HANDBOOK OF OPERATIONS RESEARCH IN NATURAL RESOURCES pdf

HANDBOOK OF OPERATIONS RESEARCH
IN NATURAL RESOURCES
Recent titles in the INTERNATIONAL SERIES IN
OPERATIONS RESEARCH & MANAGEMENT SCIENCE
Frederick S. Hillier, Series Editor, Stanford University

Gass & Assad/
AN ANNOTATED TIMELINE OF OPERATIONS RESEARCH: An Informal History
Greenberg/ TUTORIALS ON EMERGING METHODOLOGIES AND APPLICATIONS IN
OPERATIONS RESEARCH
Weber/ UNCERTAINTY IN THE ELECTRIC POWER INDUSTRY: Methods and Models for Decision
Support
Figueira, Greco & Ehrgott/ MULTIPLE CRITERIA DECISION ANALYSIS: State of the Art Surveys
Reveliotis/ REAL-TIME MANAGEMENT OF RESOURCE ALLOCATIONS SYSTEMS: A Discrete Event
Systems Approach
Kall & Mayer/ STOCHASTIC LINEAR PROGRAMMING: Models, Theory, and Computation
Sethi, Yan & Zhang/ INVENTORY AND SUPPLY CHAIN MANAGEMENT WITH FORECAST
UPDATES
Cox/ QUANTITATIVE HEALTH RISK ANALYSIS METHODS: Modeling the Human Health Impacts of
Antibiotics Used in Food Animals
Ching & Ng/ MARKOV CHAINS: Models, Algorithms and Applications
Li & Sun/ NONLINEAR INTEGER PROGRAMMING
Kaliszewski/ SOFT COMPUTING FOR COMPLEX MULTIPLE CRITERIA DECISION MAKING
Bouyssou et al/ EVALUATION AND DECISION MODELS WITH MULTIPLE CRITERIA: Stepping
stones for the analyst
Blecker & Friedrich/ MASS CUSTOMIZATION: Challenges and Solutions
Appa, Pitsoulis & Williams/ HANDBOOK ON MODELLING FOR DISCRETE OPTIMIZATION
Herrmann/ HANDBOOK OF PRODUCTION SCHEDULING
Axsäter/ INVENTORY CONTROL, 2
nd

Ed.
Hall/ PATIENT FLOW: Reducing Delay in Healthcare Delivery
Józefowska & Węglarz/ PERSPECTIVES IN MODERN PROJECT SCHEDULING
Tian & Zhang/ VACATION QUEUEING MODELS: Theory and Applications
Yan, Yin & Zhang
/ STOCHASTIC PROCESSES, OPTIMIZATION, AND CONTROL THEORY
APPLICATIONS IN FINANCIAL ENGINEERING, QUEUEING NETWORKS, AND
MANUFACTURING SYSTEMS
Saaty & Vargas/ DECISION MAKING WITH THE ANALYTIC NETWORK PROCESS: Economic,
Political, Social & Technological Applications w. Benefits, Opportunities, Costs & Risks
Yu/ TECHNOLOGY PORTFOLIO PLANNING AND MANAGEMENT: Practical Concepts and Tools
Kandiller/ PRINCIPLES OF MATHEMATICS IN OPERATIONS RESEARCH
Lee & Lee/ BUILDING SUPPLY CHAIN EXCELLENCE IN EMERGING ECONOMIES

* A list of the early publications in the series is at the end of the book *
HANDBOOK OF OPERATIONS RESEARCH
IN NATURAL RESOURCES
Edited by

Andres Weintraub

Carlos Romero
Technical University of Madrid, Spain

Trond Bjørndal
CEMARE, University of Portsmouth, UK

Rafael Epstein
University of Chile, Santiago, Chile

Jaime Miranda
Diego Portales University, Santiago, Chile
University of Chile, Santiago, Chile
with the collaboration of
Andres Weintraub Carlos Romero
University of Chile Technical University of Madrid
Santiago, Chile Madrid, Spain
Trond Bjørndal Rafael Epstein
University of Chile
Portsmouth, United Kingdom Santiago, Chile
Fred Hillier
Stanford University
Stanford, CA, USA
Library of Congress Control Number: 2007924350


Printed on acid-free paper.

