Abstract
Measuring the effectiveness of agri-environment schemes depends on
scheme type, taxon and landscape. Here we show how spatial scale, i.e.
studied transect, field or farm level, and controlling for yield loss,
can drastically change the evaluation of biodiversity benefits of
on-field (organic farming) vs. off-field (flower strips) schemes.
Transects may lead to misleading evaluations, because flower strips,
covering only 5% of conventional fields, support less bees than large
organic fields; but if their 20% yield loss is considered to compare
identical yield levels, 80 ha conventional plus 20 ha flower strip
farming promotes more bees than 100 ha organic farming.
There is a decades-long discussion on how landscape may be designed
delivering high agricultural productivity and biodiversity conservation
alike (Zhang et al. 2007; Landis 2017). To address these
challenges, a variety of agri-environment schemes (AES) have been
introduced (Batáry et al. 2015). The AES exhibit a positive
effect on species richness and abundance of farmland biota, but these
effects depend on taxonomic group, landscape structure and ecological
contrast between the treated and the control site (Batáry et al.2015; Marja et al. 2019). Recently, (Batáry et al. 2015)
reviewed the broad range of European AES with a meta-analysis and
compared their relative contributions to biodiversity conservation. AES
approaches can focus on non-productive areas, such as field boundaries
and wildflower strips (off-field practices (Garibaldi et al.2014)), or productive areas, such as arable crops or grasslands
(on-field practices). Schemes promoting off-field areas include
hedgerows (often for bird conservation (Batáry et al. 2012)),
sown or naturally regenerated field margins (often flower strips for
pollinators (Pywell et al. 2012)) or simply taking land out of
production (e.g. abandoned land for great bustard conservation in
Hungary (Kovács-Hostyánszki et al. 2011)). In contrast, on-field
practices support environmentally sensitive approaches to the management
of land that is used to grow crops or feed livestock. For example, they
might reduce or prohibit the use of agrochemicals or confine management
such as mowing grassland within specified points in time. The most
widespread on-field scheme is organic farming (Reganold & Wachter 2016;
Seufert & Ramankutty 2017). Batáry et al. (2015a) found that
off-field schemes were much more effective at enhancing species richness
than on-field schemes. The conversion of crop monocultures to
semi-natural habitat results in a much larger increase in resource
availability (i.e. creates a larger ecological contrast to the untreated
control) for a wider range of species than on-field schemes, such as
reducing stocking rates or restricting fertilizer and pesticide
application in organic farming (Marja et al. 2019). Furthermore,
schemes promoting the establishment of wildflower strips might be better
targeted to the conservation of a given species group than on-field
schemes, because they often specifically address a resource that is
limiting population growth or size, e.g. floral resources for flower
visiting insects (Warzecha et al. 2018).
However, the meta-analysis by Batáry et al. (2015a) has
limitations in the comparison of off- vs. on-field practices, as it
combines very different studies, which refer to biodiversity gains at
very different spatial scales. For example, insect and plant surveys
cover typically only a minor part of a study field (e.g. the field
margin) without considering upscaling the effect size to the whole field
or farm. Further, biodiversity-yield trade-offs have not been
considered. Gabriel et al. (2013) showed in a large scale UK
study that arthropod diversity did not differ between organic and
conventional cereal fields when controlling for the more than 50% yield
loss in organic farming. Here, we focus on different spatial scales of
two most popular AESs (Batáry et al. 2015), namely organic
farming as on-field measure and planted flower strips as off-field
measure, leading to contrasting assessments of their biodiversity value.
In general, organic farming is applied at farm scale, and organic
farmers do not apply for flowering strip (FS) schemes. Hence, FS as an
AES is typically used by conventional farmers. FS are usually sown with
seed mixtures of wild flowers and/or flowering crop species on arable
land along field boundaries (Marshall & Moonen 2002; Warzecha et
al. 2018). The width, the species mixtures and the management of the
strips vary between countries and even between states. FS are most often
targeted for insect conservation, especially favouring flower visitors
to ensure crop pollination and natural enemies contributing to
biological pest control (Wratten et al. 2012; Blaauw & Isaacs
2014; Tschumi et al. 2015). Haaland et al. (2011) found in
their review that sown wild FS support higher insect abundances and
diversity than cropped habitats.
