PROCESS MINING APPROACH TO ANALYSE USER BEHAVIOUR

Laura M

arus¸ter, Niels R. Faber, Ren

e J. Jorna and Rob J. F. van Haren

Faculty of Economics and Business, University of Groningen, PO Box 800, Groningen, The Netherlands

Keywords:

Modelling user behaviour, decision support systems, process mining, web personalization.

Abstract:

Designing and personalising systems for speciﬁc user groups encompasses a lot of effort with respect to

analysing and understanding user behaviour. The goal of our paper is to provide a new methodology for

determining navigational patterns of behaviour of speciﬁc user groups. We consider agricultural users as a

speciﬁc user group, during the usage of a decision support system supporting cultivar selection - OPTIRas

Combining process mining techniques with insights from decision making theories, we provide a method

of analysing logs resulted from usage of decision support systems. For instance, farmers show difﬁculties

in fulﬁlling the goal of OPTIRas, while other agricultural users seems to manage better. The results of our

analysis can be used to support the redesign and personalization of decision support systems.

1 INTRODUCTION

As the on-line services and Web-based information

systems proliferate in all domains of activities, it

becomes increasingly important to model user be-

haviour and personalization, so that these systems

will appropriately address user characteristics. Par-

ticular topics are addressed by research in human-

computer interaction (HCI) and user-system inter-

action (USI), such as the discovering of user be-

haviour or navigation styles (Herder and Juvina,

2005; Menasalvas et al., 2003; Balajinah and Ragha-

van, 2001), developing metrics involved in modelling

and assessing web navigation (Juvina and Herder,

2005; Herder, 2002; Spiliopoulou and Pohle, 2001),

cognitive models for improving the redesign of infor-

mation systems (Bollini, 2003; Ernst et al., 2005; Lee

and Lee, 2003). Various methods have been devel-

oped to model web navigation in case of generic users

(see for instance (Mobasher, 2006)).

Furthermore, it becomes increasingly important

to address speciﬁc user groups (Song and Shepperd,

2006). By investigating navigational patterns of these

groups, the (re)design of the systems used by speciﬁc

user groups can be made more effective. Although

various methods have been developed to model user

behaviour of generic users, no research speciﬁcally

targeted, as far as we know, the navigational patterns

of agricultural user groups. Our contribution partic-

ularly focuses on agricultural users as a speciﬁc user

group. By analysing agricultural users’ patterns of be-

haviour, we aim to support the redesign of web-based

information systems, illustrated in particular for the

redesign of the decision support system OPTIRas

The goal of this work is to illustrate a new method-

ology of analysing user behaviour using process min-

ing techniques (van der Aalst and Weijters, 2004) and

insights from decision making theories. By consider-

ing agricultural users as a speciﬁc user group, we in-

vestigate agricultural user’s patterns of behaviour dur-

ing the use of a web-based IT system, namely a de-

cision support systems called OPTIRas

, that aids

farmers in their cultivar selection activities (AGRO-

BIOKON, 2006). The results of the analysis provide

recommendations concerning the redesign of the de-

cision support system’s website in order to address

speciﬁc agricultural users’ characteristics. Therefore,

in our analysis we use insights from decision making

theories.

The organisation of our paper is as follow. In Sec-

tion 2 we provide an introduction into decision mak-

ing, decision support system OPTIRas

, and data

collection issues. In Section 3 we determine agricul-

tural user’s patterns of behaviour with process mining

techniques. The conclusions and implications for re-

design are presented in Section 4.

208

aru¸ster L., R. Faber N., J. Jorna R. and J. F. van Haren R. (2008).

A PROCESS MINING APPROACH TO ANALYSE USER BEHAVIOUR.

In Proceedings of the Fourth International Conference on Web Information Systems and Technologies, pages 208-214