© 2007 by Springer Science+Business Media, LLC
All rights reserved. This work may not be translated or copied in whole or in part
without the written permission of the publisher (Springer Science+Business Media,
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The use in this publication of trade names, trademarks, service marks and similar
terms, even if the are not identified as such, is not to be taken as an expression of
opinion as to whether or not they are subject to proprietary rights.

9 8 7 6 5 4 3 2 1

springer.com
With the collaboration of:
Series Editor:
Jaime Miranda
Diego Portales University,
Santiago, Chile
e-ISBN 978-0-387-71815-6 ISBN 978-0-387-71814-9
CEMARE, University of Portsmouth
Contents
Contributing Authors ix
Preface xiii
Acknowledgments xv
AGRICULTURE 1
Importance of Whole-Farm Risk Management in Agriculture 3
RUUD HUIRNE, MIRANDA MEUWISSEN,
AND MARCEL VAN ASSELDONK
Dealing with Multiple Objectives in Agriculture 17
KIYOTADA HAYASHI
Modeling Multifunctional Agroforestry Systems with Environmental
Values: Dehesa in Spain and Woodland Ranches in California 33

PABLO CAMPOS, ALEJANDRO CAPARRÓS, EMILIO CERDÁ,
L
YNN HUNTSINGER, AND RICHARD B. STANDIFORD

Environmental Criteria in Pig Diet Formulation with Multi-Objective
Fractional Programming 53

TERESA PEÑA, CARMEN CASTRODEZA , AND PABLO LARA
vi Contents
Modeling the Interactions Between Agriculture and the Environment 69

SLIM ZEKRI AND HOUCINE BOUGHANMI
MCDM Farm System Analysis for Public Management of Irrigated
Agriculture 93

JOSÉ A. GÓMEZ-LIMÓN, JULIO BERBEL, AND MANUEL ARRIAZA
Water Public Agencies Agreeing to A Covenant for Water Transfers:
How to Arbitrate Price–Quantity Clauses 115

ENRIQUE BALLESTERO
Positive Mathematical Programming for Agricultural and
Environmental Policy Analysis: Review and Practice 129

BRUNO HENRY DE FRAHAN, JEROEN BUYSSE, PHILIPPE POLOMÉ,
BRUNO FERNAGUT, OLIVIER HARMIGNIE, LUDWIG LAUWERS,
G
UIDO VAN HUYLENBROECK, AND JEF VAN MEENSEL
FISHERIES 155
Fisheries Management 157
RAGNAR ARNASON
Shared Fish Stocks and High Seas Issues 181
TROND BJØRNDAL AND GORDON MUNRO
Game Theoretic Applications to Fisheries 201
VEIJO KAITALA AND MARKO LINDROOS

Uncertainty in Bioeconomic Modelling 217
LINDA NØSTBAKKEN AND JON M. CONRAD
Planning in Fisheries-Related Systems 237
DANIEL E. LANE
Capacity and Technical Efficiency Estimation in Fisheries:
Parametric and Non-Parametric Techniques 273

SEAN PASCOE AND DIANA TINGLEY
Studies in the Demand Structure for Fish and Seafood Products 295
FRANK ASCHE, TROND BJØRNDAL, AND DANIEL V. GORDON
Contents vii

FORESTRY 315

Models for Strategic Forest Management 317
ELDON A. GUNN
Tactical-Level Forest Management Models 343
RICHARD L. CHURCH
Harvest Operational Models in Forestry 365
RAFAEL EPSTEIN, JENNY KARLSSON, MIKAEL RÖNNQVIST,
AND ANDRES WEINTRAUB
Log Merchandizing Model Used in Mechanical Harvesting 378
HAMISH MARSHALL
Forest Transportation 391
RAFAEL EPSTEIN, MIKAEL RÖNNQVIST, AND ANDRES WEINTRAUB
Optimization of Forest Wildlife Objectives 405
JOHN HOF AND ROBERT HAIGHT
Spatial Environmental Concerns 419
ALAN T. MURRAY
Heuristics in Forest Planning 431

