Agroecology, scaling and interdisciplinarity - Global Restoration ...

Indlæg til seminarrækken
“Bedriftsstudier, systemteori og systemisk forskning”, tirsdag den 2/4 2003
Bedriften som udgangspunkt for opskalering
Af Tommy Dalgaard, JPM-RUKA
Mit foredrag vil tage udgangspunkt i den teoretiske ramme for opskalering, der er
gennemgået i vedlagte artikel til Agriculture, Ecosystems & Environment.
Med bagrund heri vil jeg gennemgå et eksempel på scenarier for
drikkevandsbeskyttelse, hvor bedriften, og bedriftsmodellen FASSET, er anvendt som
udgangspunkt for opskalering af landbrugets kvælstoftab i et vandopland omkring
Tyrebækken syd for Bjerringbro. I vedlagte, skriftlige indlæg, der stammer fra
afslutnings-seminar-rapporten for projektet ”Arealanvendelse og landskabsudvikling
belyst ved scenariestudier” (ARLAS), fås et indtryk af den anvendte metodik, der
danner baggrund for det eksempel, der bliver præsenteret i nærværende seminarrække
den 2/4 2003.
Agroecology, scaling and interdisciplinarity
Tommy Dalgaard a , Nicholas J. Hutchings a , John R. Porter b
a
Danish Institute of Agricultural Sciences, Department of Agroecology, Box 50. DK-
8830 Tjele, Denmark
b
The Royal Veterinary and Agricultural University. Department of Agricultural
Sciences. Agrovej 10, DK-2630 Taastrup, Denmark
1 Introduction
A major challenge facing the world is how a 21 st century population of perhaps nine
billion people will feed themselves in a sustainable manner (Evans, 1998). During the
20 th century, a doubled population was fed via the so-called Green Revolution, with
its introduction of pesticides, synthetic fertilisers and new high-yielding cultivars.
With the reduction in the proportion of hungering people from more than fifty percent
of the total population after World War II to under twenty percent today (Grigg,
1993), the success of this revolution is indisputable. However, there are still
malnourished people and the impacts of intensive agriculture on natural resource
degradation and the environment may not be sustainable (Brown, 2000). The
proposed role of agroecology is to facilitate the design and management of sustainable
food production systems (Gliessman, 1998), and to investigate possible synergisms
that can help alleviate the above problems (Altieri, 1980). However, agroecology has
not fully matured as a scientific discipline. In this paper, the definition and scientific
method of agroecology, its credentials as a scientific discipline and the challenges that
face it are considered. The intention is to establish a general framework for the
integration of information within agroecology, and for the communication of this
1

information to the decision-makers targeted. Here it is recognised that the rationale for
agroecology is currently the need to develop sustainable systems of food production
and this requires that knowledge must be effectively delivered to the people who are
in a position to take appropriate action.
2 A history of agroecology
The term agroecology was in parallel proposed by German zoologists (Friederichs,
1930), and American crop physiologists (Hanson, 1939) as a synonym for the
application of ecology within agriculture. At that time, ecologists had relatively
narrow foci but with a trend towards a more integrative view of ecosystems. The early
population ecology school of Henry Gleason investigated plant populations seen from
the organism’s perspective, thereby focussing on the hierarchical levels of the
organism (Figure 1). In contrast, the community ecology school of Frederic Clements
viewed plant populations from the landscape perspective, thereby also including
higher hierarchical levels than the organism (O’Niell et al., 1986). However, the true
roots of agroecology probably lie in the school of process ecology as typified by
Arthur Tansley (1935), whose worldview included both biotic entities and their
environment (Figure 1). It was from this school of process ecology that the
agroecosystem concept emerged (Harper, 1974), and the foundations for modern
agroecology were laid.
****** Figure 1 around here ******
2.1 “Hard” agroecology
According to Hecht (1995), the hard branch of agroecology (physical-analytical and
natural science based) was initiated by works such as “Silent Spring” (Carson, 1964),
“The Population Bomb” (Ehrlich, 1966), “Tragedy of the commons” (Hardin, 1968)
and “The Limits to Growth” (Meadows et al., 1972). The gloomy predictions of these
and similar polemical writings have largely not come to pass, mainly because the
speed of technological developments was underestimated. However, hard agroecology
has shown that badly managed agriculture can lead to the degradation of agricultural
land (Waldon et al., 1998), undesirable changes in semi-natural ecosystems (Lambert
et al., 1990) and the depletion and pollution of natural resources (e.g. Molenaar,
1990). Consequently, the focus of agricultural science has changed over the past 20-
30 years from the maximisation of food and fibre production towards understanding
the mechanisms linking costs (nutrient losses, loss of biodiversity and landscape
degradation) to the benefits of agriculture (production, wealth generation and
landscape maintenance). To understand these linkages required a combination of
ecology, agronomy and economy (Reijntjes et al., 1992) that may be considered
“hard” agroecology. Such hard systems thinking, integrating various disciplines
within natural sciences and economy, was significantly developed during the 1980’es
and 1990’es, but remains the approach of an engineer or an classical economist
(Checkland, 1999). This means that the resources entering and leaving agricultural
systems are considered to be finite capital measured in physical or monetary units
(Pearce, 1996). Furthermore, the position of the observer and scientist are thought of
as external to the systems under study, which as we will see is not necessarily the case
of soft agroecology.
2

2.2 ‘Soft’ agroecology
There has been a debate whether hard system optimisation of agriculture alone could
solve the problem of feeding an expanding world population. It is increasingly felt
that this is not the case and that a much broader view of the structure, function and
role of agroecosystems is called for (Conway, 1987). Such a vision addresses ‘hard’
issues such as the flows of energy and matter through agroecosystems but also
includes the role of humans and society, and the empowerment of citizens for
developing their own food systems, and thereby feeding themselves. The exploration
of the interaction between these human activity systems and the hard agroecosystem
is here defined as ‘soft’ agroecology. According to this soft-system thinking
(Checkland, 1999), the capital entering and leaving agricultural systems is not only
measured in physical units but also includes cultural knowledge, human experiences,
potentials for technological developments etc. In contrast to hard capital, this soft
capital is flexible (Pearce, 1996) and can even to some degree substitute hard capital.
For example, knowledge of traditional farming systems inherited from their
forefathers may help future farmers to save physical inputs (Gliessman, 1990a).
However, a major problem is that the disciplines of rural sociology and economics,
which deal with this area of soft agroecology, tend to operate at higher hierarchical
levels than the hard disciplines of agronomy and ecology. This means that the soft
disciplines often work at the farm or the regional level, while the hard disciplines
often work at the plot or the field level. Furthermore, some soft systems researchers
work as accomplices to the farmer, both giving and receiving knowledge, unlike their
hard systems colleagues who work as external observers to the system under study.
This is a consequence of the inclusion of interactions between humans within the
window of agroecology (Figure 1). They argue that all people dealing with food
production- and consumption systems, including scientists, are intimately and
subjectively involved in the activities of the growing of food and that to study this
process is to become a part of it (Longino, 1990).
2.3 Where is agroecology now?
Recognising that agroecology is still developing, a survey of the published literature
was conducted to establish its current status. The survey was conducted by
interrogating electronic databases (CAB, 2001; AGRICOLA, 2001; ISIS-SCI, 2001;
SSCI, 2001; ECONLIT, 2001), reading literature reviews (Carls, 1988, 1989, 1990)
and visiting The Agroecology Library, University of California, Santa Cruz. In
agreement with Carroll et al. (1990), most references were related to natural sciences
within the fields of agronomy and ecology (e.g. the work in Gliessman, 1990b).
However, references were also found within the social sciences (e.g. Francis and
King, 1997; Thomas and Kevan, 1993), economics (e.g. Allen, 1999; Rosset, 1996),
or in combination of two or more areas (e.g. Edwards et al., 1993). To quantify this
distribution, the number of references to “agroecology” or “agro-ecology” (with a
hyphen) in literature databases of natural sciences (ISIS-SCI, 2001), resulting in 94
references, social sciences (SSCI, 2001) and economics (ECONLIT, 2001) were
compared. The majority (66%) of the references were only found in the natural
science databases, with 13% only in social science database, and 5% only in economic
3

literature. No references were in the databases from all three fields of science. The
remaining references were found in two out of the three fields, with 2% in social and
natural sciences, none in social sciences and economics, and 16% in a combination
between natural sciences and economics (Figure 2).
****** Figure 2 around here ******
Compared to the total number of references in the searched databases, relatively few
referred to agroecology. For example CAB (2001) refers to 1195 abstracts including
the term agroecology out of the more than 2 million references in total. In
comparison, more than 300,000 references referred to animal nutrition. Using the
definition of agroecology stated in the next section, we could have redefined a number
of additional and often earlier studies as agroecological, even though the authors
chose not to describe them as such at the time. However, the point of the survey was
not to determine what work was being done but rather whether the scientists involved
considered the studies to be agroecological.
2.4 A definition of agroecology
In this paper, agroecology is defined as “the study of the interactions between plants,
animals, humans and the environment within food production- and consumption
systems”. Agroecology as a discipline therefore covers integrative studies within
agronomy, ecology, sociology and economics (Figure 1). Most authors acknowledge
agroecology as a discipline of integration, but define it in other terms, for example as
‘the application of ecological science to design and management of sustainable
agroecosystems’ (Gliessman, 1998). Thereby, the upper part of the window of
agroecology in Figure 1 is excluded. Clearly there is still not one, finally
acknowledged definition of agroecology, indicating the ongoing development within
the discipline.
The historical development of agroecology shows that it began originally as a part of
crop physiology, agricultural zoology, and ecology but the term was adopted by a
movement which wished to promote the development of sustainable agriculture
through the integration of ideas and methods from other disciplines (Altieri, 1995).
Now agroecology departments exist at a number of universities across the world but
particularly in the USA and Europe. This implies that at least some people think that
agroecology has made the transition from a proposition to a separate scientific
discipline. In the next section, the case for considering agroecology as a separate
scientific discipline is examined.
3 Agroecology as a separate, scientific discipline
3.1 A separate discipline
To be considered a separate discipline, agroecology must be distinguishable from
existing disciplines. The argument is that agroecology is distinguished from its
parental disciplines of agronomy, ecology and socio-economics by its integration
between these disciplines and across scales. The agroecology-related studies found in
the literature survey were characterised by an integrative approach, where information
4

