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Computers and Electronics in Agriculture 73 (2010) 4455

Contents lists available at ScienceDirect

Computers and Electronics in Agriculture

j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m / l o c a t e / c o m p a g

A user-centric approach for information modelling in arable farming

C.G. Sorensen a,, L. Pesonen b, S. Fountas c, P. Suomi b, D. Bochtis a, P. Bildse a, S.M. Pedersen d

arhus University, Faculty of Agricultural Sciences, Department of Agricultural Engineering, Research Centre Foulum, Blichers Alle 20, 8830 Tjele, Denmarkb MTT Agrifood Research Finland, Vakolantie 55, 03400 Vihti, Finlandc Center for Research and Technology, Institute of Technology and Management of Agricultural Ecosystems, Technology Park of Thessaly, 1st Industrial Area, GR 385 00, Volos, Greeced Institute of Food and Resource Economics, University of Copenhagen, Rolighedsvej 25, 1958 Frederiksberg C, Denmark

a r t i c l e i n f o

Article history:

Received 21 August 2009Received in revised form 30 March 2010

Accepted 8 April 2010

Keywords:Precision agriculture

Fertilisation

Core-task analysis

FMIS

DFD models

a b s t r a c t

Agriculture and farmers face a great challenge in effectively manage information both internally and

externally in order to improve the economic and operational efficiency of operations, reduce environ-

mental impact andcomply withvariousdocumentationrequirements.As a partof meeting thischallenge,

the flow of informationbetweendecisionsprocesses defined as realizinga decision mustbe analyzedand

modelled as a prerequisite for the subsequent design, construction and implementation of information

systems.

This paper defines the actors, their role and communication specifics associated with the various

decision and control processes in farmers information management. Core-task analysis and core task

demands from earlier research are utilised as premises for the modelling of information flow from the

farmers point of view. A user-friendly generic FMIS design reference model is the primary objective for

the study in which planning, execution and evaluation measures have been incorporated.

A user-centric approach to model the information flows for targeted field operations is presented. The

information modelsare centred aroundthe farmeras theprincipaldecisionmakerand involves external

entities as well as mobile unit entities as the main information producers. This is a detailed approach

to information modelling that will enable the generation of a Farm Management Information System in

crop production.

2010 Elsevier B.V. All rights reserved.

1. Introduction

The analysis of decision processes, as well as information mod-

eling for field operations is not a new approach. Decision-making

is an important aspect in farm management and has been stud-

ied by numerous studies (e.g. Anderson et al., 1980; Van Elderen

and Kroeze, 1994). The reasoning of why there is a need to ana-

lyze decision-making has been addressed by Gladwin (1989), who

argued that the benefit is to know and understand why a specific

group of people acts as they do. This will enable researchers to pro-

vide the farmers with supporting knowledge and tools as a way to

enhance decision-making at specific stages of the process. In agri-culture though, farmers, in general, both generate and execute any

plan made, andtheir decision process associated with the planning

remains very much implicitand internal(Srensen, 2000) and often

make decisions based on their intuition and not using formalized

planning tools. That is contradictory to the industry, where there is

a long tradition for explicit planning comprising formalised docu-

ments passed down to the shop floor by the management section

Corresponding author. Tel.: +45 89991930.

E-mail address: [email protected](C.G. Sorensen).

for implementation (Chary, 2006). The efforts aimed at developing

agricultural planning support mustbe targeted at externalising and

formalising the farmers planning effort.

Kay and Edwards (1999) discussed the unique attributes that

make farm business complex in comparison to the industry, such

as the biological processes, the fixed supply of land, the small

size, weather forecast and the perfect competition (Runge, 2006).

They argued that the systematic analysis of decision-making pro-

cess would not necessarily lead to perfect decisions, but would

help a farm manager act in a logical and organized manner when

confronted with choices. The US North-Central Regional Research

team in Farm Information Systems (2000) categorized the farm-ers into two groups: information hogs seeking and using large

amountof informationand seatof thepantswherepersonalintel-

lect and intuition are the main drivers in decision-making. They

observed that seat of the pants farmers most likely utilize infor-

mation in ways not fully understood by researchers or advisors.