JOHN SESSIONS, PETE BETTINGER, AND GLEN MURPHY
Forestry Economics: Historical Background and Current Issues 449
RONALD RAUNIKAR AND JOSEPH BUONGIORNO
Multiple Criteria Decision-Making in Forest Planning:
Recent Results and Current Challenges 473

LUIS DIAZ-BALTEIRO AND CARLOS ROMERO
Forest Fire Management 489
DAVID L. MARTELL
A Model for the Space–Time Spread of Pine Shoot Moth 511
ROBERTO COMINETTI AND JAIME SAN MARTÍN
Adaptive Optimization of Forest Management in A Stochastic World 525
PETER LOHMANDER
viii Contents
MINING 545

Application of Optimisation Techniques in Open Pit Mining 547
LOUIS CACCETTA
Optimisation in Underground Mining 561
CHRISTOPHER ALFORD, MARCUS BRAZIL, AND DAVID H. LEE
Long- and Short-Term Production Scheduling at Lkab’s Kiruna Mine 579
ALEXANDRA M. NEWMAN, MARK KUCHTA,
AND MICHAEL MARTINEZ
An Integrated Approach to the Long-Term Planning Process
in the Copper Mining Industry 595

RODRIGO CARO, RAFAEL EPSTEIN, PABLO SANTIBAÑEZ,

AND ANDRES WEINTRAUB
Index

611


Contributing Authors
Christopher Alford
University of Queensland

Pete Bettinger
University of Georgia


Ragnar Arnason
University of Iceland


Trond Bjørndal
University of Portsmouth


Manuel Arriaza
Andalusian Institute of Agricultural,
Fisheries and Food Research


Houcine Boughanmi
Sultan Qaboos University


Frank Asche

University of Stavanger


Marcus Brazil
University of Melbourne


Enrique Ballestero
Technical University of Valencia


Joseph Buongiorno
University of Wisconsin


Julio Berbel
University of Córdoba


Jeroen Buysse
Ghent University





x Contributing Authors

Louis Caccetta
Curtin University of Technology

Bruno Fernagut
Centre for Agricultural Economics


Alejandro Caparrós
Spanish Council for Scientific
Research (CSIC), Spain


José A. Gómez-Limón
University of Valladolid


Rodrigo Caro
University of Chile


Daniel V. Gordon

Carmen Castrodeza
University of Valladolid


Eldon A Gunn
Dalhousie University


Emilio Cerdá
University Complutense of Madrid

Robert Haight


Richard L. Church
University of California at Santa
Barbara


Olivier Harmignie
Université Catholique de Louvain


Roberto Cominetti
University of Chile


Kiyotada Hayashi
National Agricultural Research
Center


Jon M. Conrad
Cornell University


Bruno Henry de Frahan
Université Catholique de Louvain

Luis Diaz-Balteiro
Technical University of Madrid


John Hof
Rocky Mountain Research Station


Rafael Epstein
University of Chile


Ruud Huirne
Wageningen University

University of Calgary
North Central Research Station

Contributing Authors xi


Lynn Huntsinger
University of California
(Berkeley)

Hamish Marshall
New Zealand Forest Research
Institute Ltd.

Veijo Kaitala
University of Helsinki


David L. Martell
University of Toronto


Jenny Karlsson
Linköping University


Michael Martinez
Colorado School of Mines

Mark Kuchta
School of Mines


Miranda Meuwissen
Wageningen University


Daniel E. Lane
University of Ottawa


Gordon Munro
University of Portsmouth

Ludwig Lauwers
Centre for Agricultural Economics


Glen Murphy
Oregon State University


David H. Lee
University of Melbourne


Alan T. Murray
Ohio State University


Marko Lindroos
University of Helsinki


Alexandra M. Newman
Colorado School of Mines


Peter Lohmander
Department of Forest Economics

Linda Nøstbakken
Cornell University









xii Contributing Authors

Sean Pascoe
University of Portsmouth

John Sessions
Oregon State University


Teresa Peña
University of Valladolid


Richard B. Standiford
University of California (Berkeley)


