16th IFOAM Organic World Congress, Modena, Italy, June 16-20, 2008
Archived at http://orgprints.org/12542
Evaluation of Farm Biodiversity with Indicators in the
Context of Sustainability
Siebrecht, N. & Hülsbergen, K.-J. 1
Key words: Biodiversity, Assessment, Indicators, Sustainability, Organic Farming
Abstract
Organic farming depends on the promotion of biodiversity and the corresponding
functions. No tools are known for the obtention of farm-specific information explaining
the influence of farm management on biodiversity. The paper describes an approach
that allows estimating such effects. It has been applied in an experimental farm with
an organic and a conventional farm section. The results distinguish between both
sections. The investigations made so far allow concluding that multiple-structured lowinput systems achieve better marks than specialized high-input systems. For further
development and validation additional studies are required. It is planned to test the
indicator model in numerous farms, in order to disclose bottlenecks and deficiencies.
Introduction
Conservation and promotion of biological diversity belong to the principles of organic
farming. The guidelines of IFOAM include the demand to protect organisms,
communities and ecosystems with the aim to safeguard the ecological balance. One
of the targets defined in EC Regulation 834/2007 is the maintenance of a rich
biodiversity by cautious farm management. The reason for this high esteem is the
importance of biodiversity for the functioning of agro-ecosystems. Organic farming
derives benefits from the natural regulation of harmful organisms and the support of
mass cycles.
The influence of organic farming is mostly positive and only in rare cases indifferent or
negative (e.g. Bengston et al. 2005, Hole et al. 2005). Divergent statements were
sometimes caused by the choice of species or species groups. Such state indicators
(e.g. target organism) are not suitable for integration into indicator models oriented on
estimating the environmental impact by farm enterprises. What we need are methods
rating the consequences for biodiversity on the basis of management data and derived
pressure indicators. Such an approach, pursued in an experimental farm for years, is
the focus of this paper.
Materials and Methods
The elaborated approach has been integrated into the indicator model REPRO
(Hülsbergen 2003) following the existing conception: Potential environmental effects of
farm enterprises are analyzed and estimated on the basis of management data and
1
Chair of Organic Farming, Technical University Munich, Alte Akademie 12, 85350 Freising,
Germany, E-Mail: norman.siebrecht@wzw.tum.de, Internet www.wzw.tum.de/oekolandbau
16th IFOAM Organic World Congress, Modena, Italy, June 16-20, 2008
Archived at http://orgprints.org/12542
site specifics. In the past, relevant analyses were oriented on the abiotic environment;
statements on the biodiversity were missing almost completely. The new approach
considers the complex relationships of farm management in form of structural features
(acreage and cropping structure), input parameters (fertilizer and pesticide input) as
well as specifics of process design. For this purpose, partial indicators (PI) have been
sampled, evaluated and finally aggregated to get the complex indicator Biodiversity
Development Potential (BDP) (Fig. 1). The analysis of the PI is made using evaluation
functions which convert the indicator value into a dimensionless figure. Value 1 stands
for the best, 0 for the worst effect on the environment (see Fig 2).
Structures
Inputs
landuse and crop
diversity
proportion of farmland
without pesticide use
W
landuse diversity
W
crop group diversity
W
crop diversity
W
varietal diversity
E/W
Measures
pesticide treatment index
intensity of fertilization
field size
E/W
field perimeter
E/W
variation coefficient field
size[autom.]
E/W
E/W
diversity of tillage
operations
E/W
E/W
diversity of harvest
operations
E/W
E/W
frequency of utilization
frequency of vehicle
crossing
E/W
E/W
Aggregation
Biodiversity Development Potential (BDP)
Figure 1: Scheme of partial indicators and aggregation to the BDP; E/W =
Evaluation and Weighting
The principle of analysis and evaluation has been described on the example of land
use and crop diversity. This PI is a derivative of the crop diversity: Land use and
cropping structure are grouped on hierarchic levels. The first level describes types of
land use (arable land, fallow areas, ..), which are then broken down to crop groups
(cereals, root crops, ..), species and varieties. On each level, calculations of the
Shannon Index (SHI) are made. The SHI values of the levels are aggregated with
weighting factors and merge in an overall indicator which is then evaluated (Fig. 2).