DOI: 10.5220/0001526002080214

 SciTePress

2 AGRICULTURAL USERS AND

DECISION MAKING

2.1 Decision Making and Decision

Support Systems

A well-known decision making model was formu-

lated by Simon (Simon, 1977). He explained the

human decision process using three phases, namely

intelligence, design, and choice. In the intelligence

phase, an individual explores the issue about which

he is making a decision, and determines relevant is-

sues. Subsequently, the individual formulates one or

more alternatives solutions to the recognised (sub)-

decisions in the design phase. Eventually, a ﬁnal solu-

tion is formed in the choice phase. In this ﬁnal phase

of the decision process, the partial solutions are eval-

uated against criteria of the outcome that have to be

met. Partial solutions that best meet these criteria are

selected. The selected partial solutions are combined

into the overall decision. Because information that is

used in the three phases is not always complete, the

phases do not linearly follow upon each other. Com-

monly, the decision process is characterised by many

iterations in which additional information about the

issue is collected (intelligence) or more alternatives

are explored (design). Mintzberg refers to a similar

trichotomy: identiﬁcation, development, and selec-

tion (Mintzberg et al., 1976). Speciﬁc decision mak-

ing models have been developed for different kinds of

users. For instance, in case of farmers, decision mak-

ing models have been developed by (Johnson et al.,

1961;

Ohlm

er et al., 1998) and (Fountas et al., 2006).

Whenever these models are linear (sequential) or

iterative, they all show a common structure: ﬁrst, in-

formation is collected about the problem at hand, sec-

ond, different candidate solutions are formed, and ﬁ-

nally, a choice is made. It is evident that a decision

support system has to address in one way or another

the basic phases of the decision making process. In

the following, we investigate in which way the deci-

sion support system OPTIRas

has addressed these

phases.

2.2 OPTIRas

A decision support system (DSS) is perceived as a

computer system that aids people in making a deci-

sion regarding a speciﬁc domain (Klein and Methlie,

1995). This aid is provided by the DSS by connecting

to the human decision process (Turban and Aronson,

2001).

OPTIRas

system was designed to target low-

yielding potato farmers, attempting to realise an in-

crease in the yield of farmers. OPTIRas

is a de-

cision support system for cultivar selection that sup-

ports a farmer in selecting cultivars relative to culti-

var characteristics. A variety of properties, relating to

yield, resistance against pests and diseases, and stor-

age, characterise a potato cultivar. Potato Cyst Nema-

todes (PCN), a severe potato disease, causes each year

an average loss of 100-150 euros per hectare per year

in the North East region of The Netherlands. This is

about 10-15% of the net revenues of cropping (before

taxation and investment calculations). Reducing the

PCN infestation level to economic acceptable levels

requires the right combination of sampling data, cul-

tivar, growth frequency, ﬁeld choice, and nematicide

usage. The goal of the decision support system is to

ensure that farmer gains insight into PCN damage lev-

els and the ﬁnancial consequences of selected cultivar

and the application of pesticides.

The interface of OPTIRas

DSS consists of

seven main pages: Field ID, PCN History, Reorder

Cultivars, Yield Information, Crop Rotation, Report

and Option. Moreover, there are also pages offering

Help, Details about a speciﬁc cultivar, but we do not

use them in our analysis.

Web pages do not have the same function, and this

depends on the purpose of the web site. In our case,

the site’s goal is to provide the farmer with informa-

tion about different yield scenarios, given the cho-

sen factors. Therefore, the following types of pages

can be distinguished: (i) the pages reﬂecting the site’s

goal, e.g. containing information about the yield, (ii)

the pages that can lead to fulﬁl the site’s goal, and (iii)

the other pages.

In literature, this categorization is called con-

cept hierarchies, or service-based hierarchies

(Spiliopoulou and Pohle, 2001). Concept hierarchies

are used in market-basket analysis or to study the

segmentation of companies clients. Spiliopoulou

and Pohle developed a service-based concept hier-

archy to determine the success of a web site, which

distinguishes between action pages and target pages

(Spiliopoulou and Pohle, 2001). According to their

deﬁnition, “an action page is a page whose invocation

indicates that the user is pursuing the site’s goal. A

target page is a page whose invocation indicates that

the user has achieved the site’s goal”.

As we stated in the previous section, a DSS sys-

tem should support the user in all phases of the de-

cision making process. For instance, in terms of Si-

mon’s decision making phases, ’Action’ pages would

correspond to the Intelligence phase, where an indi-

vidual explores the issues about which he is making

a decision, while ’Target’ pages would correspond to

the Design phase, where candidate solutions are de-

A PROCESS MINING APPROACH TO ANALYSE USER BEHAVIOUR

209

Table 1: Categorization of OPTIRas

pages.

’Action’ pages ’Target’ pages Other pages

Field ID Yield Details

PCN information Options

history Crop Help

Reorder rotations Logon

cultivars Report Help

Save

veloped.