Philippe Polomé
Université Catholique de Louvain

Diana Tingley
University of Portsmouth


Ronald Raunikar
University of Wisconsin


Marcel Van Asseldonk
Wageningen University


Carlos Romero
Technical University of Madrid,


Guido Van Huylenbroeck
Ghent University


Mikael Rönnqvist
The Forestry Research Institute
of Sweden


Jef Van Meensel
Centre for Agricultural Economics

Jaime San Martín
University of Chile


University of Chile


Pablo Santibañez
University of Chile, Santiago


Slim Zekri
Sultan Qaboos University



Andres Weintraub



Preface
Operations Research/Management Science (OR/MS) approaches have
helped people for the last 40 years or so, to understand the complex func-
tioning of the systems based upon natural resources, as well as to manage
this type of systems in an efficient way. The areas usually viewed within the
natural resources field are: agriculture, fisheries, forestry, mining and water
resources.

Even though, the above areas are usually viewed as separate fields of

study, there are clear links and relations between them. In fact, all of them
share the common problem of allocating scarcity along time in an optimal
manner. The scale of time or length of the planning horizon is very different.
Thus, we have almost a continuous renewal in the case of the fisheries,
periodic cycles in the case of agriculture and forestry (ranging from some
few months in the case of a horticultural crop to more than a century for
some forest species), and enormous periods of time much beyond the human
perception in the case of mining resources. But in all the cases, the key
matter is to obtain an efficient use of the resource along its planning horizon.

Another element of connection among the different natural resources is
due to the interaction between the use of the resource, and the environmental
impact caused by its extraction or harvest. This type of interaction implies
additional complexities in the underlying decision-making process, making
the use of OR/MS tools especially relevant.


xiv Preface


The above views are corroborated by the massive use of quantitative
approaches in the management of natural resources. It can be said that this
broad field was one of the first where the OR/MS discipline was successfully
applied.

The papers presented correspond to invitations made to the specialists
we considered the most distinguished in each area, and we are extremely
satisfied with the positive response we obtained from them. In defining the
subject matters, we tried to cover comprehensively the most relevant topics
in each area, from the application point of view, as well as consideration

of the operations research techniques involved. In particular, we wished to
highlight the successes of the OR approach to deal with problems, which
involves a conceptual view of problems, modelling of complex realities, and
development of algorithms for problems increasingly difficult to solve.
Issues of large scale, uncertainty, multiple objectives appear increasingly in
these decision processes. Also, we view the integration in multidisciplinary
approaches, where specialists in the specific areas need to interact with
operations research specialist, and the need to incorporate information tech-
nologies for implementations is also present.

The set of papers compiled in this volume attempts to provide readers
with significant OR/MS contributions in each one of the applied areas
previously defined. In this way, we hope to encourage the use of quantitative
techniques in order to manage the use of the different natural resources
efficiently from an economic as well as an environmental point of view.

The papers are divided by area of application: agriculture, fisheries,
forestry and mining.


Acknowledgments
The preparation of the volume was a very long process that exceeds
considerably the initial target. Hence, we thank all the authors for their co-
operation and patience. All the papers were assessed following a blind
reviewing process. Our gratitude to all the anonymous referees.

The following funding is acknowledged. The work of Carlos Romero
was supported by the Spanish “Ministerio de Educación y Ciencia” under
research grant SEJ2005-04392. The work of Rafael Epstein and Andres
Weintraub was supported by Nucleo Milenio “Complex Engineering Systems”.

PART I
A
A
G
G
R
R
I
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C
C
U
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L
T
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E
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In the area of agriculture we have eight chapters with different concerns such
as conceptual problems related with risk analysis, the interaction between
agriculture and the environment, water resources planning, agroforestry sys-
tems management, simulation of effects on agriculture of changes in the