The described method has been applied in commercial and experimental farms,
followed by tests of its practicability. Below, results of its use in the Experimental Farm
Scheyern in the south of Germany are given. The farm has concentrated on
investigations in agro-ecosystems since 1990. In 1992, it was subdivided into an
organically (OF) and a conventionally (CF) run section.
Results and Discussion
The indicator approach allows differentiating between the two farm sections (Fig. 3).
Considering the BDP overall index, OF was rated 0.74, CF 0.41. The result reflects
the influence of higher land use and crop diversity (more differentiated cropping
structure and catch crops) and the omission of mineral fertilizers and plant protection
agents. With regard to the PIs, field size and field circumference, which serve the
estimation of field structures, OF ranked slightly behind CF. This can be explained by
the fact that, compared to CF areas, OF fields are smaller (1.3 ha on average) and of
16th IFOAM Organic World Congress, Modena, Italy, June 16-20, 2008
Archived at http://orgprints.org/12542
very compact shape (nearly square). The investigations made so far allow concluding
that many-sided structured low-input systems, which often are under organic
management, achieve better marks than specialized high-input systems. Due to its
sensitivity to management measures, the indicator BDP permits a detailed
differentiation between farm types. Intensively run or specialized ecofarms, marked by
a closer crop rotation and higher intensity of fertilization, are rated lower.
Analysis
Landuse and crop diversity
Evaluation
(indicator value)
Farm area
(Landuse)
SHI
Arable land
(Crop group) Cereals
0,99
Grassland
Row crops
(Crops)
Winter wheat
(Varieties)
Bussard
…
…
0,6
…
0,9
Oat
…
…
2,2
Capo
…
…
0,4
x 0,4
x 0,3
x 0,2
x 0,1
Figure 2: Scheme of the PI land use und crop diversity
The farm-internal sensitivity of the indicator becomes evident when several years are
compared (Fig. 4). Variations of the indicator value in CF go back to changes in the
cropping structure and the intensity of fertilization and plant protection. OF shows a
comparatively constant situation of the cropping structure throughout the reference
period; variations are the result of changes in fertilization intensity and use frequency.
Especially conspicuous is the pronounced differentiation of the two farm sections after
the shift to organic management. The OF areas received clearly higher overall ratings
due to the changes in management.
Land use and crop
diversity
CF (BDP: 0.74)
OF (BDP: 0.41)
1,00
Frequenzy of vehicle
crossing
0,90
Field perimeter
0,80
0,70
0,60
0,50
Frequenzy of utilization
Field size
0,40
0,30
0,20
0,10
0,00
Diversity of harvest
operations
Variation coefficient
field size
Diversity of tillage
operations
Intensity of fertilization
Proportion of farmland
without pesticide use
Pesticide treatment
index
Figure 3: Evaluation of PI compared between OF and CF
16th IFOAM Organic World Congress, Modena, Italy, June 16-20, 2008
Archived at http://orgprints.org/12542
Conclusions
The approach produces farm-related analyses of the influence on the biodiversity
without extensive surveying or mapping. The method is oriented on high transparency
tracing back PIs, algorithms and evaluation functions. The required input data are
available in commercial farms. The integration into an indicator model allows widespread applications and guarantees a high practicability. However, the indicator
cannot provide information on the occurrence of species because the development
potential of biodiversity is only estimated. It may be fuzzy due to site conditions,
temporal delay, deviating spatial consideration or the structure of the entire
farmscape.
1,0
CF
OF
0,8
0,6
0,4
0,2
1991
1993
1995
1997
1999
2001
2003
2005
Figure 4: Development of the BDP for CF and OF (1991 – 1994)
The introduced indicator method has been coordinated and discussed in an expert
commission. Additional studies are required for further development and validation,
because the latter is not yet sufficiently documented. However, preliminary results
obtained in the Experimental Farm Scheyern indicate that the development tendencies
for groups of species (e.g. weed flora) coincide with those of the BDP (Osinski et al.
2005). It is planned to test the indicator model in numerous farms, in order to disclose
any bottlenecks and deficiencies. It should be discussed whether further farm
parameters and criteria have to be integrated as PI.
References
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