We categorize OPTIRas

pages as presented in

Table 1. In the following, we consider that whenever

a user has visited one of the target pages (Yield infor-

mation, Crop rotations, Report), the user has reached

the goal of the web site.

Based on Simon’s decision making phases, we

proceed with the following mapping:

1. ’Action’ pages corresponds to the Intelligence

phase: the user is supported to explore the issue

(the selection of the cultivar), by specifying Field

characteristics and the PCN history;

2. ’Target’ pages corresponds to the Design phase:

the system provides the user with one or more

possible solutions;

3. the Choice phase does not have any correspon-

dence in the DSS; the ﬁnal decision is actually

taken by the user outside of the system.

Assessing whether the DSS is “really” supporting

the targeted users, or in other words, whether the site

goal is fulﬁlled, we expect that both decision mak-

ing phases, Intelligence and Design, should be per-

formed, to the same extend. This translates into visi-

tations of both ’Action’ and ’Target’ pages, to a com-

parable extend. In Section 3 we will investigate how

the navigational patterns look like, e.g. how ’Action’

and ’Target’ types of pages are visited.

2.3 Data Collection

According to Herder and Juvina (Herder and Juvina,

2005), user’s navigation behaviour can be modelled

using syntactic information (“e.g. which links are fol-

lowed, what does the navigation graph look like, what

is the time that users spent on each page”), semantic

information (“i.e. what is the meaning of the infor-

mation that the user encountered during navigation”),

and pragmatic information (“i.e. what is the user us-

ing the information for, what are the user’s goals and

tasks”). We choose to record per session syntactic in-

formation, e.g. information concerning the movement

from one ’source’ page to a ’destination’ page (time

Table 2: An example of the log ﬁle.

Time stamp FromPage ToPage

2004-12-22 22:13:29 ﬁeld pcn

2004-12-22 22:13:35 pcn order

2004-12-22 22:14:00 order yield

2004-12-22 22:14:26 yield crop

2004-12-22 22:16:16 crop yield

2004-12-22 22:16:25 yield crop

2004-12-22 22:16:53 crop details

2004-12-22 22:17:54 details allcultivars

stamp, name of source page, and name of destination

page, see Table 2).

Table 2 shows an example of an OPTIRas

se-

quence of navigation, where the user sequentially ac-

cesses 7 types of pages. The log ﬁle (Table 2) pro-

vides us syntactic information, namely information

needed to build navigation graphs (see Section 3).

Analysing users as a homogeneous group is not

a suitable basis for re-designing decision support sys-

tems that target heterogeneous end users. OPTIRas

system was designed for farmers. However, a

larger group of agricultural users is actually using

OPTIRas

, that can be differentiated into three tar-

get groups, namely farmers (called also growers

extension workers, and scientists. Extension work-

ers are people with a degree in agricultural sciences,

who help growers in their practical work. Scientists

also use OPTIRas

, but rather for experimental pur-

poses.

Agricultural users can login in OPTIRas

reg-

istering with their e-mails, but they can register also

with an anonymous ID. The system provides this op-

portunity for the sake of user-friendliness, but also to

protect users’ privacy. The anonymous ID takes into

account the IP number. In this way advantages for

the users are provided, but it implies disadvantages

for the analysis. A user may login the ﬁrst time with

his/her e-mail, the second time anonymously using

computer A, and the third time again anonymously

using computer B; in the analysis, these three ses-

sions will count as sessions belonging to three dis-

tinct users, and we cannot see anymore how a user

eventually changes his behaviour in time. Moreover,

users can login by specifying their role, i.e. grower,

scientist, extension worker, or other. OPTIRas

expected to be used in particular within two peak pe-

riods (i) November/December and February (period

for purchasing crops, expected to be the peak period)

and (ii) April and August/September (period when

In this article, the terms ‘farmer’ and ‘grower’ are used

interchangeably.

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210

the yield is actually obtained). Farmers may com-

pare the obtained yield with the advice given earlier

by OPTIRas

We have inspected only the log ﬁles starting

from the 18th of December 2004 (the date when

OPTIRas

became on-line) and stopped at with the

30th of May 2006. Thus we have two peak periods:

(i) December 2004 - February 2005 and (ii) Novem-

ber 2005 - February 2006. In total, 763 user ses-

sions belonging to 501 individual users (see discus-

sion above about distinct users) have been logged and

included in the analyses.