from single disciplines was collected and combined to solve problems at a higher
scale. An additional indication that agroecology is a separate discipline is that the
numbers of references to agroecology have increased in recent years, indicating that
more scientists feel that their work lies sufficiently far from the existing scientific
disciplines that an alternative term is necessary.
3.2 A scientific discipline
The assessment of agroecology as a scientific discipline was made using the norms of
science as defined by the sociologist Robert King Merton (Merton, 1973). This
approach was inspired by a recent attempt by the physicist John Ziman (Ziman, 2000)
to define science in terms of what it is and what it means.
The first Mertonian norm of science is communalism, meaning that the outcomes of
academic science are delivered to the public in the broadest sense, including other
scientific colleagues and the wider public. Scientists differ in the weight they assign to
the importance of these dissemination routes. These can vary from scientific papers in
specialist journals to popular television programmes. Agroecology values
communalism. It is probably the case that many agroecologists place as much
emphasis on sharing results with society as with their scientific colleagues.
The second norm is that science should be universal and open to contributions from
all, irrespective of race, gender, nationality, religion etc. The only things that should
wither, and be excluded from science, are ideas and theories not meeting with
experimental verification or observation. Agroecologists would try to maintain the
norm of universality in the Mertonian sense, as is evidenced by the papers refereed to
in section 2.3 above. However, universality in agroecology can often be very broad
and may deliberately include other stakeholders, so that agroecology sometimes
borders on being a socio-political movement. Agroecology faces a paradox when
moving its focus to higher and thereby more aggregated levels of hierarchy (Figure 1,
Figure 3). At the highest hierarchical levels, local context can so swamp generality
that research in, for example, rural sociology often ends as a series of case studies
from which it is impossible to draw general conclusions.
Disinterestedness in the reporting of science is the third norm. Science reporting is
unusual principally because of its impersonal manner, conveying an impression of
non-prejudice and disinterestedness from the reported work. Thus, the impersonality
and care taken in reporting science stems from the knowledge that results and
conclusions are likely to be challenged by others. It is thus part of a scientist’s duty to
facilitate this examination in the interests of the wider scientific enterprise. With
respect to disinterestedness, agroecology does not differ from any other scientific
discipline. Thus, experiments are reported, models can be verified, and social and
economic analyses are sometimes but not always repeatable. An important interaction
between these norms is that the communalism of science acts as a control on
science’s disinterestedness (Ziman, 2000) – the value of an objective scientific
observation or experiment assessed via the social process of peer review. Thus,
science and agroecology are disinterested attempts to search for objective truths that
are paradoxically mediated by socially constructed controls and evaluation processes.
5

Originality is the fourth norm. The tried and tested route to making an original
scientific contribution, in the sense of a ‘new’ piece of knowledge, is to plough the
furrow ever deeper. Thus, it is a rational scientific response to focus on ever more
detail in the hope of developing a fragment of the scientific story for oneself. In
supplement, agroecology’s originality also stems from synthesis as well as from
thinking outside the commonly accepted thesis of the existing knowledge base.
Marching under the twin banners of synthesis and interdisciplinarity, agroecology, in
line with disciplines like anthropology, psychology and sociology, is at odds with
what is commonly termed ‘basic’, natural research with its clear defined boundaries
for research, theoretical framework and sense of coherence. However, science is
perhaps moving towards the agroecological model, where the constructed and the
objective aspects of science are both recognised. For example, disciplines such as
climatology and some aspects of geosciences appear to becoming more integrative
and less reductionist. This trend is evident, for example, when the activities of humans
are seen as within the system of study rather than external to it. An example would be
the role that human activities play in land use change or as drivers of biogeochemical
processes such as the global carbon and nitrogen cycles. Nowadays, these originally
natural cycles cannot be studied and understood without understanding and
integrating the human role. In this ‘post-academic’ science (Ziman, 2000) the cultural
and social context of science as a process of knowledge creation is explicit.
Doubt is the restraint on originality in science and its application, via scepticism, is
the fifth Mertonian norm. This enters in at least two stages in the scientific process.
New ideas and theories are evaluated against a sceptical starting point – the null
hypothesis. Having successfully cleared this obstacle, a new piece of scientific
knowledge is then subjected to further doubt by anonymous referees who act on
behalf of the scientific community. In agroecology, the first of these steps sometimes
differs from the above scientific norm. Some agroecological studies do not start with a
classical null hypothesis but include semi-quantitative surveys, rapid rural
assessments and studies closely linked to agricultural development. These can be and
are subjected to the second level of doubt. However, no method of collecting data
should preclude the need for an explicit underlying hypothesis, question or
assumption that is being tested.
In summary, agroecology meets many of the Mertonian norms of science and where it
differs it does so in a way that perhaps anticipates the manner and the direction in
which the social position of science is changing. Having concluded this, the next two
sections consider two of the main issues that face agroecologists; scale and
interdisciplinarity (Marceau, 1999).
4 Scale
The issue of scale means that there is a gap between the scale at which most
agroecological information is currently generated and the scale at which most
decisions concerning food production- and consumption systems are made. The
results of agroecological studies, generated on the plot, field or farm level, cannot
always readily be generalised to the regional, national or global level relevant for
decision-makers. Because of this gap, the results are often misinterpreted or not used
6

in the decision-making process (Lerland et al., 2000). Scaling issues have been
addressed for many years in sciences such as physics (Crutchfield, 1994) or
economics (Cropper and Oates, 1992). Until recently, there has been relatively little
focus on methods to convey information between scales in the environmental sciences
of ecology (Rastetter et al., 1992) or agronomy (Bierkens et al., 2000; Stein et al.,
2001), although within theoretical ecology there are some references from the 1970s
and 1980s (e.g. O’Neill et al., 1986). Consequently, agroecologists tend to use scaling
procedures that are too simple (Grace et al., 1997) and that are poorly suited to global
problems e.g. green house gas emissions (Flavin and Dunn, 1998). However, recent
advances in scaling have responded to the need to translate environmental and socioeconomic
indicators from the scale of observation or collection to that of individual
operator or national policy. This has led to several new statistical developments, and
the application of geostatistics in particular (Riley, 2001).
4.1 Hierarchy and scale
Shown below are the classical examples of the hierarchy within natural (1) or
agricultural systems (2, 3) (Odum, 1971), where the lower levels of organisation or
complexity are to the left, and the higher levels to the right:
1) cell ↔ organism ↔ population ↔ community ↔ ecosystem ↔ landscape
2) plot ↔ field ↔ farm ↔ watershed ↔ region ↔ nation ↔ union ↔ globe
3) cell ↔ organ ↔ animal/plant ↔ herd/field ↔ farm ↔ region
These hierarchies represents levels of organizational complexity ranked by category
or class, and are the basic structural units of the system investigated (Whyte et al.,
1969). Often hierarchical levels are nested, so that high level units consist of lower
level units (Fresco, 1995; Figure 3). The boundary between hierarchical levels may be
visible, such as the skin of an organism or the shoreline of a lake, or intangible in the
case of for instance of populations and species. There are two dimensions of scale:
spatial and temporal (Figure 3). Consequently, the term scale relates to space and time
period (e.g. a regional scale study of a 100 km 2 area in four years). In this paper, the
colloquial definition of scale is used (Curran et al., 1997), meaning that large scale
studies cover large areas and/or time spans and small scale studies the reverse.
4.2 Linear, non-linear and hierarchical scaling
Even though hierarchies and scales are connected, so that high level hierarchies are
normally studied on larger temporal and spatial scales, they are not synonymous
(Figure 3). The range over which a single level in a hierarchy can extend has
consequences for describing the behaviour of higher hierarchical levels because there
may be scale-dependent processes present within one or more levels of a hierarchy.
***** Figure 3 around here *****
For example the total diesel fuel use Ftotal (l) can be calculated at the hierarchical level
of the field (Equation 1), and aggregated to the farm level (Equation 2). In this very
7

simplified example, derived from the Dalgaard et al (2001) model, Fn is the average
fuel use per ha on field n, An is the field area in ha, and N is the number of fields on
the farm.
Ftotal = Fn
•An
F
=
N
total ∑
n=
1
F
n •
A
n
With a linear scaling procedure (also called simple scaling, Grace et al., 1997), Fn is
constant, e.g. Fn=100 L/ha for all fields, and the fuel use is an identical, linear
function of both field and farm area. With a non-linear scaling procedure, Fn is a nonlinear
function of the field area. For example, if Fn=103 An – An 2 , when An< 3 ha, Fn>
100 L/ha, whereas if An> 3 ha, Fn

***** Table 2 around here *****
In the following, an application of this simplified framework is illustrated with a
simple example, with one group of decision-makers. However, in reality there are
often many different actors, stakeholders and decision-makers in many hierarchies
and scales, making application of the framework more complicated. In the present
example, the criteria of the framework of hierarchy and scale are indicated with
numbers in brackets. The targeted decision-makers were Danish politicians who after
the Rio-Conference in 1992 demanded information on how agriculture could
contribute to reduce the Danish energy use and greenhouse gas emissions by the
promised 20%. Specifically, they wanted to know whether three different scenarios
for conversion to organic farming might help to reduce the energy use (Bichelcommittee,
1999). The time scale was a 12-year period (Danish Ministry of
Environment, 1995), and the spatial scale was the 27,000 km 2 agricultural area of
Denmark (criteria 1 in Table 2). As the existing figures on energy use were sampled
on the field and animal housing level (criteria 2), the question was how to upscale
these data to the national level. The simplest option would have been a linear scaling
procedure, where the average energy uses for different field crops and livestock
housings is multiplied with national crop and livestock figures (criteria 3). However,
because of scale dependent non-linearities and significant emerging factors, a linear
scaling procedure was too simple for the upscaling (criteria 4). For reasons discussed
in section 4.2 a non-linear scaling procedure (criteria 3) was also too simple to predict
fuel use (criteria 4), and a hierarchical scaling procedure was needed (criteria 3). In
this case, a two-step application of the hierarchy-scale framework was tested. Step
one was from the field to the farm level and step two was the final national level
generalisation.
4.3.1 Step one
Measurements revealed a 47% deficiency in farm level fuel use compared to field
level literature values linearly upscaled to the farm level (Refsgaard et al., 1998), and
extended sampling on the field and farm level was initiated (criteria 4). A new model
for calculation of farm level fossil energy was made (Dalgaard et al., 2001) including
fuel use as a function of the amount of inputs used, yield and the soil type on each
field (criteria 3). Also, the above-mentioned emergent factors of fuel use for transport
between fields and the farm and between the farm, fodder stocks and feedstuff
businesses were included. Finally, the new model was verified (criteria 4) with
samples of fuel use divided by crop-type and farm level and the observed - simulated
difference was found to be insignificant.
4.3.2 Step two
The derived model was used in the final national level generalisation of the fossil
energy use in the primary Danish agricultural sector. A linear scaling procedure was
used, where the estimated average energy use for each crop and animal type was
multiplied by the areas of crops and the number of animals according to national
agricultural statistics (criteria 3). The simulated and lineally upscaled energy use
9