It is perhaps useful to recognize intuition as a complex result of a

given farmers unique experience and familiarity with his/her farm.

Much of the information exists as tacit knowledge of the farmer,

but in order to specify all the elements it is necessary to explic-

itly specify the detailed information flows for individual planning

tasks.

0168-1699/$ see front matter 2010 Elsevier B.V. All rights reserved.

doi:10.1016/j.compag.2010.04.003
http://dx.doi.org/10.1016/j.compag.2010.04.003http://dx.doi.org/10.1016/j.compag.2010.04.003http://www.sciencedirect.com/science/journal/01681699http://www.elsevier.com/locate/compagmailto:[email protected]://dx.doi.org/10.1016/j.compag.2010.04.003http://dx.doi.org/10.1016/j.compag.2010.04.003mailto:[email protected]://www.elsevier.com/locate/compaghttp://www.sciencedirect.com/science/journal/01681699http://dx.doi.org/10.1016/j.compag.2010.04.003


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C.G. Sorensen et al. / Computers and Electronics in Agriculture 73 (2010) 4455 45

With the advent of Precision Agriculture (PA) technologies

farmers acquire a vast amount of data and they face even big-

ger problems on how to effectively utilize them to make better

decisions (Auernhammer, 2001). PA aims to change the focus of

agricultural production from quantity to quality and sustainability

( Jensen et al., 2000). By generic definition, PA refers to agricul-

tural techniques that increase the number of (correct) decisions

per unit area of land per unit of time, with associated net benefits

(McBratney et al., 2005). When practising PA, a farmer manages

crop production inputs (seed, fertiliser, lime, pesticides, etc.) on

a site-specific basis to increase profits and crop quality, but also

to reduce waste and maintain environmental quality. In order to

make precise decisions in different phases of the farming process,

the farmer therefore needs to analyse information from different

vastand dispersedlocatedinformation sources. Managementof the

information and decision-making is the core issue for the farmer in

successful PA, not the data acquisition process.

Fountas et al. (2006) have decomposed the decision-making

process for farmers using PA into twenty-one decision analysis

factors. These factors were assembled into a data flow diagram

describingthe main information processes and flows.The diagrams

analyzed theinformation flows forfield operations taking a general

approach to represent the transformation from gathered data into

informationandthendecisions.Nash etal. (2009) modeledallrangeof data flows covering the broad spectrum of PA practices into one

very large diagram showing the interrelations, while also including

the modeled data-streams for specific PA practices, such as man-

agement zones, yield mapping or exploitation of remote sensing

data.

The required information modelling can be fulfilled through

concentrated efforts aimed at extracting domain knowledge and

deriving information flows at various planning and process lev-

els. This effort demands considerable research and development,

which is the case in terms of incorporating user preferences and

requirements. The tendency to use a more user-centric approach

in developing new technologies has gained considerable appeal

(e.g. Akao and Mazur, 2003; Norros, 2004). The core-task analysis

(CTA) is a user-centric methodology, which was initially devel-oped in Governmental Technical Research Centre of Finland (VTT:

Valtion Teknillinen Tutkimuskeskus) (Norros, 2004). It is a func-

tional modelling technique that informs system modellers of the

aims, intrinsic constraints and user practices in the work under

study A user-centric approach assumes that the users ideas and

requirements reactions concerning the specific characteristics of

the designed technology are integrated in the subsequent design.

When end-users and other actors in the value chain are involved

into the design and development process from its early stage, the

system becomes more realistic to realise and build in real world,

andit readily meets most of theuser requirements. Since the adop-

tion of Decision Support Systems (DSS) and Farm Management

Information Systems (FMIS) within PA has been disappointingly

low (Rosskopf and Wagner, 2003; McBratney et al., 2005; Parker,2005), this kind of user-centric development method is expected

to improve the acceptance of the new technology in the markets

and among end-users. This leads to smaller risk associated with

introducing new FMIS in the farm business domain (Norros et al.,

2009), which was communicated in a research project, InfoXT1

user-centric mobile information management in automated plant

production running from 2006 to 2008 in Scandinavia (Pesonen

et al., 2008). Here,the applicability of using a user-centric approach

to develop an information system for mobile work units was indi-

cated.