3 MINING NAVIGATIONAL

BEHAVIOUR WITH PROCESS

MINING TECHNIQUES

Developing navigation patterns and performing web

personalization is often done by using data mining

techniques such as Clustering, Classiﬁcation, Asso-

ciation Rule Discovery, Sequential pattern Discov-

ery, Markov models, hidden (latent) variable models,

and hybrid models (Mobasher, 2006; Herder, 2002;

Juvina and Herder, 2005; Chang et al., 2006). Mostly

of these techniques assume web navigation as a se-

quential process. However, the decision making pro-

cess exhibits a distributed nature (Holsapple, 1988),

which should be captured by the web-mining tech-

nique.

Process mining techniques allow for extracting in-

formation from event logs and can be used to dis-

cover models describing processes, organisations, and

products (van der Aalst et al., 2003). Because these

techniques are able to model parallel events, moni-

tor deviations (e.g., comparing the observed events

with predeﬁned models or business rules), and rely

on robust mathematical formalism, they are a good

candidate for providing insights into navigational be-

haviour for decision making processes. However,

the choice of the most appropriate process mining

algorithm for a certain problem is not straightfor-

ward. A suitable algorithm is the heuristic mining,

implemented in the ProM framework. ProM offers

a wide variety of process mining techniques (avail-

able at www.processmining.org) (ProM, 2007). We

choose heuristic mining because for its robustness for

noise and exceptions (Weijters and Aalst, 2003). The

heuristic mining is based on the frequency of patterns,

therefore it is possible to focus on the main behaviour

in the event log.

We provide insights into two kinds of navigational

patterns: (i) navigation patterns of all agricultural

users show, and (ii) navigational patterns of differen-

tiated target group (e.g. growers, extension workers,

scientists).

3.1 Mining the Navigational Pattern of

All Users

This section concentrates on the navigational patterns

exhibited by all users. In Figure 1 the result of ap-

plying the ProM heuristic mining plug-in is shown.

The focus is to grasp the patterns of navigation from

one page to another (we use the FromPage ﬁeld of the

log ﬁle, see Table 2). The ﬁle consists on 763 user

sessions, belonging to 501 individual users. The rect-

angles refer to transitions or activities, in our case to

page names, such as Field, Order, etc. There are two

special activity names, ArtiﬁcialStartTask and Artiﬁ-

cialEndTask that refer to a generic start or end page.

The term ’complete’ refers to a ﬁnished action (in

terms of a ﬁnite-state machine, an activity can be in

the states ’new’, ’sent’, ’active’, ’suspended’, ’com-

plete’, etc.). The number inside the rectangle shows

how many times a page has been invoked. The arcs

between two activities A and B are associated with

two numbers

: (i) a number between 0 and 1, which

represents the causality/dependency measure between

A and B, and (ii) an integer number that represents

the frequency of occurrences of A and B next to each

other. For instance in Figure 1, Field page is directly

followed 553 times by the Pcn page, with a depen-

dency measure of 0.742, and 374 times by Artiﬁcial-

EndTask, with a dependency measure of 0.996.

The complicated picture from Figure 1 can be in-

terpreted as follows. We can determine “the most

common pattern” which consists of ArtiﬁcialStart-

Task → Field (758), Field → Pcn (553), Pcn → Yield

(530), and Yield → ArtiﬁcialEndTask (221). The se-

quence Field → Pcn → Yield is actually the “pre-

scribed” order of pages in OPTIRas

, e.g. the order

in which pages are presented in OPTIRas

We notice the reversed link Yield → Pcn, which

may suggest that users are changing PCN values to

observe the impact on calculating the yield (the higher

the values of PCN, the smaller the yield). Notice-

able is also the high frequent self-recurrent link to

Yield(655).

The interpretation of these ﬁndings is that farm-

ers in general use the prescribed sequence of page in-

vocation. The directed graphs from Figure 1 reveal

that invocation of ’Action’ pages are predominating.

In Figure 1, the sum of incoming arcs from ’Action’

pages (Field, Pcn, Order) to activity ArtiﬁcialEnd-

For more details, see (Weijters et al., 2006).