embedded in each of the accounted energy carriers was similar to the expected energy
use according to national statistics, with a total difference of less than 3% (Dalgaard et
al., 2001). In this case, it was concluded that the applied linear scaling procedure was
sufficient for the step two generalisation (criteria 4).
5 Interdisciplinarity
Interdisciplinary means working across traditional disciplinary boundaries. For
science in general, this can lead to creative breakthroughs, the identification of
oversights, and provide more holistic solutions than work within single disciplines
(Nissani, 1997). For agroecology, the specific issue is its continued growth from its
roots in agriculture and ecology to include relevant aspects in sociology and
economics. This development is desirable both because humans are an integral and
important part of food producing systems and because it is necessary if decisionmakers
are to act on the basis of both ecological, social and economic principles
(Wood, 1998).
Achieving interdisciplinarity will require the removal of the barriers to the flow of
information between the disciplines relevant to agroecology. These barriers include
mind set and communication, where science has developed into increasingly
specialised disciplines, talking different languages and having different areas of
interest. Ideally, one could call upon scientists with a more generalist background to
assist in communication between specialists but institutional barriers within modern
science mean there is no encouragement for such creatures to flourish. These barriers
are both physical and organisational. The physical barrier is that scientists from the
different disciplines that interact with agroecology are normally in different institutes
or departments, often in different physical locations. With the developments in
information technologies and infrastructures this barrier may be less than it once was
but the lack of social interaction will continue to be an obstacle to collaboration. The
organisational barrier relates to the way in which science reward researchers via the
provision of resources and career advancement. This depends heavily on the
publication of papers in peer-reviewed scientific journals. Here the researchers within
agroecology are faced with new opportunities but also several problems. The
challenge of scaling is encouraging the development of novel experimental
methodologies, e.g. through the combined use of modelling, observational science and
advanced statistical mapping procedures (Riley, 2001). However, for the more
reductionist scientists, integration to higher hierarchical levels, e.g. from the field to
the farm level, means fewer opportunities for controlled experiments, experiments of
greater duration and more time spent communicating with scientists from other
disciplines. The experiments are likely to provide results that are more difficult to
generalise than is common for more reductionist disciplines. However, as discussed
earlier, the norms and social position of science are changing as the presumption that
science is sacrosanct withers and scientists increasingly have to argue their value to
human endeavour. As agriculture is a mature science, compared to other disciplines
such as biotechnology or microelectronics, agroecologists may find themselves in the
forefront of this development.
10

6 Procedures and strategies to address agroecological questions
at different scales
It is argued here that studies of how to cross the barriers of scaling and
interdisciplinarity should be central issues for future agroecogical research projects.
For soil sciences, Bouma (1997) drew similar conclusions and appealed for new
methods to deal with the issues and proposed a seven-step procedure for research in
sustainable management of agricultural soils (Bouma et al. 1998). This procedure was
found useful to address agroecological questions (Wagenet, 1998) but problems were
encountered in integrating socio-economic information and in the issues of hierarchy
and scale (Dumanski et al., 1998). The framework presented here (Table 2) builds
upon Bouma et al.’s (1998) procedure and corresponds to the application of the
scientific method of natural sciences - observing, measuring and interpreting -
stressed in the introduction to the agroecology book by Carrol et al. (1990). The
difference is that the framework presented here was extended to distinguish between
hierarchy and scale in the form of the defined linear, non-linear or hierarchical scaling
procedures. In addition, the present framework included a test (criteria four) in the
defined scaling procedure and an iterative exploration of scaling functions that
proceeds until the error does not exceed a sensible threshold value (Table 2). To
develop such scaling functions, comprehensive decision-support systems have been
developed (Bierkens et al., 2000) but to date only methods to answer “hard”
agroecological questions have been included, while the integration of information
from the soft agroecological ones is not included. The framework of hierarchy and
scale presented here is similarly bound to hard agroecology but provides a hint at a
starting point for interdisciplinary research projects. This would typically be a
common problem of sustainability, investigated by both “soft” and “hard” researchers,
corresponding to the criteria one problem in the framework. However, because the
“soft” sciences do not produce pure quantitative results, it might not merely be a
question of currency shifts - for example from the physical units of an agronomic
study to the monetary units of an economical study (Squire and Gibson, 1997) -
followed by the traditional scaling procedure of criteria two to four. Instead, criteria
two to four could be interpreted as a communicative process (Bawden, 1995), where
statements regarding solutions to the common problem are compared at spatiotemporal
scales of relevance for decisions.
One example where the results from “soft” and “hard” sciences differed, and
interdisciplinary research would gain knowledge is illustrated in the recent debate
concerning the possible benefits of introducing a “golden rice variety” (Schiermaier,
2001). In contrast to traditional varieties, the golden rice is genetically engineered to
contain vitamin A precursors, a deficiency of which causes blindness and other
illnesses. One of the inventing plant scientists predicted the introduction of this new
variety to solve the “unnecessary death and blindness of millions of poor every year”
(Potrykus, 2001). However, nutritionists argue that vitamin A can only be absorbed by
the body if consumed with sufficient oils and fats, which is often lacking in many
Third World diets, and social scientists argue that some of the vitamin A problem is
caused by the social status of eating hulled, white rice, with a low vitamin A content,
and that better nutrition could as easily have been achieved by campaigning for the
consumption of existing, more wholesome varieties of brown paddy rice (Schnapp
and Schiermaier, 2001). This example illustrates that the potential effect of feeding
11

malnourished people with golden rice varieties differs when seen from the perspective
of the uni-disciplinary perspective of the plant scientist than from a perspective that
also includes nutritional and social knowledge. Estimating consequences of
conversion to organic farming is another subject where both ecological (Dalgaard et
al., 1998), economic (Hansen et al., 2001), and social driving forces (Trewavas, 2001)
are relevant to include. This is because conversion to organic farming is driven both
by the ecological potential of this system compared to conventional farming and by
the socio-economic gains for farmers and the society.
A common feature of the problems encountered in the development of sustainable
food production is the need for feedback mechanisms between the different research
disciplines and between decision-makers and researchers. This is because the
decisions to be made must take into account both the functioning of natural
ecosystems and the response of humans acting either as individuals or as part of
society. Consequently soft and hard science mechanisms interact with one another,
and a dialectical approach (Levins and Lewontin, 1985), where both top-down and
bottom-up viewpoints are valued, becomes fundamental for the iterative integration of
information from multiple disciplines.
7 Conclusions and perspectives
The current driving force for agroecology is the need to facilitate the development of
more sustainable agricultural systems. This emphasis on sustainability is drawing
agroecology up from its roots in agronomy and ecology to include elements of both
sociology and economics. This study found that agroecology can currently be defined
as the study of interactions between plants, animals, humans and the environment
within food production- and consumption systems. One of its hallmarks is that it
integrates between scientific disciplines and scales.
The first Green Revolution was achieved primarily through the development and
application of technology. Whilst successful in terms of food production, serious
questions have been raised concerning the impact of these agricultural practices on the
health of the cultivated land (Oldeman et al., 1991). Conway (1997) argued that a
second Green Revolution is required, which is even more productive than the first
Green Revolution and even more ’green’ in terms of conserving natural resources and
the environment. In addition to the productive and environmental aspects, the social
and economic dimensions of food production systems must therefore also be
considered.
In recent years, significant progress has been made in understanding the issue of
scaling and in the development of appropriate techniques. The barriers to
interdisciplinarity are mainly cultural and political not technical, and lying deeply
embedded in the way science has developed, these barriers present the major obstacle
to the development of agroecology.
Acknowledgements
The authors wish to thank the ARLAS research project, and the Danish Research
Councils for funding this work as part of the research project Agrar2000, from which
this is paper no. 4. Tommy Dalgaard would like to thank Dr. Avaz Koocheki from
12