1

www.mtt.fi.infoxt.

The aim of this paper is to define the actors, their role and

communication specifics associated with the various decision and

control processes in farmers information management. The core-

task analysis involving farmer interviews and derived Core-Task

demands is the basic framework for the pursued approach. A user-

friendly generic FMIS design reference model is the primary target

for the study where both planning, execution and evaluation mea-

sures are incorporated. The generic design is intended to support

and guide the actual implementation of a specific FMIS in terms

of capability, invoking of information and communication tech-

nologies, etc. The design reference model is developed in the early

phases of system design and the detailed decomposition and com-

ponent construction can be derived from this model.

This study was part of an on-going EU research project

FutureFarm.2 FutureFarm has defined aims at meeting the chal-

lenges of the farm of tomorrow by integrating Farm Management

Information Systems (FMIS) to support real-time management

decisions and compliance to standards.

2. Farm management and field operations

The agricultural production processes within arable farming

involve transformation processes that are realised by biologicalprocesses (e.g. crop biomass growth) taking place in the course of

the growing season. The processes are regarded as an autonomous

system, which is basically independent of decisions made by the

farmer. In contrast to this, the intervention realised by labour and

machinery during the plant growing process is dependent on deci-

sions made by the farmer and termed an operation. An formaldefinition of an operation is given by Van Elderen (1977), who

statesthat an operationis a technicalcoherent combination of treat-ments by which at a certain time a characteristic change of conditionof an object (a field, a building, an equipment, a crop) is observed,realised or prevented. This definition extends operations beyondthose for crop production to supporting enterprise functions like

maintenance, repairs, etc. An operation is generallyseen as the link

between some resources (e.g. labour and machinery), some mate-

rials processed, and some material produced (e.g. harvested crops,

repaired machine, etc.).

The decomposition of information processes attributed to the

planning and execution of field operations is based on the manage-

ment functions ranging from strategical to operational planning,

execution control and evaluation, and a number of underlying pro-

cesses and sub-processes. All planning levels have to be included

in a generic FMIS as it is necessary to know what kind of infor-

mation the system has to be able to handle. Farmers cannot have

separate systems for each managementlevel. All levelsutilise/need

data/information produced in the other levels. The integration of

all planning levels is pivotal to the usefulness of the FMIS. Fig. 1

outlines the basic management processes which are identified

within theagricultural plant production cycle forboth manned and

unmanned machinery items.

Plan generation and execution must be linked in a system

monitoring effects of actions, unexpected events and any new

information that can attribute to a validation, a refinement, or a

reconsideration of the plan. Plans must be presented condition-

ally, so that supplementary knowledge from observations, farm

databases, sensors, etc.,can be incorporated in orderto reviseplans.

It should be noted, that although that the concept of farm

databases is an important issue in the modelling of a FMIS, it is not

within the scope of the paper. The pursued concept, in principle,

does not make any difference between information in a database

2

www.futurefarm.eu .
http://www.mtt.fi.infoxt/http://www.futurefarm.eu/http://www.futurefarm.eu/http://www.mtt.fi.infoxt/


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46 C.G. Sorensen et al. / Computers and Electronics in Agriculture 73 (2010) 4455

Fig. 1. Information and planning activities in agricultural operations management with the identification of the revised task formulation to be invoked in the case of

autonomous vehicles (adapted from Goense and Hofstee, 1994).

and the information present in the memory of the farmer. It is

all available information which can be drawn upon and where

the distribution of the available information between a physical

database and the memory of the farmer depends on the degree

of automated decision-making and as such, the degree of explicitly

formulated information storage.

3. Methodology

The information modelling used in this study utilises the user-

centric research and designing method, the so-called core-task

analysis (CTA) method, developed in earlier work carried out in

Scandinavia (Norros, 2004; Nurkka et al. 2007; Pesonen et al.,

2008). The method employs ISO 13407 standard (ISO 13407, 1999,Human-centred design processes for interactive systems) aiming atgood systems usability by integrating the end-users to the devel-

opment process from the beginning. The method consists of seven

phases (Fig. 2) and it is based on core-task analysis (CTA) of farm

work. The phases of the method are shown in Fig. 2. CTA is a func-

tional modelling technique that informs system modellers of the

aims, intrinsic constraints and user practices in the work under

study. In the method, it is seen that a result-oriented and meaning-

ful human-environment interaction is a functionalsystem in which

the environment provides possibilities (affordances) that human

actors learn to grasp (prehensilities) (Norros and Savioja, 2006).