common agriculture policy, and so on. OR/MS techniques used are basically
the following: linear programming, multi-objective fractional programming,
goal programming, multi-attribute utility theory and control dynamic optimi-
zation.
The chapter “Importance of whole-farm risk management in agriculture”,
by Huirne, Meuwissen and Van Asseldonk, deals with the problems associ-
ated with the definition and measurement of risk at the whole-farm level.
The conceptual framework is tested through a questionnaire survey among
livestock and arable farmers in the Netherlands.
The chapter “Dealing with multiple objectives in agriculture”, by Hayashi,
presents state-of-the-art of multiple criteria decision-making approaches app-
lied to the selection problems in agricultural systems. The chapter pays special
attention to matters related with attributes definition and problem struct-
uring, in order to build suitable models for agri-environmental decision-
making.
The chapter “Modelling multifunctional agroforestry systems with environ-
mental values: Dehesa in Spain and woodland ranches in California”, by
Campos, Caparrós, Cerdá, Huntsinger and Standiford, deals with modelling
agroforestry systems (“dehesas”) with the help of optimal control techni-
ques. Two studies, one in California and the other one in Spain, are accom-
plished under a comparison basis.
The chapter “Environmental criteria in pig diet formulation with multi-
objective fractional programming”, by Peña, Castrodeza and Lara, incorpor-
ates environmental criteria in pig diet formulation. The proposed model is
satisfactorily solved by resorting to an interactive multigoal programming
model that allows the incorporation of goals of fractional nature.
The chapter “Modelling the interactions between agriculture and the
environment”, by Zekri and Boughanmi, reviews the integration of different
OR/MS approaches for modelling the interaction between agriculture and the
environment. In this way, the authors propose a decision support system

based upon multi-criteria techniques and geographical information systems
within a participatory decision-making perspective.
2 Part I: Agriculture

The chapter “MCDM farm system analysis for public management of
irrigated agriculture”, by Gómez-Limón, Berbel and Arriaza, proposes a
multi-criteria approach to assist policy decision-making on water manage-
ment for irrigated agriculture. The methodology is a hybrid between multi-
attribute utility theory and goal programming. The methodology is applied to
several Spanish case studies within the recent European Water Framework
Directive.
The chapter “Water public agencies agreeing to a covenant for water
transfers: How to arbitrate price-quantity clauses”, by Ballestero, deals with
inter-basin water covenants guided by the principle of arbitration and imple-
mented through public agencies. The methodology is illustrated with the
help of a realistic example in a maritime region near the Mediterranean Sea.
Finally, the chapter “Positive mathematical programming for agriculture
and environmental policy analysis: Review and practice”, by de Frahan,
Buysse, Polomé, Fernagut, Harmignie, Lauwers, van Huylenbroeck and van
Meensel, introduces a farm-level sector model, called SEPALES, based upon
the approach known as a positive mathematical programming. After this,
SEPALES is used to simulate several economic and environmental effects on
Belgium agriculture, due to some possible changes in the European Common
Agricultural Policy.
Chapter 1
IMPORTANCE OF WHOLE-FARM RISK
MANAGEMENT IN AGRICULTURE
Ruud Huirne, Miranda Meuwissen, and Marcel Van Asseldonk
Institute for Risk Management in Agriculture, Wageningen University, The Netherlands
Abstract Risk management is an increasingly important topic. At the farm level, it

received little attention in Europe. Research indicates that whole-farm risk-
management approaches, that is approaches in which multiple risks and farm
activities are considered simultaneously, seem more efficient than ‘single risk
and commodity strategies’. This chapter first gives an overview of risk
management and then it discusses the results of a questionnaire survey among
livestock and arable farmers in the Netherlands. The survey deals with farmers’
perceptions of risk and risk-management strategies. Risk-management stra-
tegies include both ‘single risk’ strategies as well as strategies for simultane-
ously covering multiple risks. The latter are restricted to the type of strategies
currently available in the Netherlands. Next, opportunities for broadening
the scope of risk-management strategies covering multiple risks are dis-
cussed. The paper concludes by identifying areas for further research in the
field of whole-farm risk management.
Keywords: Risk management, agriculture, whole-farm approach, multiple risks, question-
naire survey
1 INTRODUCTION
The agricultural firm is constantly developing. The farm is and remains an
essential player in the agricultural supply chain and in the rural area. The
differences between the agricultural sector and the rest of the industry are
getting smaller and smaller. Increasing farm sizes result in a more indus-
trialized way of undertaking such operations. Important ‘new’ characteristics
of such bigger, industrialized farms include: importance of manufacturing
processes (vs. commodities); a systems approach to production and distri-
bution; separation and realignment of the stages in the food chain for the
purpose of efficiency and low cost-price; negotiated coordination among
4 Ruud Huirne et al.

those stages and with the environment (rural area); concern about system
power and control; and new kinds of risk combined with a more important
role for information. This implies that risk considerations are becoming more

important and should be addressed in a more formal way.