Iran and Professors Steve Gliessman and Miguel Altieri from the USA for initial
discussions during a study visit to The Agroecology Centre, University of California,
Santa Cruz and to Chris Kjeldsen, Aalborg University, for his comments on the thesis
presented in this paper. Finally, we thank the reviewers of the paper for their many
useful comments, which helped significantly to improve our work.
References
AGRICOLA (2001) AGRICultural OnLine Access (http://www.nalusda.gov/ag98/). The National
Agricultural Library, U.S. Department of Agriculture, Beltsville, Maryland, USA
Allen, P., 1999. Reweaving the food security safety net: Mediating entitlement and entrepreneurship.
Agriculture and Human Values 16, 117-129.
Allen, T.F.H. and Hoekstra, T.W., 1992. Toward a Unified Ecology. Columbia University Press, New
York. ISBN 0-231-06919-7. 384 p.
Altieri, M.A., 1980, 1995. Agroecology: The Science of Sustainable Agriculture. Westview Press,
Colorado, USA. ISBN 0-8133-1764-9. 433 p.
Bawden, R., 1995. Systemic development: a learning approach to change. Centre for Systemic
Development, University of Western Sydney, Hawkesbury. Occasional Paper #1. 68 p.
Bichel Committee, 1999. Rapport fra den Tværfaglige økologigruppe (Report from the interdiciplinary
group on organic farming). Danish Environmental Protection Agency, Copenhagen. ISBN 87-7909-
292-6. 98 p.
Bierkens, M.F.P., Finke, P.A., de Willigen, P., 2000. Upscaling and Downscaling Methods for
Environmental Research. Developments in Plant and Soil Sciences vol. 88. Kluwer Academic
Publishers, Dordrecht. ISBN 0-7923-6339-6. 190 p.
Bouma, J., 1997. Soil Environmental Quality: A European Perspective. J. Environ. Qual. 26, 26-31.
Bouma, J., Finke, P.A., Hoosbeek, M.R., Breeuwsma, A., 1998. Soil and water quality at different
scales: concepts, challenges, conclusions and recommendations. Nutrient Cycling in Agroecosystems
50, 5-11.
Brown, L., Flavin, C. and Fench, H., 2000. State of the World 2000. W.W.Norton and Compagny,
London. ISBN 0-393-04848-9. 263 p.
CAB (2001) Commonwealth Agricultural Bureaux, CAB International (http://www.cabi.org/).
Wallingford, Oxford, UK.
Carls, J., 1988. Abstracts on Sustainable Agriculture. Vol. 1. Deutches Zentrum für
Entwicklungstechnologien, Braunsweig, Germany. 294 p.
Carls, J., 1989. Abstracts on Sustainable Agriculture. Vol. 2. Deutches Zentrum für
Entwicklungstechnologien, Braunsweig, Germany. Vieweg. ISBN 3-528-02060-1. 372 p.
Carls, J., 1990. Abstracts on Sustainable Agriculture. Vol. 3. Deutches Zentrum für
Entwicklungstechnologien, Braunsweig, Germany. Vieweg. ISBN 3-528-02062-8. 453 p.
Carroll, C.R., Vandermeer, J.H., Rosset, P.M. (eds.), 1990. Agroecology. Biological Ressource
Management Series. McGraw Hill Publishing Compagny, New York. ISBN 0-07-052923-X. 641 p.
Carson, R.L. 1964. Silent Spring. Noughton Miffin Compagny. ISBN 0-395-68329-7.
13

Checkland, P., 1999. Systems Thinking, Systems Practice. Includes a 30-year retrospective. John Wiley
and Sons, Chichester. ISBN 0-471-98606-2. 330 p.
Conway, G., 1997. The Doubly Green Revoloution. Food for All in the Twenty-First Century. Cornell
University Press. ISBN 0-8014-8610-6. 335 p.
Conway, G., 1987. The Properties of Agroecosystems. Agricultural Systems 24, 95-117.
Cropper, M.L. and Oates, W.E., 1992. Environmental Economics: A Survey. Journal of Economic
Litterature XXX, 675-740.
Crutchfield, J. P., 1994. Is Anything Ever New? Considering Emergence. In: Complexity: Metaphors,
Models, and Reality, G. Cowan, D. Pines, and D. Melzner (eds.). SFI Series in the Sciences of
Complexity XIX:479-497. Addison-Wesley, Redwood City.
Curan, P.J., Foody, G.M., van Gardingen, P.R., 1997. Scaling Up. In: Gardingen, P.R., Foody, G.M.
and Curran, P.J. Scaling-up From Cell to Landscape. p. 1-6. Society for Experimantal Biology.
Seminar Series 63. Cambridge University Press. ISBN 0-521-47109-5. 386 p.
Dalgaard, T., 2001. Simulation and Regional Generalisation of Agricultural Resource Use. Ph.D.
Thesis. The Royal Veterinary and Agricultural University of Denmark, Copenhagen. ISBN 87-988287-
3-8. 184 p.
Dalgaard, T., Halberg, N., Kristensen, I.S., 1998. Can organic farming help to reduce N-losses?
Experiences from Denmark. Nutrient Cycling in Agroecosystems 52, 277-287.
Dalgaard T., Halberg N., Porter J., 2001. A model for fossil energy use in Danish agriculture used to
compare organic and conventional farming. Agriculture, Ecosystems and Environment 87(1), 51-65.
Danish Ministry of Environment and Energy, 1995. Danmarks Energifremtider (Danish energy
futures). The Danish Energy Agency, Copenhagen. 214 p.
Dumanski, J., Pettapiece, W.W., McGregor, R.J., 1998. Relevance of scale dependent approaches for
integrating biophysical and socio-economic information and development of agroecological factors.
Nutrient Cycling in Agroecosystems 50, 13-22.
ECONLIT, 2001. American Economic Association's electronic bibliography of economic literature
(www.econlit.org). Journal of Economic Literature, Pittsburgh, USA.
Edwards, C.A., Grove, T.L., Harwood, R.R., Pierce Colfer, C.J., 1993. The role of agroecology and
integrated farming systems in agricultural sustainability. Agriculture, Ecosystems and Environment 46,
99-121.
Erlich, P.R., 1966. The Population Bomb. Buccaneer Books. 1997 reprint. ISBN 1-568-49587-0.
Evans, L., 1998. Feeding the 10 billion: Plants and Population Growth. ISBN 0-521-64685-5.
Cambridge University Press.
Flavin, C., Dunn, S. 1998. Responding to the Threat of Climate Change. In: Brown, L.R., Flavin, C.F.,
French, H. (eds.), 1998. State of the World. W.W. Norton, New York. pp.113-130. ISBN 0-393-31727-
7.
Francis, C. and King, J., 1997. Impact of personal values on agricultural research. Society and Natural
Resources 10(3), 273-282.
Fresco, L.O., 1995. Agro-ecological knowledge at different scales. In: Bouma, J., Kuyvenhoven, A.,
Bouman, B.A.M., Luyten, J.C. and Zandstra, H.G. (eds.): Eco-regional Approaches for Sustainable
14

Land Use and Food Production. pp. 133-141. Kluwer Academic Publishers, Dordrecht. ISBN 0-7923-
3608-9.
Friederichs, K., 1930. Die Grundfragen und Gesetzmässigkeiten der land- and forstwirtshaftlichen
zoologie, Berlin. In: Tischler, W., 1965. Agrarökologie. Gustav Fischer Verlag, Jena. 499 p.
Gliessman, S.R., 1998. Agroecology. Ecological Processes in Sustainable Agriculture. Ann Arbor
Press, Chelsea, MI. ISBN 1-57504-043-3.
Gliessman, S.R., 1990a. Understanding the basis of sustainability for agriculture in the tropic:
Experiences in Latin America. Sustainable Agricultural Systems, Soil and Water Conservation Society,
Ankeny, Iowa, The USA. Chapter 22, pp. 378-390.
Gliessman, S.R., 1990b. Agroecology. Researching the Ecological Basis for Sustainable Agriculture.
Springer Verlag, Berlin. ISBN 0-387-97028-2. 380 p.
Grace, J., van Gardingen, P.R., Luan, J., 1997. Tackling Large-scale problems by scaling up. In:
Gardingen, P.R., Foody, G.M. and Curran, P.J. Scaling-up From Cell to Landscape. p. 7-16. Society for
Experimantal Biology. Seminar Series 63. Cambridge University Press. ISBN 0-521-47109-5. 386 p.
Grigg, D., 1993. The World food problem. 2 nd edition. Oxford, Blackwell.
Hansen, B., Alrøe, H.F., Kristensen, E.S., 2001. Approaches to assess the environmental impact of
organic farming with particular regard to Denmark. Agriculture, Ecosystems and Environment 83, 11-
26.
Hanson, H.C., 1939. Ecology in agriculture. Ecology 20, 111-117.
Hardin, G., 1968. The Tragedy of the Commons. Science 162, 1243-1248
Harper, J.L. 1974. Agricultural Ecosystems. Agroecosystems 1, 1-6.
Hecht, S., 1995. The Evolution of Agroecological Thought. In: Altieri, M. Agroecology: The Science
of Sustainable Agriculture. Westview Press, Colorado, USA. P. 1-20. ISBN 0-8133-1764-9. 433 p.
ISIS-SCI, 2001. Science Citation Index Expanded (http://www.isinet.com). Institute for Scientific
Information. Thomson Scientific, Philadelphia, PA, USA.
Lambert, J.D.H., Brubacher, D., Arnason, J.T., 1990. Nutrient Mobility in a Shifting Cultivation
System, Belize, Central America. In: Gliessman, S.R. Agroecology. Researching the Ecological Basis
for Sustainable Agriculture. pp. 122-129. Springer Verlag, Berlin. ISBN 0-387-97028-2.
Lerland, E. van, Lansink, A.O., Schmieman, E., 2000. Summary from the conference: Sustainable
Energy: New Challenges for Agriculture and Implications for Land Use. Wageningen, The
Netherlands. May 18-20 2000.
Levins R., Lewontin R., 1985. The Dialectical Biologist. Harward University Press. ISBN 0-674-
20281-3.
Longino, E., 1990. Science as Social Knowledge. Princeton University Press. 262 pp. ISBN 0-691-
02051-5.
Marceau, D.J., 1999. The scale issue in social and natural sciences. Canadian Journal of Remote
Sensing 25:4, 347-356.
Marshall, B., Crawford, J.W., Porter, J.R., 1997. Variability and scaling: matching methods and
phenomena. In: Gardingen PR, Foody GM and Curran PJ, Scaling-up From Cell to Landscape. p. 253-
15