In this study, we illustrate four different scenarios of
scale-dependency of these agri-environment schemes by using wild bee
data of three types of surveys (organic wheat field, conventional wheat
field and conventional wheat fields with FS) from ten landscapes
replicated in two years (methods: Supplement 1). We investigated,
whether the effectiveness of the two AES (relative to the control, i.e.
conventional fields) depends on the spatial scale considered. We
supposed that scaling up the transect level data to field or farm level,
by considering their larger contribution due to their larger area, and
controlling for yield loss, might significantly change the whole
picture.
Tuck et al. (2014) found in their meta-analysis that organic
management supports 30% higher species richness per 1 ha field than
conventional management. Flower strips adjacent to conventional fields
are often found to be even more species rich than organic fields without
such strips. Batáry et al. (2015) quantified this in their
meta-analysis in that off-field practices (often flower strips) were
more effective measures in maintaining or restoring biodiversity than
measures on productive areas, such as organic farming on arable land or
grassland; effect size of off-field practices was about two times high
than that of on-field practices. In this scenario, comparisons consider
the transect level, i.e. sampling of pollinator data at the transect
level of organic vs. conventional vs. FS (adjacent to conventional
fields) (Geppert et al. 2020), exhibiting an eight times higher
effectiveness of FS than organic management (Fig. 1a).
The second scenario focuses on the field level, considering the area
share of sown flowers in case of FS fields (Fig. 1b). When FS occupy
15% of a conventional field, which was the situation in our study
(Geppert et al. 2020), the effectiveness of conventional
management with FS was still 43% higher than the effectiveness of
organic management (compared to conventional fields without FS). Hence,
the difference at field level is much less expressed than in the
transect scenario.
In the third scenario, we further scaled up pollinator abundance data to
farm level with farm size of 100 ha. In case of conventional farming
with FS, this extrapolation of field to a 100 ha farm level considered
5% area taken out for FS. We took 5% FS, as it corresponds to the
minimum area of the greening measure of the Common Agricultural Policy
(Zinngrebe et al. 2017). We found that 100 ha organic farming,
which is usually characterized by a much higher cover of flowering weeds
than conventional fields (Batáry et al. 2013), was about twice
more effective than 100 ha conventional farming including FS in
supporting pollinator abundance. This is because organic management
promotes bee abundance with a 20 times larger area than the small area
(5 ha) of flower strips. Holzschuh et al. (2008) showed that
increasing the area with organic farms per landscape from 5% to 50%
triples the number of bee species on surrounding fallows.
The last scenario controls for yield loss in organic compared to
conventional farming (Gabriel et al. 2013). As productivity of
organic farms is, on average, 20% lower worldwide (Seufert &
Ramankutty 2017), 100 ha organically managed farm may be compared with
80 ha conventional farm with 20 ha flower strips, thereby producing
equal crop yield. In this situation, the same yield per 100 ha farm is
the target, and we found that organic farming supported about 40% fewer
pollinators due to the large area of flowering strips/fields allowed in
conventional farming. When organic yield is even halved (as in case of
cereals (Gabriel et al. 2013; Batáry et al. 2017)), the
difference can be even much higher. Finally, one might consider further
scenarios that we could not test with our data. For example, if organic
farmers manage their farms with higher crop diversity and longer crop
rotations than conventional farmers, biodiversity might further increase
(Sirami et al. 2019).
A plethora of studies addresses the ecological effectiveness of
different agri-environment schemes with nearly all of them focusing
exclusively on the transect level (Batáry et al. 2015), whereas
upscaling to higher spatial scale (field or farm level) is rare (Batáryet al. 2017). Although small-scale off-field measure can have a
very positive biodiversity outcome at that scale, such as in case of
flower strips, upscaling to field and farm level can reveal that the
biodiversity benefit of FS is on par or even lower than that of on-field
measures such as organic farming (Geppert et al. 2020). Focusing
studies on the transect scale can give misleading results, as FS make up
typically only ca. 5% of a conventional farm, enhancing less bees than
a same sized organic farm. This, can be turned around again, when we
control for yield losses from organic farming (Chave 2013). As yield in
organic farming is on average 20% lower, 100 ha organic farm has the
same productivity as 80 ha conventional farm with 20 ha flower strips,
which supports much higher biodiversity than organic farming. In
conclusion, considering various scales in the evaluation of AES measures
is necessary in order to get a balanced understanding of their
ecological and also economic effects for further development of their
effectiveness.