Income from farming is usually considered rather volatile because of a
whole series of stochastic factors, that is risk. Over the years, a range of risk-
management strategies has been used to reduce, or to assist farmers to
absorb, some of these risks (see later). Risk-management strategies, especially
risk-sharing strategies, generally deal with only one type of risk at a time.
For instance, futures market contracts deal with price risks, hail and storm
insurance schemes cover weather-related production risks, and livestock insu-
rance schemes cover the death of animals. Even disaster relief programs in
such events as droughts and floods consider only one type of risk (which, in
itself, is relevant if the whole
– or a notable part of the – crop or herd is
destroyed).

This chapter first discusses risk management in general (definition, sources
of risk, risk-management strategies) and then the results of a questionnaire
survey among livestock and arable farmers in the Netherlands. Because
Dutch farms are not really representative compared to farms in many other
countries, the results of the survey should be seen as an example. The survey
deals with farmers’ perceptions of risk and risk-management strategies. Risk-
management strategies include both ‘single risk’ strategies as well as strategies
for simultaneously covering multiple risks. The latter are restricted to the
type of strategies currently available in the Netherlands. Next, opportunities
for broadening the scope of risk management strategies covering multiple
risks are discussed. The chapter concludes by identifying areas for further
research in the field of whole-farm risk management.
2 RISK MANAGEMENT
The concepts of ‘risk’ and ‘uncertainty’ have already been referred to several
times. It is time to elaborate upon them. The meanings of ‘risk’ and

‘uncertainty’ come close (Hardaker et al., 2004). Uncertainty is the result of
incomplete knowledge. Risk can be defined as uncertain consequences or
results at the time of making decisions. Risk particularly concerns exposure
to unwanted, negative consequences. Risk management concerns the way
in which managers deal with risk and uncertainty (Meuwissen et al., 1999,
2001; Huirne et al., 2000; Van Asseldonk et al., 2001; Huirne, 2002).
Importance of Whole-Farm Risk Management in Agriculture 5

2.1 Types of Risk
The current government policy has increasingly been aimed at creating an
open market system. This results in, amongst other things, the fact that
agriculture in the Netherlands is increasingly confronted with price-making
in international markets, such as the world market, which generally means
lower and definitely more fluctuating prices (Huirne et al., 1997; Meuwissen
et al., 1999). Further modernization of the sector has resulted in increasing
economic consequences. Dealing with such risks, that is risk management, is
gaining more and more importance, not only for individual farmers, but for
all firms in the agricultural supply chain.

Many activities of an agricultural firm take place outdoors and are weather-
dependent. The agricultural sector also deals with live material. Because of
this the sector is an outstanding example being exposure to risks (Anderson
et al., 1977; Barry et al., 2000; Van Asseldonk et al., 2001; Hardaker et al.,
2004). Production risks are caused by the unpredictable character of the
weather and hence uncertainty as to the physical yield of animals and crops.
Diseases and infestations can have a great influence on farm results, as the
classical swine fever outbreaks in 1997/1998 and the foot-and-mouth disease
outbreaks in 2001 clearly showed.

Moreover, the prices of production means most often purchased (such

as concentrates, fertilizer, pesticides and machines) and of products sold
(such as milk, tomatoes and cut flowers) are not known, at least not at the
time decisions on these have to be taken. As already mentioned, farmers
are increasingly exposed to price-making forces in unpredictable markets.
Thus, market and price risks are important factors.

Governments form another source of risk to farmers. Changes in laws
and regulations with respect to running the farm can have far-reaching
consequences for farm results. Examples are the continuing changes in the
regulations regarding environment, pesticides, animal diseases and animal
welfare. On the other hand, governments have also set off particular risks
(up to now).

Farmers working on their farms are themselves a risk to the profitability
and continuity of the farm. The farm’s survival may be threatened by death
of the owner, or by divorce of a couple together running the farm. Long-
term illness of the owner or employees can also cause considerable losses
or can increase the costs considerably. Such risks are called human or per-
sonal risks.
6 Ruud Huirne et al.