272. Society for Experimental Biology. Seminar Series 63. Cambridge University Press. ISBN 0-521-
47109-5. 386 p.
Meadows, D.H., Meadows, D.L., Randers, J. and Behrens, W.W., 1972. The Limits to Growth: A
Report for the club of Rome’s Project on the Predcament of Mankind. Earth Island Ltd., London.
Merton, R.K. 1973. The Normative Structure of Science. In: The Sociology of Science: Theoretical and
Empirical Investigations, N.W. Storer (ed). University of Chicago Press, pp. 267-278.
Molenaar, J.G. de, 1990. The impact of Agrohydrological Management on Water, Nutrients, and
fertilisers in the Environment of The Netherlands. In: Gliessman, S.R. Agroecology. Researching the
Ecological Basis for Sustainable Agriculture. P. 275-304. Springer Verlag, Berlin. ISBN 0-387-97028-
2.
Nielsen V., Sørensen K.G., 1994. Green Fields. Operational Analysis and Model Simulations. Report
59. National Institute of Agricultural Engineering, Bygholm. ISBN 87-7471-040-0. 155 p.
Nissani, M., 1997. Ten cheers for interdisciplinarity: The case for interdisciplinary knowledge and
research. Social Science Journal 34 (2) 201-216.
Odum, H.T. 1971. Fundamentals of Ecology. 3rd ed. W.B. Saunders, Philadelphia. ISBN 0-7216-6941-
7. 574 p.
Oldeman, L.R. et al., 1991. World-Map of the Status of Human-Induced Soil Degradation. An
Exlanatory Note. 2 nd ed. International Soil Reference and Information Centre and U.N. Environment
Programme: Wageningen, Netherlands and Nairobi.
O’Neill, R.V., DeAngelis, D,L, Waide, J.B. and Allen, T.F.H., 1986. A hierarchical concept of
ecosystems. Monographs in Population Biology. 23, 1-272
Pearce, D., 1996. Measuring sustainable development. In: Nicolaisen M (ed.): Four lectures on
environmental sciences. University of Copenhagen. ISBN 87-87848-910
Potrykus, I., 2001. In Vitro Cellular and Developmental Biology – Plants. IAPTC&B Journal, MA,
USA. Vol 37:2.
Rabbinge, R., 1997. Integrating policy and technical issues for international research on agriculture and
the environment using systems approaches. In: Teng, P.S., Kropff, M.J. et al. Applications of Systems
Approaches at the Farm and Regional Levels. Volume 1. p. 249-262. Kluwer Academic Publishers,
Dordrecht. ISBN: 0-7923-4285-2.
Rastetter E.B., King A.W., Cosby, B.J., Hornberger G.M., O’Niell, V.R., 1992. Aggregating fine scale
ecological knowledge to model coarse-scale attributes of ecosystems. Ecological Applications 2(1), 55-
70.
Reintjes, C., Haverkort, B., Waters-Bayer, A., 1992. Farming for the Future. MacMillan Press Ltd.,
London. ISBN 0-333-57011-1. 250 p.
Refsgaard, K., Halberg N., Kristensen, E.S., 1998. Energy Utilization in Crop and Dairy Production in
Organic and Conventional Livestock Production Systems. Agric. Systems 57(4), 599-630
Riley, J., 2001. Preface. The indicator explosion: local needs and international challenges. Agriculture,
Ecosystems & Environment Vol. 87 (2), p. 119-120.
Rosset, P.M., 1996. Input Substitution. A dangerous trend in sustainable agriculture. Institute for Food
and Development Policy. Working Paper 4. Oakland, CA, USA.
Schiermaier, Q., 2001. Designer rice to combat diet deficiences makes its debut. Nature 409, 551.
16

Schnapp, N. and Schiermaier, Q., 2001. Critics claim ‘sight-saving’ rice is over-rated. Nature 410, 503.
SSCI, 2001. Social Sciences Citation Index). Institute for Scientific Information. Thomson Scientific,
Philadelphia, PA, USA.
Squire, G.R. and Gibson, G.J., 1998. Scaling-up and scaling-down: matching research with
requirements in land management and policy. In: Gardingen PR, Foody GM and Curran PJ, Scaling-up
From Cell to Landscape. p. 17-34. Society for Experimental Biology. Seminar Series 63. Cambridge
University Press. ISBN 0-521-47109-5. 386 p.
Stein, A., Riley J. and Halberg, N., 2001. Issues of scale for environmental indicators. Agriculture,
Ecosystems & Environment Vol. 87 (2), p. 119-259
Tansley, A., 1935. The use and abuse of vegetational concepts and terms. Ecology 16, 284-307.
Thomas, V.G., Kevan, P.G., 1993. Basic Principles of Agroecology and Sustainable Agriculture. Journ.
Env. Ethics. 6:1, 1-19.
Trewavas, A., 2001. Urban myths of organic farming. Nature 410, 409-410
Van Latesteijn, H.C., 1997. Scenarios for Land-Use in Europe: agroecological options within socioeconomic
boundaries. In: Bouma, J., Kuyvenhoven, A., Bouman, B.A.M., Luyten, J.C. and Zandstra,
H.G. (eds.): Eco-regional Approaches for Sustainable Land Use and Food Production. pp. 43-64.
Kluwer Academic Publishers, Dordrecht. ISBN 0-7923-3608-9.
Wagenet, R.J., 1998. Scale issues is agroecological research chains. Nutrient Cycling in
Agroecosystems 50, 23-34.
Waldon, H., Gliessman, S., Buchanan, M., 1998. Agroecosystem Responses to Organic and
Conventional Management Practices. Agricultural Systems 57(1), 65-75.
Whyte, L.L., Wilson, A.G. and Wilson, D., 1969. Hierarchical Structures. American Elsevier
Publishing Compagny, New York. ISBN 444-00069-0. 317 p.
Wood, D., 1998. Ecological principles in agricultural policy: but which principles? Food Policy vol. 23.
5, 371-381.
Ziman, J.M., 2000. Real Science: What it is and What it Means. Cambridge University Press. 399 p.
17

Tables
Table 1
Example on the linear-, non-linear and hierarchical scaling procedure, used to
calculate the farm level fuel use Ftotal on a 4 ha small farm with N=2 fields, and a
larger 50 ha farm with N=3 fields.
n An
(ha)
Small Farm
Dn
(km)
Ftotal for different scaling procedures
(l)
Linear Non-linear Hierarchical
1 1 1 100 102 104
2 3 1 300 300 302
Total 4 400 402 406
Average (l ha -1 ) 100 101 102
Larger Farm
1 20 2 2000 1660 1702
2 10 1 1000 930 941
3 20 10 2000 1660 1870
Total 50 5000 4250 4513
Average (l ha -1 ) 100 85 90
An is the area of field no. n, Dn is the distance to the field.
18

Table 2.
General Framework of Hierarchy and Scale with four criteria to support and evaluate
the conveyance of information between science and a decision-maker
Criteria 1. Define the decision-maker, their problem and the scale at which the
decision-maker needs information.
Criteria 2. Determine on which scales information regarding this problem is
available and collect the relevant information.
Criteria 3. Create a hypothesis of how existing information, identified in criteria 2,
can be transformed to the scale needed for decision-making, identified in criteria 1.
First try with simple linear scaling procedures, and after having tested them in criteria
4, try more complicated, non-linear or hierarchical scaling procedures.
Criteria 4. Test the hypothesis of criteria 3 with independently sampled decisionmaker
scale information. If the hypothesis is rejected, try with a new hypothesis or
seek new information, which can be transformed to the decision-maker scale.
19

Figures and figure captions
Figure 1.
The box symbolises The Window of Agroecology within food production systems.
The viewpoints of the different schools of ecology are marked with eye signatures.
The classical, scientific disciplines, where some are within the window of
agroecology, are lined up in the right column, ordered in a hierarchy with the ‘hard’
disciplines at the bottom and the ‘soft’ disciplines at the top (Checkland, 1999).
20

Social Sciences
Natural Sciences
Economics
Figure 2.
The triangular composition of the subjects for agroecological studies. The area of the
circles is proportional to the number of references found.
21

Figure 3.
Examples of the spatial and temporal scale for investigations of hierarchical levels
within natural (light coloured), and agricultural systems (dark coloured). (After
Rabbinge, 1997).
22

ARLAS’ scenariesystem
ET GRUNDLAG FOR HELHEDSORIENTEREDE KONSEKVENSVURDRINGER AF
ÆNDRINGER I AREALANVENDELSEN OG LANDBRUGSPRODUKTIONEN
Tommy Dalgaard 1 , Chris Kjeldsen 1 , Birgit M. Rasmussen 1 , Jesper R. Fredshavn 2 ,
Bernd Münier 3 , Jesper S. Schou 3 ,Mette Dahl 4 , Irene A. Wiborg 5 , Per Nørmark 6 og
Jørgen F. Hansen 1
1 Danmarks JordbrugsForskning, Afdeling for Jordbrugssystemer. Box 50. DK-8830 Tjele.
E-mail: tommy.dalgaard@agrsci.dk. 2 Danmarks Miljøundersøgelser, Afdeling for Landskabsøkologi, Grenåvej 14, DK-8410
Rønde. 3 Danmarks Miljøundersøgelser, Afdeling for Systemanalyse, Frederiksborgvej 399, DK-4000 Roskilde. 4 Danmark og
Grønlands Geologiske Undersøgelser. Øster Voldgade 10, DK-1350 København K. 5 Landbrugets Rådgivningscenter,
Landskontoret for Planteavl. Udkærsvej 15, DK-8200 Aarhus N. 6 Viborg Amt, Skottenborgvej 26, DK-8800 Viborg.
8 Indledning
Formålet med dette kapitel er at give en oversigt over scenariesystemet i ARLAS og
eksemplificere brugen af dette system som grundlag for helhedsorienterede
konsekvensvurdringer af ændringer i arealanvendelsen og landbrugsproduktionen.
Først beskrives værkstedsområdet og datagrundlaget for de tre scenarier, som er
belyst i projektet. Dernæst gennemgås kort sammenhængene og dataflowet mellem de
modelsystemer, der er integreret i scenariesystemet, og endelig gives en oversigt over
resultater fra scenarierne.
9 Materialer og Metoder
9.1 Værkstedsområdet
Værkstedsområdet ligger på kanten mellem Viborg og Århus Amter, 30 km SØ for
Randers, og udgør et 10 km x 10 km udsnit af et typisk og geologisk varieret
Midtjydsk landskab (Torp 2002, Caspersen og Jensen 2002). Landbrugsjorden i
området er præget af intensiv svine- og kvægproduktion, sammen med forholdsvis
mange små deltidsbrug, placeret omkring egnscentret Bjerringbro By (Rygnestad et
al. 2000). Desuden præges området af en stor andel af skov og krat, specielt ved
Tange Sø, og Gudenåen der gennemløber området (Tabel 1, Figur 1).
Tabel 1. Arealanvendelsen i værkstedsområdet 1998
Værkstedsområdet (ha)
Landbrugsjord:
Mark i omdrift 5300
Vedvarende græs
Ikke landbrug:
850
Skov og krat 2500
Veje og bebyggelse 1050
Vand 200
Andre biotoper 100
I alt 10000
23

Figur 1. Arealanvendelsen i værkstedsområdet 1998
Figur 2. Marker som indgår i de landbrugsmæssige beregninger i ARLAS samt disse
markers placering i forhold til værkstedsområdet (firkanten) og i forhold til Viborg
24