A PROCESS MINING APPROACH TO ANALYSE USER BEHAVIOUR

211

allcultivars

(complete)

details

(complete)

279

options

(complete)

crop

(complete)

385

yield

(complete)

1207

pcn

(complete)

889

ArtificialEndTask

(complete)

763

order

(complete)

1097

field

(complete)

1494

ArtificialStartTask

(complete)

763

0.981

118

0.8

139

0.998

555

0.998

655

0.996

374

0.75

0.742

553

0.979

0.991

255

0.875

0.923

0.5

0.993

221

0.957

154

0.999

758

0.958

0.983

102

0.938

302

0.938

530

0.192

0.995

709

0.992

342

0.958

0.909

0.989

162

Figure 1: The mined behaviour corresponding to all agri-

cultural users.

Task is 374+162+342=878. This value is much larger

than the sum of incoming arcs from ’Target’ pages

(Yield, Crop) to the activity ArtiﬁcialEndTask, which

is 221+99=320.

The ﬁrst conclusion is that pages from the cat-

egory ’Target’ are visualized signiﬁcantly less than

’Action’ pages. This is a disappointing result, given

the fact that the main goal of OPTIRas

is to pro-

vide detailed information about yield. Second, after

each page a signiﬁcant number of sessions stop (in

Figure 1, 374 after page Field, 162 after page Pcn).

However, we also see that whenever users do not ’give

up’ in early stages, and visualize ’Target’ pages, they

show a relevant activity. They recall repeatedly the

Yield page, and they revisit pages which are not di-

rectly linked in the prescribed order (the reverse link

Yield → Pcn). This fact illustrates that whenever

users reach a ’Target’ page, their interest may rise.

The Details page is visited unexpectedly often: 279

times, and even forms a path containing only one page

(e.g. the path ArtiﬁcialStartTask, Details and Artiﬁ-

cialEndTask (see Figure 1). This suggests that users

are interested in information about different cultivars.

3.2 Mining the Navigational Patterns of

differentiated Target Groups

In this section we investigate the patterns of behaviour

of different target groups. These target groups are

growers, extension workers, and scientists. Whenever

a user logs in into OPTIRas

, he/she has to specify

his/her role, e.g. growers, extension worker, scientist

or others. The distribution of sessions is as follow-

ing: from all 763 sessions, 535 are sessions belonging

to growers, 59 are sessions coming from extension

workers and 72 are results of scientists usage. The

rest of 97 sessions which are not labelled, are left out

from the analysis.

The run of the heuristic algorithm (see Figure 2a.)

depicting the navigation pattern of farmers provide

more insights into the most generic behaviour: there

seems to be differences between all agricultural users

(Figure 1) and farmers (Figure 2a.) For instance, in

case of farmers, the most used path is constituted by

Field and Pcn pages, without the Yield page. This fact

could be interpreted as the majority of farmers do not

reach the Yield page, and thus we may doubt whether

the DSS fulﬁls its goal. Also, the navigational pattern

of farmers does not contain loops (see in Figure 2a.

the loop between Pcn and Yield), except from self-

loops.

Inspecting further the navigation patterns of the

extension workers, we see in Figure 2b. that the

most common behaviour is to stop after visiting the

Field page. The other path starting with the Pcn page

is less easy to be interpreted (without any incoming

arc); however, we can note the loop between Pcn, Or-

der and Yield, which illustrates a rather complex be-

haviour.

The behaviour of scientists is the most complex

(see Figure 2c.): there are two loops, e.g. (i) Field

and Pcn and (ii) Order and Yield. This illustrates the

fact that scientists, instead of only following the pre-

scribed behaviour, are checking the effects of chang-

ing value parameters. Also, most of users of this

group visit ’Target’ pages (Yield and Crop), which

may suggest that they like to explore DSS possibili-

ties.

4 CONCLUSIONS

In this article we provided a new methodology for

determining navigational behaviour of agricultural

users, by combining process mining techniques and

insights from decision making theories. To our

knowledge, this combination, together with apply-

ing the approach of process mining in case of deci-

sion support systems supporting farming activities are

new. This methodology can be used to redesign the

decision support system, by addressing the character-

istics of agricultural users.