There are also financial risks involved (Belli et al., 2001). These are
related to the financing of the farm. Using borrowed capital (such as
mortgages and the like) means that first the interest needs to be paid before
increasing one’s equity capital. For farms with relatively much debt capital
(for example, as a result of large investments), little will be left as a reward
to one’s equity capital at times of high interest rates. Only farms that are
entirely equity-financed are not subject to such financial risks, but yet can
sustain capital loss. Other risks connected to the use of credit and loans are
uncertain interest rates and inability to obtain a loan or mortgage.

2.2 Reducing and Sharing Risk
Risks are thus unavoidable and influence almost any decision the farmer
takes. That is to say risks are present, but can be counteracted. The farmer
should anticipate such risks by his management. But how? In what way can
risks be reduced? There are two categories of measures to reduce risks:
taking measures within the farm and sharing risks with others (Huirne et al.,
1997; Belli et al., 2001; Huirne, 2002; Hardaker et al., 2004).

During many uncertain events (extra) information can be obtained easily.
For example, asking for the weather forecast, analyzing feed or soil samples
and consulting experts. Also particular risks can possibly be avoided or
prevented. It is known that certain activities carry more risks than others.
Reducing farm contacts can, for example, reduce the risk of disease intro-
duction considerably. Another good strategy to minimize risks is not to
invest all of one’s money on a single farm activity. By selecting a combi-
nation of activities, risks can be considerably reduced. The same holds for
having various suppliers and buyers. Flexibility can be mentioned as a last
measure at the farm level. Flexibility refers to how well a farm can anticipate
changing conditions. For example, by investing in multipurpose machines
and buildings.

The second set of measures refers to sharing risks with others (Huirne
et al., 1997; Hardaker et al., 2004). One possibility here is buying insurance.
At present, there are several types of insurance available, with which, by
payment of a premium, risks can be reduced or even eliminated. The farmer
can also conclude contracts for example with suppliers and buyers in which
price agreements are laid down. Agreements can be made on the duty to
deliver and to buy as well as on the quality of the products or raw materials.
Lastly, by using the futures market, price risks can largely be eliminated.
The futures market is not yet well known in the Netherlands, but in the USA

it is popular for a number of agricultural products.
Importance of Whole-Farm Risk Management in Agriculture 7

Most farmers try to reduce risks when they face decisions that may have
a considerable influence on their income or wealth (Anderson et al., 1977;
Belli et al., 2001; Hardaker et al., 2004). Examples of such decisions are
sizeable investments in milk quotas or in a second farm enterprise. The
attitude of reducing exposure to risks is called risk aversion. A risk-averse
person is willing to sacrifice part of his income to reduce risks. This consi-
deration serves as a means to make a choice among the above measures.
However, reducing risks will generally involve a cost.
2.3 Risk Perception
Managers, policy makers and researchers alike often have a binary way
of dealing with risk and uncertainty. One either assumes certainty and an
exactly predictable future, or uncertainty and an entirely unpredictable
future. In the latter case further analyses are often omitted and decisions are
made either intuitively or not made at all. Under- as well as overestimating
the risks is potentially dangerous. Further analysis reveals that there are at
least four levels of risk and uncertainty (Courtney et al., 1997):

1. A clear-enough future; a single forecast precise enough for the purpose of
decision making
2. Alternate futures; a few discrete outcomes that define the future
3. A range of futures; a whole range of possible outcomes
4. True ambiguity; no basis to forecast the future

Levels 1 and 4 do not occur very often in practice; they are extreme situations.
Therefore, it is all the more distressing that many managers and advisors
regularly operate at these levels of risk. Particularly working at level 1 where
calculations are carried out and advice is given under the assumption of

complete information and certainty, is alarming.
3 FARMERS’ PERCEPTIONS OF RISK
MANAGEMENT
3.1 Materials
The questionnaire survey included questions on: (i) the farm, (ii) farmers’ risk
attitude, (iii) farmers’ perception of risk-management strategies, (iv) their
perceptions of risks and the extent to which risks are managed on their
own farm, (v) farmers’ ability to define ‘risk management’, and (vi) farmers’
interest into assistance for setting up a whole-farm risk-management plan
8 Ruud Huirne et al.