Amts udpegning af skovrejsningsområder, områder hvor skovrejsning er uønsket, og
områder med særlige drikkevandsinteresser (OSD).
Landbrugspraksis i form af afgrødevalg og gødskning modelleres for alle marker på
de bedrifter, som har marker i værkstedsområdet (Figur 2).
9.1.1 Afgrødevalg og sædskifte
Afgrødevalget modelleres for hver mark med en sædskiftemodel (Kjeldsen 2000,
Dalgaard et al. 2002a). Til brug for faunamodelleringerne anvendes en forsimplet
version af den model der anvendes ved bedriftsmodelleringerne, idet afgrøden på
hver mark vælges ud fra den statistiske sandsynlighed for at denne afgrødetype
forekommer i sædskiftet på den aktuelle bedriftstype (Odderskær 2002, Dalgaard
2000). Modellen der anvendes ved bedriftssimuleringerne indeholder 13 forskellige
afgrødetyper og skelner mellem 3 forskellige sædskiftekategorier (Figur 3).
Bedrifternes marker opdeles i marker med et grovfodersædskifte (1), marker med et
salgsafgrødesædskifte (2) og marker med et vedvarende græssædskifte (3). Markerne
med grovfodersædskifte placeres tættest på bedriftens bygninger, idet der inddrages
hele marker i grovfodersædskiftet indtil der opnås det areal med grovfodersædskifte,
som er tættest på at dække et areal svarende til 0,36 ha grovfoderareal per dyreenhed
kvæg på bedriften. Marker med vedvarende græs ifølge de empiriske oplysninger for
1998 forbliver vedvarende græs (3), og de resterende marker dyrkes i et
salgsafgrødesædskifte (2).
Figur 3. Skematisk oversigt over sædskiftekategoriernes fordeling på en bedrifts
marker. 1) Grovfoder-sædskifte, 2) Salgsafgrøde-sædskifte, 3) Vedvarende
græsmarks-sædskifte.
Sædskiftemodellen arbejder med 3 forskellige bedriftstyper; A) Plantebedrifter, B)
Kvægbedrifter og C) Svinebedrifter, opdelt i henhold til EUROSTAT’s og
Fødevareøkonomisk Instituts metode (Dalgaard og Rygnestad 2000). I prototypen af
modellen er grovfodersædskiftet ens på alle bedriftstyper, mens
salgsafgrødesædskiftet på kvægbedrifter adskiller sig fra salgsafgrødesædskiftet på
svine- og planteavlsbedrifter (Tabel 2). På hver bedrift fastholdes markerne med
vedvarende græs i et vedvarende græsmarks-”sædskifte” udenfor omdriften.
25

Tabel 2. Afgrøderækkefølger i grovfoder- og salgsafgrødesædskifterne i
sædskiftemodellen.
Grovfoder-sædskiftet Salgsafgrøde-sædskiftet på Salgsafgrøde-sædskiftet på
kvægbedrifter
plante- og svinebedrifter
Byg-ært helsæd m. udlæg Vårbyg Vinterraps
1. Års kløvergræs Vinterhvede Vinterhvede
2. Års kløvergræs Vårbyg Vårbyg
Byg-ært helsæd m. udlæg Vårbyg
Slætgræs Brak
Roer Ærter
Vinterhvede
Vinterrug
Vinterbyg
Sædskiftemodellen køres for en 30 årig periode. Her vises gennemsnit for 26 års
sædskifte og gødskning, svarende til den 26 års periode, som de gennemsnitlige
modellerede N-tab i form af ammoniak og udvaskning opgøres for. De 4 første år
bruges til initialisering af modellen for udvaskning og modellen for
gødningsfordeling. Resultaterne af sædkiftemodellens afgrødevalg er samenlignet
med empirisk indsamlede afgrødeoplysninger fra 1998 (Tabel 3).
Tabel 3. Landbrugsafgrøder i værkstedsområdet 1998.
% Empiri Model
Vårkorn, modenhed 18 18
Vinterkorn, modenhed 37 34
Raps og ærter 13 16
Brak 8 8
Helsæd 2 5
Omdriftsgræs 9 7
Roer, majs og kartofler 2 2
Vedvarende græs 10 11
100 100
9.1.2 Gødskning
Husdyrgødningen fordeles hvert år på markerne i forhold til 1998 gødningsnormen for
den aktuelle afgrøde, forfrugt og forforfrugt på den aktuelle mark, og i forhold til
jordtypen på hver mark (Dalgaard et al. 2002b). Der regnes ikke med vanding i
værkstedsområdet. Der spredes ikke husdyrgødning ud på vedvarende græsmarker.
Produceres der mere plantetilgængeligt N i form af husdyrgødning end der er behov
for ifølge normberegningerne eksporteres husdyrgødning til den nærmeste bedrift,
hvor behovet er større end husdyrgødningsproduktionen. Hvis den nærmeste bedrift
26

ikke kan modtage alt husdyrgødningen eksporteres der også gødning til den
næstnærmeste bedrift, hvor behovet er større end husdyrgødningsproduktionen osv.
Der tildeles nu, i hver af de 30 år sædskiftemodellen løber, husdyrgødning til alle
marker proportionalt med N-normbehovet på de enkelte marker. Som sagt tildeles der
ikke husdyrgødning til vedvarende græs. Dernæst suppleres der for alle marker op til
N-, P- og K-normen med handelsgødning. Således beregnes produktionen, eksporten
og udbringningen af husdyrgødning, samt udbringningen af handelsgødning i
værkstedsområdet (Tabel 4).
Tabel 4. Gødningsproduktion og gødningsanvendelse på markerne (Modelleret
praksis for 1998)
Areal
(ha)
Husdyrgødning
Handelsgødning
(ton total N)
(ton N)
Produktion Eksport Udbragt Udbragt
Plantebedrifter 5310 123 -77 200 482
Kvægbedrifter 1524 343 50 293 115
Svinebedrifter 2155 297 27 270 98
I alt 8990 763 0 763 695
Figur 4. Modelleret fordeling af husdyr- og handelsgødningskvælstof på markerne i
værkstedsområdet.
27

9.2 De tre scenarier
I ARLAS projektet er gennemført tre scenarier: Maginaljordsscenariet,
Drikkevandsscenariet og Småbiotopscenariet. Alle disse scenarier vedrører ændringer
i arealanvendlsen på landbrugsjorden og de heraf følgende konsekvenser for
landbrugsproduktion, økonomi, miljø og natur.
Tabel 5. De tre scenariers ændring i arealanvendelsen (ha) på landbrugsjorden i
værkstedsområdet
Marginaljords- Drikkevands- Småbiotopscenario
scenario scenario
Marker i omdrift 1 Vedvarende græs
-1071 -832 -208
2 1071 -42 -157
Vedvarende græsbrak 0 568 168
Skov 0 305 197
1 2
) Incl. brak i omdrift. ) Incl. græsningsarealer indført i marginaljordsscenariet
28

Figur 5. Den ændrede arealanvendelse i hver af de tre scenarier
29

Figur 6. Modelleret fordeling af husdyr- og handelsgødningskvælstof på markerne i
hver af de tre scenarier
30

9.2.1 Marginaljordsscenario
I marginaljords-scenariet defineres marginaljord som alle arealer med JB 1 (+2 som
ikke findes) eller JB 11, plus alle skrånende arealer klasse 6 grader og klasse 12
grader. Alle marker med centrum i disse marginaljordsområder defineres som
marginale.
I scenariet udlægges vedvarende græsmarker (og benævnt marginaljordsgræs) i
forbindelse med disse marginaljordsarealer efter følgende beslutningsregler (Schou og
Abildtrup 2001):
1. Hvis mindre end 25% af bedriftens areal er marginaljord antages at bedriften kan
tilpasse sig uden radikale driftsomlægninger:
- Salgsafgrødeproduktionen reduceres svarende til det omlagte areal.
- Det eksisterende husdyrhold er uændret.
- Marginale arealer afgræsses med 1 ammeko med opdræt pr. 2 ha.
2. Hvis mellem 25% og 75% af bedriftens areal er marginaljord antages det at:
- Salgsafgrødeproduktionen ophører – arealet overgår til foderproduktion.
- Husdyrproduktion fortsætter (hvis bedriften har mindre end 3 DE/ha).
Ellers afvikles
den eksisterende husdyrproduktion og hele arealet udlægges som marginale
arealer
- Marginale arealer afgræsses med 1 ammeko med opdræt pr. 2 ha.
3. Hvis mere end 75% af bedriftens areal er marginaljord antages det, at hele
bedriften omlægges til ekstensiv afgræsning med 1 ammeko med opdræt pr. 2 ha.
Figur 7. Fra marginaljordsområdet lige øst for Tangeværket. Området er også er
udpeget som Særligt Følsomt Landbrugsområde (SFL). Foto: Tommy Dalgaard
(1998).
9.2.2 Drikkevandsscenario
I drikkevands-scenariet fokuseres der på to strategier for drikkevandsbeskyttelse
omfattende græsbraklægning og skovrejsning i områder, der af Viborg Amt i 2002 er
udpeget med henblik på særlig drikkevandsbeskyttelse (Nørmark 2002). De to
strategier omfatter:
31

1. Skovrejsning: Etablering af løvskov på alle landbrugsarealer, hvor
skovrejsningsområder og drikkevandsområder (OSD) er sammenfaldende.
2. Braklægning: Braklægning med flerårig græs på al øvrig landbrugsjord i OSDdrikkevandsområder
(Jf. erfaringer iflg. Wiborg 2002)
Ved de to strategier er det centrale element for bedriftstilpasningen, hvor stor en andel
af bedriftens landbrugsareal, der berøres af scenariet. Fælles for de to strategier
antages det, at udtagningen af arealerne er irreversibel, således forstået, at de fremover
ikke kan indgå i bedriftens aktiviteter, dvs. anvendes som harmoniareal mv.
Hvor det kun er en mindre del af arealet der berøres, kan det være rimeligt at antage,
at produktionen på bedriftens øvrige arealer fortsætter uændret efter en proportional
kapacitetstilpasning. Dette betyder, at husdyrholdet reduceres i forhold til andelen af
bedriftens areal, der udtages af omdrift. Det resulterende afgrødevalg, på det
resterende areal i omdrift, følger af sædskiftemodellen (Dalgaard et al. 2002a). Hvor
en større del af bedriftens areal berøres af tiltagene vil det være rimeligt at antage, at
husdyrproduktionen helt ophører.
En væsentlig forskel i forhold til marginaljordsscenariet er at de ekstensiverede
arealer i dette scenario fortsat kunne indgå i landbrugsdriften – dog som ekstensivt
drevne arealer. Dette er ikke tilfældet for drikkevandsscenariet. Ligeledes må de
forventes at landbrugsdriften helt vil ophøre ved udtagning af hovedparten af bedrifts
areal. Med dette udgangspunkt anvendes følgende beslutningsregler for
drikkevandscenariet:
1. Hvis mindre end 25% af bedriftens areal udtages, antages at bedriften kan tilpasse
sig således at det dyrkede areal og husdyrproduktionen reduceres proportionalt
med det udtagne areal.
2. Hvis mellem 25% og 75% af bedriftens areal udtages antages det at bedriften
tilpasser sig, således at husdyrproduktionen ophører, medens det resterende
landbrugsareal i omdrift anvendes til salgsafgrødeproduktion, og vedvarende
græsmarker er uændrede. Dvs. alle disse bedrifter bliver drevet som
planteavlsbedrifter.
3. Hvis mere end 75% af bedriftens areal er udtaget antages det, at
landbrugsproduktionen ophører på hele bedriften.
32