We analysed logs resulting from a decision sup-

port system called OPTIRas

. With respect to all

agricultural users, they use the prescribed order of

pages, e.g., Field, Pcn, Order, Yield. We discovered

that often users visualise just the ﬁrst page, and then

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212

allcultivars

(complete)

details

(complete)

235

options

(complete)

crop

(complete)

321

yield

(complete)

980

pcn

(complete)

703

ArtificialEndTask

(complete)

535

order

(complete)

932

field

(complete)

1099

ArtificialStartTask

(complete)

535

0.512

373

0.998

529

0.933

276

0.178

0.667

0.992

221

0.984

297

0.875

0.741

425

0.917

0.995

397

0.947

0.974

0.99

112

0.98

0.75

112

0.997

596

0.997

430

0.727

0.993

217

0.975

0.99

213

0.957

crop

(complete)

details

(complete)

options

(complete)

yield

(complete)

pcn

(complete)

ArtificialEndTask

(complete)

order

(complete)

field

(complete)

105

ArtificialStartTask

(complete)

0.346

0.8

0.983

0.5

0.667

0.933

0.929

0.97

0.857

0.923

0.672

0.955

0.875

0.5

0.857

crop

(complete)

details

(complete)

ArtificialEndTask

(complete)

options

(complete)

yield

(complete)

110

order

(complete)

pcn

(complete)

107

field

(complete)

129

ArtificialStartTask

(complete)

0.833

0.933

0.986

0.958

0.646

0.95

0.929

0.857

0.5

0.967

0.341

0.933

0.947

0.958

0.933

0.8

0.667

a. growers b. extension workers c. scientists

Figure 2: The mined behaviour of farmers (a.), extension workers (b.) and scientists (c.).

leave the system. With respect to goal fulﬁlling, the

invocation of ’Action’ pages predominates, while the

invocation of ’Target’ pages have a smaller proportion

than expected. This corroborates with our assump-

tion that users of OPTIRas

spend time in the Intel-

ligence phase of the decision making process, explor-

ing the alternatives in the domain of cultivar selection.

This shows that users do not realise fast enough what

the goal of OPTIRas

is, and what kind of advan-

tages they may have when they use this system. This

leads to a lack of interest, which implies that users

stop using the system. The fact that often the Details

page is visited in the beginning of farmer’s sessions

suggests that in some cases, users are more interested

in information about different cultivars, rather than

cultivar selection based on Yield or Crop ﬁgures.

By analysing logs of different target groups, we

found that the most common behaviour of farm-

ers (growers) shows difﬁculties in fulﬁlling the goal

of OPTIRas

, e.g. visiting ’Target’ pages, while

the other two target groups, extension workers and

scientists seems to manage better. Given the fact

that OPTIRas

was especially developed to support

farmers (growers), it is a clear sign that redesign ac-

tions are needed.

This methodology can be used to support the re-

design of DSSs in order to address speciﬁc agricul-

tural user’s characteristics. First, OPTIRas

should

better support the decision making process, i.e. by

letting users gather information about the various top-

ics involved in cultivar selection, instead of present-

ing itself as an instrument to make optimal choices.

Second, we suggest that in the very ﬁrst page of

OPTIRas

, a user should be confronted with the

goal of the system. This should be very explicitly and

clearly stated, perhaps illustrated with a short exam-

ple. Third, depending on the target group, hints for

the following steps should be given: e.g. in case of

growers, what path should be followed to get the yield

overview. For this, the use of a sitemap is regarded to

be appropriate.

In future research we plan to investigate in more

detail the navigation behaviour of different farmer

groups, e.g. top-farmers, quality, quantity and nor-

mal farmers (for farmer categories, see (Faber et al.,

2006)), especially to relate their navigational patterns

with their preferred learning styles. Information about

the behavioural patterns of various agricultural user

groups may enhance the successful design and use of

DSSs. Second, we plan to let these users participate

in the redesigning process and to make OPTIRas

more interactive. Third, we intend to incorporate in

our analysis also semantic and pragmatic information.

As a ﬁnal remark, we would like to state that, al-

though the present research focused on decision sup-

port systems and agricultural users, the methodology

presented in this paper can be used in case of any in-

formation system enhanced with logging functional-

A PROCESS MINING APPROACH TO ANALYSE USER BEHAVIOUR

213

ity, and with groups of users that, based on some cri-

terion, can be differentiated into subgroups.

ACKNOWLEDGEMENTS

We thank Iain Milne from Scottish Crop Re-

search Institute for enabling the logging function of

OPTIRas

. AGROBIOKON is supported by the

Northern Netherlands Provinces (SNN), the Product

Board for Arable Products (HPA), and AVEBE.

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