repository
FFD3.1_System_Analysis_FINAL Data
processing
Internal
repository
Info
filtration
Documentation
generation
Extract to
audit
Search
internal
info
Automated
validation
Operations
plan
generation
Plan
repository
Plan
execution
Initiated by farm manager
and influenced by:
- experience
- preferences
- best management practises (BMP’s)
- social context
-…
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Figure 6: The concept of the FMIS to be developed with the needed processes (rounded squares); it is divided into four
subsystems, internal data collection, external information collection, plan generation and report generation. The data
collection and processing is an automated monitoring system, whereas the report and plan subsystems are to be initiated by
the farm manager.
The system will include recognised strategies or external identified and imposed strategies, which the famer will have to comply in order to receive subsidies, assure quality requirements, etc. Within the context of these strategies, the farmer will apply personal practises governing the planning and execution of processes and operations within the production system. These personal practises will be influenced by the expected capability and characteristics of the applied technology targeting the selected field operations. Table 2 gives an overview of the included management strategies, which is integrated in the concept of the FMIS. Table 2. Management strategies included in the system (WP2 – D2.1.2). Recognized Personal Practices Technology Strategies Conventional Operations Maximized yield Cross Compliance Variable Rate Technology Maximized Return Organic (VRT) Minimized Integrated Crop Controlled Traffic Environmental Impact Management Contour cultivation Intercropping Water Conservation tillage Replenishment Conventional Robots Minimized Risk of Economic Operator assist Agronomic Failure Conventional Headland turns Minimized Financial Risk Subsistence Route Planning Minimized Cost of Production Tracking – Tracing Selective Harvesting Field Operations Tillage Seeding Fertilizing Irrigation Spraying Harvesting The standards included in the analysed system comprise Cross Compliance, Organic Farming, Integrated Crop Management, Water Policy, and INSPIRE and is described in detail in WP2, D2.1.1. These standards cover the main regulatory frameworks which are directed at farming. The identified field operations in Table 2 also frame the use‐cases, which will be studied in the course of the project. A use case defines a goal‐oriented set of interactions between external actors and the system under consideration (Booch et al., 1999). In the specific case using the proposed information system for standard compliance, the information flow surrounding planning, execution and evaluation of the identified/selected field operations is viewed as use‐cases initiated from external actors like the EU or national administrative bodies. Next, a scenario is an instance of a use case, and represents a single path through the use case. Thus, one may construct a scenario for the main flow through the use case, and other scenarios for each possible variation of flow through the use case (e.g., triggered by options, error conditions, etc.). The scenario analyses will be addressed in WP3.2. Selected scenarios to be analysed include : 1) automated steering and optimized route planning, 2) planning of fertilizer application and variable rate application, 3) variable herbicide spraying based on weed maps and weather forecast, 4) variable rate cultivation of soils based on soil maps, 5) harvest logistics harvest timing with fleet management, 6) variable rate seeding, 7) chlorophyll content measuring before harvesting to optimize harvest procedures, and 8) management of areal subsidies. These identified scenarios are detailed in WP5, D.5.1. together selected crops to be analysed. The planning and control activities and subsequent decisions for field operation ranges from manual planning systems to various degrees of automated planning involving parameterisation of the planned operation FFD3.1_System_Analysis_FINAL Page ‐ 15 ‐ of 19 Last printed 26/02/2009 15:29:00 Project No. 212177
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have been attempted. Figure 7: outlines the basic management processes and decisions which are identified within the agricultural plant production cycle for both manned and unmanned machinery items. The management activities concentrate on planning and controlling the execution of operations on some soil or crops (Sørensen, 1999). The decomposition of the information processes is based on the management functions ranging from strategical to operational planning, execution control and evaluation, and a number of underlying processes and sub‐processes. The operational plans are decomposed for formulation and control of the planned operations and tasks.
Arable farming
Strategic
planning
Tactical
planning
Operational
planning
Planned
operations
formulate
jobs
formulate
Revision of
task formulation
Task
formulation
terms
route
in in
terms
of of
instructions
for and
planning
vehicle/implement
task scheduling
jobs
formulate
tasks
formulate
tasks
modify
task
modify
tasks
Operational
crop planning
required
operations
operations
urgency
operations
specifications
Execution
Handling
tasks
select
task
inspect
task
Evaluation
Execute
tasks
control
task
control
operation
control
device
Figure 7: Information and planning activities in agricultural operations management with the identification of the
revised task formulation to be invoked in the case of compliance for standards (adapted from Sørensen (2007)
and Goense & Hofstee (1994))
In relation to Figure 7, it should be noticed that the activity of observing and monitoring is also to be regarded as an operation, which can be planned, executed and controlled in the same way as for the traditional machine operations (Sørensen et al., 2002). In this way, the task of observing/monitoring can be formulated according to the actual needs of observing or monitoring of the systems states, the costs‐benefits of acquiring a specific information, etc. 3.4 Decision making and the managing of agricultural operations
On the arable farm, field operations are carried out in relation to a number of different objects, like field, crop, operators, machinery, etc. The important aspect of efficient management at any level is efficient information processing. Figure 8 gives on an aggregated level the objects involved in field operation management together with their attributes describing the information needed. When field operations are planned and implemented, the central aspects of Figure 8 will evolve around farmers acquiring information on the current or future states of the objects involved in order to show, for example, compliance with standards. The farmers will then make their planning efforts and subsequently revise their plans according to the observations made. The next steps in the efforts of specifying the proposed FMIS will involve detailed information modelling of selected field operations.
O p e ra to rs
fu n k tio n
wage
h o u rs
w o r k in g
F a rm
nam e
a d d re s s
e n t e r p r is e
Land
a c re a g e
lo c a t io n
F ie ld
lo c a t io n
le n g th
b re a th
shape
a re a
F ie ld s ta tu s
s ta tu s
c h a ra c te r is t ic s
FFD3.1_System_Analysis_FINAL P la n n e d
use
p e r io d
v a lu e
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fie ld s ta tu s
M a c h in e r y
m a c h in e ry
c a p a c it y
P la n n e d fie ld
s ta tu s
tim e
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m a c h in e r y
s ta tu s
p e rio d
v a lu e
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Figure 8. Objects involved with the planning and implementation of field operaions
4 Conclusion
This research has shown the benefit of using dedicated system analysis methodologies as a preliminary step to the actual design of a novel FMIS. The soft systems methodology targets organisational business and process modelling and identify unstructured problems as well as identifying non‐obvious problem solutions. Specifically, the approach provides the possibility of more clearly capturing the change that is necessary for a current system to transform into a proposed system that will fulfil the user requirements of tomorrow. 5 References
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