Fig. 2. Seven phasesof thecore-task analysis-based userdemandmodelling system

(Nurkka et al., 2007).

Affordances and prehensilities of a particular domain and the aimed

results constitutethe core-task of that activity.The modellingof thecore-task gives the potential for action in the particular context.The CTA method was developed further and applied in a study that

aimed to improve information management of high quality cereal

(malt barley) production (Nurkka et al. 2007). The CTA method

attained the form where it consists of two parts: research and

design (Fig. 2).

The methodology employed in this study, utilises basic work of

phases 14 of Fig. 2 for targeted farm operations. Science-based

modelling of a core task was the first phase of the modelling

relying on expert knowledge of the use of PA practices within

crop production. Scientific and professional literature, four expert

interviews and several workshops with the research group pro-vided the data for the modelling. The aim was to indicate the

content of the relevant and important information regarding the

decision-making for the targeted field operations under study.This

information comprised the information used in planning, execu-

tion and evaluation, together with the core-task demands. The

core-task demands define how farmers use skills, knowledge and

collaboration to control dynamicity, uncertainty and complexity

of crop production work (Nurkka and Norros cit. Pesonen et al.,2008).

Analysis of orientation (phase 2) was carried out in Finland

among 11 interviewed producers to identify those farmers who are

keen on constantly improving field processes to gain good product

quality and environmental friendly methods, in other words the

farmers who possess PA orientation (Nurkka et al., 2007).In the third phase, as part of the practice-based modelling, indi-

vidual farmer interviews were carried out in a half-day workshop

organized at four farms in Finland (Nurkka et al., 2007) and at five

farms in Sweden (Olsson and Rydberg, 2008). During the work-

shops, the farmerspracticeswere simulatedby inviting the farmers

to draw their own conceptual models of the growing process based

on theexperiences of thepreceding farming period. The modelwas

toportraythe actions they made andtools they used duringthe pro-

cess. They were encouraged tomake as many comments as possible

on the content, origin and type of the information they used and

how they used it in different phases of the process. The aim of this

phase was to understand what the obstacles are in the use of the

systems to support PA and what the actual needs for the farmers

are regarding FMIS.


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Fig. 3. Analysis and modelling approach.

In the phase 4 (Integrated Information Modelling), all the data

from the preceding modelling were applied. The work was done

in workshops comprising agronomists, agrotechnology scientists

and farmers. This phase was creative and iterative combiningresearch and practice, and the first drafts of information models

were designed as a result. Ideas about the possible technological

advances were taken into account. The modelling was an itera-

tive process having current farm technology as a foundation, but

also looking for the possibilities to utilise efficientlyadvanced tech-

nologies like ISOBUS and wireless communication. However, at all

times, the information flow models were created and revised from

the farmers (end-users) point of view.

A detailed structuring and formalisation of physical entities

and the information, which surrounds the planning and control

of efficient mobile working units is a decisive prerequisite for the

development of comprehensive and effective ICT-system for taskmanagement on the farm. The basic idea was to capture the high-

level planning and control activities, which take place in a targeted

production section, and represent explicitlythe domain knowledge

in terms of domain entities and their relationships, (Fig. 3), and

based on the basic CTA approach and work presented above

The actors are defined as information operatives or as the enti-

ties which are capable of storing or processing information by

way of the explicitly defined decision processes. The defined actors

Table 1

Planning levels and aggregated information flows in field operations.

Planning level Information required Information provided

Strategic planning or design of the production system : Design

of production system for a period of 15 years or 2 or morecropping cyclesspecifically the labour/machinery system

and selection of types of crops

Possible production levels and price developments Number and dimensions of machines

Operations demands Machine capacityPossible work methods Labour requirement

Available machinery on the market Crops selected

Costs

Tactical planning: Setting up a production plan for a periodof 12 years or 12 cropping cycles.