for their own farm. Most questions were closed questions, mainly in the
form of Likert-type scales ranging from 1 to 5 (Churchill, 1995). In total, the
questionnaire included 177 variables. The (pretested) questionnaire was
sent in July 2001 to 390 clients of the Rabobank (major agricultural bank
in the Netherlands). These included cattle, pig, poultry and arable farmers.
After screening on completeness, the questionnaires of 101 farmers were
available for statistical analyses, that is, the effective response rate was 26%.
3.2 Results
The majority of respondents has more than one type of farming: 44 farmers
have dairy cattle on their farm, 58 have pigs, 9 respondents have poultry and
84 of the respondents are (also) crop farmers. In order to get insight into
farmers’ risk attitudes, 5 statements were rated. Table 1 shows the results.
Table 1. Farmers’ attitude towards risks, n = 101 (1: don’t agree; 5: fully agree).

1
(%)
2
(%)
3

(%)
4
(%)
5
(%)
Average Std
I am willing to take more risks
than other farmers
7 16 44 22 11 3.14 1.04
I need to take risks to be
successful
9 15 26 40 10 3.27 1.12
I am reluctant to introducing
new ideas
14 27 29 25 5 2.79 1.12
New technologies first need to
be proved at other farms
16 23 27 26 8 2.88 1.20
I am more concerned about
losses than forgoing some
profits
20 17 40 18 5 2.71 1.14

From the scores in Table 1 it can be concluded that based on these ques-
tions respondents have a risk-seeking attitude. It is noteworthy that this holds
for all statements.
Table 2 shows farmers’ perceptions of risk-management strategies. We
subdivided the strategies into strategies that cover single risks and strategies
that simultaneously cover multiple risks. In making this subdivision we
assumed that new technologies are primarily implemented to deal with

production risks, that leasing machinery has mainly to do with financial risks
and that leasing milk quota mostly deals with production risks. In the
category ‘multiple risk strategies’, we assumed that vertical and horizontal
cooperation deal with both price and production risks. In relation to spatial
diversification we supposed that this has not only to do with diversifying
production risks but most likely also with diversifying institutional risks (e.g.
in case of environmental requirements) and/or price risks.
Importance of Whole-Farm Risk Management in Agriculture 9

Table 2 shows that, in general, farmers perceive the single risk-manage-
ment strategies as more relevant than the strategies covering multiple risks:
of the ten strategies ranked highest (see last column ‘overall rank’) only
four strategies are within the multiple risk category. These strategies include
increasing the solvency rate, comprising financial reservations, on-farm diver-
sification and vertical cooperation. Popular risk-management strategies in
‘single-risk strategies’ are strict hygiene rules, business insurance, personal
insurance and the application of new technologies.
Table 2. Perception of risk-management strategies, n = 101 (1: not relevant at all; 5: very
relevant).

Average Std Overall rank
Single-risk strategies
(1)
Strict hygiene rules 4.08 0.96 1
Business insurance 3.80 0.98 4
Personal insurance 3.71 1.09 5
Application of new technologies 3.64 0.93 6
Manure delivery contracts 3.54 1.35 7
Leasing/renting machinery 3.44 1.24 8/9
Price contracts for farm input 2.90 1.10 12

Price contracts for farm output 2.88 1.10 13
Leasing/renting milk quota 2.43 1.09 15
Futures and options market 2.35 0.92 16
Multiple risk strategies
(2)
Increase solvency rate 4.02 0.96 2
Comprise financial reservations 3.81 0.99 3
On-farm diversification 3.44 1.21 8/9
Vertical cooperation 3.40 1.20 10
Horizontal cooperation 3.27 1.20 11
Off-farm investments 2.75 1.21 14
Off-farm employment 2.27 1.31 17
Spatial diversification 2.15 1.00 18

Asking respondents for their ‘top 3’ risk-management strategies resulted
in the following answers (the percentage of respondents indicating a parti-
cular strategy is given in parentheses):