Figur 8. Fra det sydlige Område med Særlige Drikkevandsinteresser (OSD). Området
i forgrunden tilplantes med skov i drikkevandsscenariet, mens arealerne omkring
Ormstrup Sø og Ormstrup Gods (i baggrunden) udtages til græsbrak. Foto: Tommy
Dalgaard (1998).
9.2.3 Småbiotopscenario
I småbiotop-scenariet fokuseres der på udtagning af små marker (

9.3 Scenariesystemet
Scenarierne baseres konkret på et informationssystem, hvor data håndteres i GIS 1 og
sendes rundt til de forskellige simuleringsmodeller. Data returneres til GIS, hvor det
bliver konverteret til kort, som kan bruges til videre analyse og formidling.
9.3.1 Opsætning af GIS systemet
Basis for generering af scenarier er således etablering af en datamodel i GIS, som
beskriver landskabet og de elementer, der indgår heri. Vores datamodel omfatter kort
sagt 3 elementer: landbrugsbedrifter (defineret som et punktobjekt), deres tilhørende
markarealer, samt en bred samling af andre objekter, som man kan kalde ”øvrige
landskabselementer”. Denne klasse består af veje, bymæssig bebyggelse, læhegn,
skov, ferskvand, grøftekanter med mere. Alle disse 3 klasser af objekter indgår i vores
datamodel, som dermed dækker hele fladen i værkstedsområdet. Ud over at modellere
værkstedsområdet i rum, skal vi også modellere i tid, da sigtet er at belyse
konsekvenser af udviklingsprocesser som fx er afhængig af klimavariationer over tid.
Det nødvendiggør en lang række tilpasninger af data, da vores udgangspunkt er data
for et enkelt punkt i tiden, nemlig 1998. Der er hovedsagelig tre elementer i vores
datamodel, som ændrer sig over tid, nemlig sædskiftet på landbrugsarealerne,
gødskningen af disse arealer, og den øvrige arealanvendelse på ikke
landbrugsarrealer. Arealanvendelsen udenfor landbrugsarealerne ændres i scenarierne
ikke over tid, så der er ikke her et behov for at modellere en eventuel ændring i
arealanvendelse. Anderledes er det med sædskiftet og dermed gødskningen over tid.
Opbygningen af modelleringen af såvel sædskifte som gødskning er omtalt under
materialer og metoder. Tilpasningen af data, så de opnår en udstrækning i tid, udføres
ved hjælp af scripts (makroer) i Arcview, som styres fra en menu i et Arcview-projekt
(Figur 10). De forskellige scripts, som er programmeret i det objektorienterede
programmeringssprog Avenue til Arcview, er dokumenteret andetsteds (Greve 2002a;
Greve 2002b).
1 Geografisk InformationsSystem. Vi har i ARLAS anvendt Arcview 3.2a som GIS-program.
34

Figur 10. Oversigt over den menu med ArcView scripts, der er udviklet til ARLAS
scenariesystemet (Greve 2002a)
De første af disse scripts initierer blandt andet opslagstabeller med husdyrdata,
gødningsnormer og andre variable, som indgår i scenarierne. Man indtaster så her
referencen til de aktuelle datafiler, man bruger til hvert scenarie. Man skal altså forud
udarbejde korrigerede tabeller for ændringer i grunddata, når man laver scenarier. Når
alle disse procedurer, er kørt igennem, er GIS-systemet klart til at udveksle data
mellem de forskellige simuleringsmodeller.
9.3.2 Flow af data i scenariesystemet
Udvekslingen afvikles i sekvens, da resultaterne fra nogle modeller er forudsætninger
for
generering af inputdata til andre modeller (Figur 11).
35

Figur 11. Sekvenser for udveksling af informationer mellem GIS og de fem
modelsystemer, som indgår i ARLAS Scenariesystemet.
Vi vil her kort gengive proceduren for flow af data mellem GIS og
simuleringsmodellerne, idet vi følger nummereringen i Figur 11. Det første trin (1a) i
udvekslingen af data er bedriftsdata i form af et regneark udtrukket fra generelle
landbrugsregistre, som overføres til økonomiberegningsdelen. Retur leveres generelle
beslutningsregler (se materialer og metoder) for på hvilken måde driftslederen på den
enkelte bedrift responderer på de strukturelle forhold, som er angivet i grunddata (2a).
På baggrund heraf genereres i hvert scenario et korrigeret bedriftsdatasæt, hvor bl.a.
afgrødevalg og gødskning er opgjort med sædskifte og gødskningsmodellen. For
udgangssituationen udføres samme procedure, hvorved afgrødevalg og gødskning
bliver sammenlignelig med scenarierne, og på baggrund af de korrigerede bedriftsdata
beregnes værdier for bedrifts- og velfærdsøkonomi, som returneres til GIS (2b). De
korrigerede bedriftsdata konverteres dernæst til et datasæt i et særligt inputdataformat,
som igen styres via et script, som efterfølgende overføres til bedriftsmodellen
FASSET (3). Denne returnerer data på markniveau for bl.a. N-udvaskning og
afstrømning (se Hutchings 2002), som efterfølgende konverteres til kort i GIS (4).
FASSET’s tal for udvaskning og afstrømning udgør basis for generering af input til
den hydrologiske model (5). Endvidere overføres der data til biotopmodellen, som
bruger angivelser af arealanvendelsen som input (6). Faunamodellens input er
information om sædskiftet (7). Herefter er man klar til at modtage resultaterne (8, 9,
10) fra de tre sidstnævnte modeller. Disse resultater konverteres også til kort i GIS og
man er nu klar til at præsentere data eller lave yderligere tværgående analyser med
resultaterne.
36

10 Resultater og diskussion
I dette afsnit samles hovedresultaterne fra beregninger af de tre gennemgåede
scenarier. Der skelnes mellem fire forskellige dimensioner, som påvirkes i hvert
scenario: 1) Økonomi, 2) Kvælstoftab, 3) Flora og 4) Fauna. I hver dimension
demonstreres hvorledes scenariesystemet, via tilknytningen til det geografiske
informationssystem, kan anvendes til at visualisere den geografiske variation af
konsekvenserne i de opstillede scenarier. Desuden samles hovedresultaterne for hver
dimension i en tabel, og der afgives i form af plusser og minusser en subjektiv,
samlet vurdering af konsekvenserne i hvert scenario. Til slut samles vurderingerne i
en oversigtstabel og styrkerne og svaghederne ved scenariesystemet diskuteres.
10.1 Økonomi
Baseret på Schou og Abildtrup (2001), Schou og Abildtrup (2002), Schou (2002)
Tabel 6. Økonomi resultatoversigt. Ændring i kr/ha på de berørte landbrugsarealer.
Marginaljords- Drikkevands- Småbiotopscenario
scenario scenario
Berørt landbrugsareal 4400 ha 1300 ha 400 ha
Velfærdsøkonomi -5000 -2200 1
-2900 2
Driftsøkonomi -9000 -800 1
-600 2
Samlet vurdering ----- --- 0 3
1 2 3
) ved udnyttelse af EU-tilskud. ) ved udnyttelse af MVJ tilskud. ) Småbiotopscenariet er ikke gennnemregnet, men antages
ikke at have nogen stor, samlet økonomisk betydning.
Figur 12. Eksempel på den geografiske fordeling af ændringer i det økonomiske
resultat for drikkevandsscenariet ved udnyttelse af MVJ tilskud. Rød og grøn
signalerer hhv. dårligere og bedre økonomi.
37

10.2 Kvælstoftab: Bedrifts- og hydrologimodellen
Baseret på Dahl et al. (2002) og Dalgaard et al. (2002e)
Tabel 7. Kvælstoftab resultatoversigt. % ændring. N der forlader landbrugsarealet er
modelleret med FASSET. N i grundvandet lige over redoxgrænsen for hele
værkstedsområdet, N i Tyrebækken, og N i vandboringen i det sydlige OSD-område er
modelleret med den hydrogeologiske model.
Marginaljords-
Scenario
Drikkevands-
scenario
Småbiotop-
scenario
FASSET:
Nitrat fra rodzonen -25 -13
Ammoniak -30
Jordpuljen 0
Hydrogeologi:
Grundvand -11
Vand i Tyrebæk -73
Boring i OSD-område -57
Samlet vurdering ++++ 1 ++++ 0 2
1 ) Kun nitratudvaskningen fra rodzonen er opgjort for marginaljordsscenariet. 2 ) Småbiotopscenariet er ikke gennnemregnet, men
antages ikke at have nogen stor, samlet betydning for N-tabet.
Figur 13. Eksempel på den geografiske fordeling af ændringer i udvaskning fra
rodzonen og N-koncentration i grundvandsmagasinet lige over redoxgrænsen ved
indførsel af drikkevandsscenariet. Rød og grøn signalerer hhv. større og mindre
kvælstoftab.
10.3 Flora
Baseret på Munier (2002)
38