Strategic plan Crop plan

Availability of land, buildings and equipment Machinery replacement

E xtern al/inter nal standar ds Fer tiliser /che mical application plans

Maintenance plans

Labour budget (peak loads)

Operational planning: Determining activities in the comingcropping cycle, i.e. within the coming season

Tactical production plan Required/optional operations

Internal/external standards Operations urgency

Maintenance plan for land, buildings and

equipment

Operations specifications

Scheduling: Work scheduling setting up formulations ofjobs. Planning the implementation of work in the

short-term.

Required operations Work plan for planned operations indicating:

Urgency of operations Starting time

Soil and crop status Duration

Weather forecast Work-sets requiredWorkability criteria

Availability of labour and equipment


Task formulation: Handling tasks concerning inspection offormulated tasks

Equipment breakdowns Deviation from plans/schedules

Unavailable material

Change in soil, crop or weather conditions

Priority changes

Execution: Controlling tasks, and work-setsperformancetask control and operation control

Work time elements (effective time, ancillary time,

preparation time, disturbance time, etc.) on

work-sets

Realised work time


Realised capacity

Set point values for implement

Evaluation: Comparing planned and actual executed tasks Realised work time Deviations from planned tasksRealised capacity

Realised yield

Documentation information


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include farm managers, machine controllers, external services, etc.A close coupling of the actors with the decision processes exists

as the actors are the executers of these processes in terms of

deciding which actions to take in relation to the execution of

activities defined as operations (goal-specific work as required by

the relevant production system) and tasks (the physical imple-

mentation of the operation in terns of resources). The decision

processes are influenced by a number of factors including strate-

gies (e.g. the farmers preferences for a specific production form),

triggers (e.g. weather conditions determining the planning and

initiation of field operations), and timing (e.g. the degree of time-

critical decision-making, where the operational decision-making

is more time-critical than strategic decision-making). The infor-

mation used in the decision process is the required information

for making a rational decision, whereas the information produced

by the decision process comprises the planning, guidance and

control information used for actual implementing the specific deci-

sion.

The consecutive decision processes were organised into a dia-

gram with a sequential timespan so that the decision processtogether with data transfer with different actors form an informa-tion flow through the different decision levels; strategic, tactical,operational, execution and evaluation. The farm specific data arestored in a database from which the data are available to a spe-cific decision process whenever needed. It is to be noticed that the

timespan will vary according to the decision level as, for example,

a decision process is faster in execution than in strategic planning.

The outcome of this modelling is a devised information flow dia-

gram (IFD). The aim of this user-centric IFD is to introduce the

actors and their roles as needed in farmers decision-making asso-

ciated the core tasks. As a result, it was easy to involve the farmers

in developing and testing the model. Farmers were able to judge

whether thedifferent details of theinformationflow in thediagram

are correct and the proposed system usable for them.

In this study, the information model was developed further

from that reported in earlier studies. Here, six field operations

were analyzed and modelled as IFD: tillage, seeding, fertilising,

spraying, irrigation and harvesting. These are the main field oper-

ations in crop production and have been as the focal point in the

development of the FMIS in the FutureFarm project (Chatzinikos

et al., 2009). By decomposing the activities inherent in the six field

operations, the information, processes and actors involved in the

decision-making were recorded. The earlier modelling work was

evaluated, supplemented and expanded to cover the core task of

European farm work by FutureFarmproject researchers. Whilst the

main focus in previous work was in information management of

mobile work units, the main focus here was on farm planning and

real-time assistance. Thus, the automated compliance to standards

and checking of plans was added as a technological advance in the

modelling. The information models produced were cross-validatedwith the information modelling produced by Fountas et al. (2006)

with its independent data set.

Fig. 4. Legend describing used symbols and identified actors in information flow modelling.


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4. Results

Fromthe six fieldoperations analyzedin the FutureFarmproject,

the information model for the fertilisation case is presented in this

paper. Table 1 sets the framework for the different decision lev-

els together with the main parts of the information flows required

by the decision process or produced by the decision process. 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. This has involved explicitly specifying tacit knowledge

of the farmer as way to extend the FMIS design into automated

decision-making.

Thepresentation of theresults include a genericschematic illus-

trates the symbols and the terms used in the information models.

Next, the information models for the five levels of decisions are

presented, that is strategic, tactical, operational, execution and

evaluation.

Fig. 4 shows the scope of the information modelling for the fer-

tiliser case in termsof identifiedactors tobe included in thesystem.

These actors include external entities outside the farm, the farmer

as theprime decision maker andthe mobileunitentitiesinvolvedin

actual carrying outof the planned tasks, such as the task controller,

implement electronic control unit (ECU) and user interfaces likethe Virtual Terminal (VT) on the tractor. These mobile unit enti-

ties, which are grouped using the dashed line, can be from one to

many as during a field operation more than one fertilizer could be

utilized. Also, the actor identification is applicable to both manned

and unmanned mobile unit configurations.

Specifically, external entities include the market receiving the

produce from the farm production system, the advisory system

providing advise to the farmer upon requests, the government

system setting the rules and provisions for the farm production,

the weather service providing weather information either gener-

ally published or dedicated weather information upon request, the

agricultural service companies like a machine contractor providing

the service of executing specific work tasks, and the technology

providers like agricultural machine dealers. As compared to the

external entities, the internal entities include the farmer as the

prime executor of decisions, the farm database as the container

of farm relevant information, and the mobile unit entities compris-

ing individual elements of the work machines on the farm. These

latter elements involve information and communication measures

as well as principal traction units like the tractor. Following the

ISOBUS concept, the virtual terminal function as an interface to

the ISOBUS compatible implements while the task controller is

the prime communication device between the mobile unit and the

management system. As the data acquisition part, the internal sen-

sors provide information for the control of the planned task while

the external sensors provide information about the field, environ-

mental indicators, etc.As part of the strategic decision level, Fig. 5 focuses on the long-

term potential production dimensions (vision) of the farm and

involves a static description or analysis of the whole farm plan-

Fig. 5. First part of strategic planning focusing on the farm development. The information transfer arrows to and from decision process are in a sequence.


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Fig. 6. Strategic planning focusing on the choice of technology according to the relevant standards and management practices.

ning integrating the specifics of fertilising. External provisions like

environmental requirements and standards for the application of

fertiliser will influence the crop rotation scheme, the necessary

technical capabilities of the equipmentfor fertiliserapplication,etc.

Next, the fertilising strategy in terms of selected fertiliser types,

strategies for application, etc. is determined based on an overall

evaluation of the external requirements, the prices of production

factors and sales products (Fig. 6). The selection of fertiliser equip-

ment and the machinery size or capacity planning concerns both

a qualitative and a quantitative selection of machinery items as

related to the demand. The optimisation can be done by deter-

mining (1) demands put up by the operation to be performed, (2)

availability of equipmenton the market, (3) possibleworking meth-ods, (4) dimensions and capacity, (5) costs, and (6) use of own

machinery or contractors. Many types of models supporting this

optimisation have been launched, from simple deterministic mod-

els (see for instance Hunt, 1983) to more complex simulation and

linear programming models (e.g. Sgaard and Srensen, 2004). The

models involve the interaction between the labour and machin-

ery system and the biological and meteorological system involving

crops, soil, weather conditions, etc. The optimisation is done

while considering constraints like available labour and machinery,

timeliness functions and workability. The strategical planning of

machinery needs is strongly interconnected with the operational

planning.If this connection is nottakeninto consideration, a strate-

gically chosen plan could turn out to be non-executable, because it

would produce a non-workable schedule.

The determination of the necessary planning information in the

subsequent planning levels is considered an integral part of the

strategic selection of the fertilising technology. This involves deter-

mining the scope of the information and the adherent information

technology measures to be available as part of the tactical and

operational planning of the fertiliser application.

The tactical planning as depicted in Fig. 7 for the fertilising case

concerns the determination of the quantity and timing of the oper-

ational activity in the medium planning range.The tactical planning

involves an all-year setting up of thefertiliser plans andoperational

plans for the input of labour and machinery. Normative models for

the labour and machinery input given a specific crop plan and a

specific machinery inventory have been developed (e.g. Srensenand Nielsen, 2005; Achten, 1997). The maindecision involve select-

ing the fertilising features of the application equipment in terms

updated information from the technology provider, new adjust-

ment features, etc. as well as acquiring planning information about

available fertiliser types, recommendations on fertiliser use in the

light of prevailing weather conditions, available external services

which will be requested for service, etc.