Tabel 8. Flora resultatoversigt. Ændring i udbredelse (ha) af skov, halvkultur
(kultureng og kulturgæsland) og halvnaturarealer (Kær, sandgræsland og andre
overdrev), samt ændringen i sammenhængen af disse arealer (beregnet ud fra invers
kvadratisk afstand til områder med samme plantesamfund. Dvs. en negativ ændring
indikerer mindre sammenhæng og færre arealer.
Marginaljords-
scenario
Drikkevands-
scenario
Småbiotop-
scenario
Udbredelse:
Halvkultur 347 358 -95
Halvnatur 735 174 107
Skov 0 308 199
Sammenhæng:
Halvkultur -85 284 -87
Halvnatur 266 23 2
Skov 0 1.483 487
Samlet vurdering +++ +++ +
Figur 14. Eksempel på den geografiske fordeling af summen af halvkultur- og
halvnaturarealer, der specielt indeholder de interessante plantesamfund, hhv. i
udgangssituationen og efter indførsel af marginaljordsscenariet.
10.4 Fauna
Baseret på Topping et al. (2002)
Tabel 9. Fauna resultatoversigt. % ændring af populationer af indikatorarter.
Marginaljords-
scenario
Drikkevands-
scenario
Småbiotop-
Scenario
39

Lærker -10 0 -10
Rådyr 0 100 50
Edderkopper -25 -20
1
Markmus 5 15
1
Samlet vurdering -- +++ +
1
Småbiotopscenariet er ikke beregnet for edderkopper og markmus.
Figur 15.
Eksempel på den geografiske fordeling af bestandene af rådyr og ynglende
sanglærker i hhv. udgangssituationen og i drikkevandsscenariet. De viste størelser er
gennemsnit af en enkel kørsel for en 2 gange en 11 årig klimadataserie, hvilket er et
spinklere talmateriale end det, der ligger bag Tabel 9 og opgørelserne i Topping et al.
(2002).
40

10.5 Resultatoversigt
Tabel 10. Samlet resultatoversigt
Marginaljords- Drikkevands- Småbiotop-
Scenario Scenario scenario
Økonomi ----- --- 0
Kvælstoftab ++++ ++++ 0
Flora +++ +++ +
Fauna -- +++ +
10.6 Diskussion
Det gennemgåede scenariesystems styrke er, at det simultant kan levere økonomiske,
miljømæssige og naturmæssig konsekvenser af ændringer i arealanvendelsen og
landbrugsproduktionen, visualisere disse konsekvenser på kort, samt danne basis for
at analysere geografisk relaterede vekselvirkninger mellem disse dimensioner.
Inden for hver af de tre aspekter – økonomi, miljø og natur - har processen med at
konstruere systemet givet anledning til væsentlige nyudviklinger af
simuleringsmodeller. Som sådan er der masser af muligheder i scenariesystemet. Der
er dog stadigvæk et stykke vej at gå, før man står med et værktøj, som er ligeså
praktisk relevant, som det er teoretisk interessant.
En vis afstand mellem teoretisk interesse og praktisk relevans viser sig konkret i den
aktuelle brug af scenariesystemet. De her opstillede scenarier har alle det til fælles, at
de ikke er formuleret i en praktisk sammenhæng, hvor man står over for en
problematisk situation og skal have afklaret sine handlemuligheder. Scenarierne er
mest valgt for at afspejle nogle dynamikker som de enkelte modeller er velegnet til at
fremstille. Der er således et mestendels ”forskningsinternt” eller teoretisk perspektiv i
de tre scenarier. En oplagt videre udviklingsproces for scenariesystemet var at vende
dette perspektiv om og tage udgangspunkt i en praktisk sammenhæng, hvor systemets
resultater i højere grad var rettet mod afklaring af usikkerheder og konsekvenser for
nogle givne ”klienter”.
At scenarierne ikke er problemorienterede, viser sig for eksempel ved at der i alle tre
scenarier er negative økonomiske resultater for landbruget. Man kunne derfor måske
konkludere, at natur og økonomi udelukker hinanden, hvilket absolut ikke behøver at
være tilfældet. Det berører en central diskussion om, hvilken form for landbrug der er
det ”gode” landbrug, set i et helhedsperspektiv. De tre scenarier her er et skridt på
vejen mod en vurdering af konsekvenser i et helhedsperspektiv, men det ville forøge
den praktiske værdi af scenarierne væsentligt, hvis de i højere grad blev målrettet mod
at afklare praktiske problemer. Det er dog et væsentligt skridt at disse forskellige
aspekter kan betragtes i rammerne af det samme informationssystem.
Omkring det praktiske perspektiv, bør man også huske på at systemet i sin nuværende
form er meget datatungt og arbejdskrævende, noget som blandt andet er relateret til
41

detaljeringsgraden af inputdata. Dette ville i de fleste anvendelsessammenhænge være
svært at realisere, eftersom man ikke kan bruge den samme tid på datagrundlaget som
i ARLAS. En styrke ved scenariesystemet er dog, at man kan bruge forskellige
modeller og koble dem sammen med GIS. For eksempel var det oplagt at bruge
”lettere” simuleringsmodeller, som i højere grad kunne bruges i en praktisk
sammenhæng. Udvikling af sådanne light-udgaver af simulerings-modellerne og
ARLAS scenariosystemet kunne være et mål for fremtiden. I den forbindelse bør det
også være et mål at udviklingen sker med henblik på afdækning af konkrete behov –
fx hos statslige, amtslige og kommunale myndigheder eller hos landbruget og dets
organisationer.
11 Referencer
Caspersen OH og Jensen FS (2002) Fra udmark til rekreativ ressource. Papir til
ARLAS Workshop 3.-4. december 2002.
Dahl M, Per Rasmussen, Lisbeth Flindt Jørgensen, Vibeke Ernstsen, Frants von
Planten-Hallermund og Stig Schack Pedersen (2002) Effekter af ændret
arealanvendelse for nitratindhold i grundvand og overfladevand - beskrevet ved
scenariestudier. Papir til ARLAS Workshop 3.-4. december 2002.
Dalgaard T (2000) Notat vedrørende sædskifter for bedriftstyperne i ARLAS.
Danmarks JordbrugsForskning, Foulum.
Dalgaard T, Christen Duus Børgesen, Mette Balslev Greve, Tove Heidmann, Nick
Hutchings, Jørgen F. Hansen, Chris Kjeldsen, Jan Nyholm, Birgit Møller Rasmussen
(2002a): Arealanvendelsen i værkstedsområdet. Danmarks JordbrugsForskning 9/2.
Dalgaard T, Christen Duus Børgesen, Mette Balslev Greve, Tove Heidmann, Nick
Hutchings, Jørgen F. Hansen, Chris Kjeldsen, Jan Nyholm, Birgit Møller Rasmussen
(2002b): Husdyrhold, gødningsproduktion og gødningsfordeling i værkstedsområdet.
Danmarks JordbrugsForskning 9/2.
Dalgaard T, Christen Duus Børgesen, Mette Balslev Greve, Tove Heidmann, Nick
Hutchings, Jørgen F. Hansen, Chris Kjeldsen, Jan Nyholm, Birgit Møller Rasmussen
(2002c): Marginaljords-scenariet. Danmarks JordbrugsForskning 10/5.
Dalgaard T, Christen Duus Børgesen, Mette Balslev Greve, Tove Heidmann, Nick
Hutchings, Jørgen F. Hansen, Chris Kjeldsen, Jan Nyholm, Birgit Møller Rasmussen,
Jesper S. Schou (2002d): Drikkevands-scenariet. Danmarks JordbrugsForskning 21/6.
Dalgaard et al. (2002e) Vandbeskyttelse. Papir til ARLAS Workshop 3.-4. december
2002.
Dalgaard T og Rygnestad H (2000) Intern arbejdsrapport vedrørende klassificering
og geografisk kortlægning af bedriftstyper. Danmarks JordbrugsForskning.
42

Greve MB (2002a) Scripts I ARLAS udgangssituationen. Danmarks
JordbrugsForskning.
Greve MB (2002b) Scripts I ARLAS Marginaljordsapplikationen. Danmarks
JordbrugsForskning.
Hutchings NJ (2002) Modellering af N-tab fra plante-, svine- og kvægbedrifter. Papir
til ARLAS Workshop 3.-4. december 2002.
Kjeldsen (2000) Predicting crop allocation on field scale. Proceedings, GIS 2000 -
14 th Annual Conference on Geographical Information Systems, March 13-16, 2000,
Metro Toronto Convention Centre, Toronto, Ontario, Canada
Münier B (2002) Biotop-modellering af 4 scenarier. Papir til ARLAS Workshop 3.-4.
december 2002.
Nørmark P (2002) Mulige kriterier for Drikkevandsscenario. Notat fra Viborg Amt
den 13. Maj.
Odderskær P (2002) Konsekvenser for sanglærken ved omlægning til økologisk jordbrug. In:
Langer et al. FØJO rapport nr. 12. ISSN 1398-716X.
Rygnestad H, Jensen JD & Dalgaard T (2000) Målrettede eller generelle politiske
virkemidler? Økonomiske analyser i geografisk perspektiv. Series WP 17/00, 48 pp.
ISSN 1398-4896.
Schou JS og Abildtrup J (2001) Økonomiske konsekvenser ved omlægning af
marginaljorder til vedvarende græs – metoder og resultater. ARLAS-notat 22. maj.
Danmarks Miljøundersøgelser, Roskilde og Fødevareøkonomisk Institut,
Frederiksberg.
Schou JS (2002) Økonomisk analyse af skovrejsning og braklægning som strategier til
drikkevandsbeskyttelse. DMU rapport in prep. Danmarks Miljøundersøgelser,
Roskilde
Schou JS og Abildtrup J (2002) Miljøøkonomiske analyser af scenarier for
landbrugets arealanvendelse. Papir til ARLAS Workshop 3.-4. december 2002.
Topping C, Odderskær P og Jepsen JU (2002) Effekten af landskabsændringer på den
vilde fauna belyst ved modelscenarier. Papir til ARLAS Workshop 3.-4. december
2002.
Torp S (2002) Jordbundskortlægning i værkstedsområdet i Bjerringbro/Hvorslev.
Papir til ARLAS Workshop 3.-4. december 2002.
Wiborg IA (2002) Nitratfølsomme indvindingsområder og aftaler om
drikkevandsbeskyttelse. Notat fra Landbrugets Rådgivningscenter 1. Maj.
43