The input of labour and machinery will be constrained within

the development of crop and weather, and consequently, peak

load periods will be unavoidable (Nielsen and Srensen, 1994). To

reduce the organisational impact of these peak load periods, crops

requiring different periods for treatment have to be selected. How-

ever, one thing, which reduces the impact of peak load periods

significantly, is that the labour force in agriculture is rather flex-


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Fig. 7. Tactical planning focusing on in-season fertilising features and information acquisition for the fertilisation planning according to the strategy.

ible when it comes to complying with the extra workload in these

periods.

The operational planning outlined in Fig.8 for thefertilisingcase

concerns determining when a work operation will have to be exe-

cuted in order to obtain a maximised work quality and maximum

labour and machinery input effectiveness. Concerning the specific

task of timing work operations, detailed job scheduling comes into

play. In an agricultural context, scheduling is defined as determin-ing thetime, when various operations areto be performed. Availabilityof time, labour and machinery supply, job priorities and crop require-ments are some important factors (ASAE, 1974). Work schedulingis the formulation of jobs based on required operations. Jobs can

be scheduled when soil, crop and weather conditions are withincertain limits. Planned jobs form the basis for task formulation (or

implementation). Task formulation involves the actual specifica-

tions of work-sets performing the tasks.

The main decisions involve selecting the final data acquisition

services that is required. That together with a subsequent direct

inspection of the field conditions will determine the actual layout

of the execution plan containing fertilising schedules, application

rates, etc.

The execution of the planned job in the operational planning

phase focus on final scheduling, task control and documentation

of realised work (Fig. 9). Task control concerns the work effi-

ciency of work-sets (measured by time elements and compared

with standard data). Operation control concerns the transforma-

tion of operation specifications into set points for various parts

of the equipment being used. Device control involves comparing

machine operation (e.g. work quality) with planned specifica-

tions.

The main decision process in this phase involve the inspec-

tion and controlling by the farm manager of the fertilising task

by receiving information about the on-going task execution and

take corrective actions if needed. Also, directly on the executing

machine, a more or less automateddecision process is running tak-

ing process information and converting it into actionable data for

on-line correctivemeasures. The workperformance is recorded and

the documentation data is stored to the farm database.

The final step in planning and control cycle for the fertilis-

ing operation involves the evaluation of the executed operation(Fig. 10). The documented field data need processing and aggre-

gation in order to be useful in the evaluation. The key point is a

comparison between the planned operation and the actual exe-

cuted operation. The result of this comparison will be integrated

in the subsequent planning cycle and will enable the manager to

adapt to theactual or unexpected conditions on the farm. The feed-

back increases the farmers situation awareness and readiness to

act in unexpected situations. The farmer gains new knowledge and

skills which he/she can utilise to improve the performance in the

subsequent planning cycle.

This phase involve 4 main decision processes: (a) data process-

ing for documentation, (b) compliance with standards check, (c)

summarising fertiliser performance, and (d) comparison with tar-

get. External requirements for documentation as well as internal


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Fig. 9. Execution focusing on final scheduling, task control and documentation of realised work.

are presented in order to discuss this connection. As a first exam-

ple, the problem of machinery selection involved in the strategic

planning level is considered. The intended optimisation tool is

supporting the planning process choosing fertilising technology

information and machinery and returning the output selected

fertilizing technology (Fig. 6). A non-linear programming model

like the one developed by Sgaard and Srensen (2004) may be

invoked and comprise the capacity of the machine/s and the num-

ber of the tractors and their power as decision variables. The

input factors included field area, the fixed annual cost, operat-

ing speed, working rate, repair and maintenance cost, and labour

cost, the fuels costs, and expected crop price, and the timeliness

cost.The second example regards the operational planning level

(Fig. 8). Bochtis et al. (2009) presented a mission planer for an

autonomous tractor covering also operations with capacity con-

straints such as fertilising. The software could support the process

formulating execution plan in the operational planning level pro-

viding as output the tailored fertilising plans. The specific output

is provided in an XML (extendible markup language) file deter-

mining several actions related to the execution level including the

sequence of points the tractor has to follow, the type of motion

between successive points (e.g., straight motion or manoeuvring),

the type of predefined turning routine used in manoeuvring, and

the actions that should be taken once the tractor reaches the

desired point (e.g., turning on or turning off the power take-off).

The input for the mission planning optimisation problem involve

the field geometry,facilityunit location,field tracks, etc. This infor-

mation has been provided by the field inspection service by the

actor external service, i.e. a GIS: geographic information system-

database, or in the case that such a base does not exists, by the

actor decision maker as a result of the process field inspection,

i.e. recording the field boundary using an on-board GPS positioning

receiver. The information machinery information provides the

machinery related input, i.e. the minimum turning radius, oper-

ating width, recommended operating speed, and tank capacity.

In the same way, the process of the generation of the updated

information in real-time systems refers to the execution level

(Fig. 9), where this information provided by actors included in

the machinery units (e.g., internal sensors, implement ECU, andexternal sensors). Optimisation algorithms that use information

generated at this level are involved in cases such as the real-time

planning of a fertilising unit in a sensor-based variable rate pre-

cision spraying with some a priory information, i.e. by a satelliteimage (vehicle routing problem with stochastic demands) (Bochtis

and Srensen, 2009), and the real-time planning for a refilling unit

in the absence ofany a priory information (dynamic vehicle routingproblem with time windows) (Bochtis and Srensen, 2010).

The described information models specify the key activities for

the targeted agricultural operations and capture the management

cycle of the farm (planning, implementation and evaluation). The

design structure facilitates the building of a dedicated informa-

tion management system as well as provides the foundation for

developing targeted decision support systems. Other important


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Fig. 10. Evaluation part where documented raw execution data is processed further and aggregated for the comparative analyses.

enhancement includes recording and storing of implement sta-

tus/work documentation into farm database, increased usage of

numerical/formative data, the Farm database has a central role in

both human and machine decision-making, active use of external

services increases, increased use of automation, and smart assist-

ingsystem features to support work arecommon,used information

management technology shifts towards knowledge management

technology.

6. Conclusions

A user-centric approach to model the information flows for tar-geted field operations has been presented. The information models

were centred aroundthe farmeras theprincipaldecisionmaker and

involved external entities as well as mobileunit entities as themain

information producers. By specifying in detail, the information pro-

vided and the information required for the information handling

processes,the design and functionalities of the individual informa-

tion system elements can be derived. This has involved explicitly

specifying tacit knowledge of the farmer as a way to extend the

FMIS design into automated decision-making. The (IFDs) describe

the systeminterface features of such a FMIS which gives support to

farmers core task. In this study,a detailed approach to information

modelling that will enable the generation of a Farm Management

Information System in crop production, which is the main focus of

the FutureFarm project, has been introduced.

The user-centric information flow models propose the imple-

mentation of effective managerial functions to the FMIS, but at the

sametime, they expectthe farmers tobe ready to adopt new work-

ing habits and perhaps also undergo further training. According to

the modelling,farmerscan utilise differentservices moreefficiently

and they are able to outsource some of the tasks they had previ-

ously performed themselves. Also, farmers would be able to gain

increased insight into their production processes and would able

to evaluate the performance of the chosen technology. This would

lead to better process control as well as an improved capability of

documentingthe quality of farming e.g. traceability,to markets and

administration.Finally, the proposed system would allow the farmers to access

and utilise better scientific research and technological develop-

ments by providing real process data and the ability to update the

systems according to the latest knowledge.

The enhancements from this approach would enable record-

ing and storing of implement status or work documentation

into farm database. This would also mean increased usage of

numerical or formative data. Farm database would have a cen-

tral role in both human and machine decision-making. Active

use of external services as well as use of automation and smart

assisting system features to support work would increase. This

would be especially important in the future when information

management technology shifts towards knowledge management

technology.


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Acknowledgements

This project was part of the collaborative research project

FutureFarm. The research leading to these results has received

funding from the European Communitys Seventh Framework Pro-

gramme (FP7/2007-2013) under grant agreement no. 212117.

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