Introduction
Global food systems are under increasing pressure to produce sufficient
food for a growing human population
(Godfray et al. 2010). Agriculture
has long aimed to address this challenge by maximising crop yields
(Curtis & Halford 2014;
Mitchell & Sheehy 2018). However, the
intensive approaches used to achieve this over much of the globe have
been linked to severe declines in biodiversity
(Green et al. 2005;
Reidsma et al. 2006;
Butler et al. 2007) and many other
adverse environmental impacts (Skinneret al. 1997; Tilman et al.2002; Tsiafouli et al. 2015).
Simultaneously, crop yields have plateaued in many systems, suggesting
that new approaches are required to support future increases
(Ray et al. 2012).
Sustainable intensification aims to increase agricultural productivity,
whilst also maintaining or bolstering biodiversity
(Cassman 1999;
Bommarco et al. 2013;
Garnett et al. 2013;
Kleijn et al. 2019). This approach
has been driven in part by increasing awareness that biodiversity
provides key ecosystem services required to maintain the long-term
viability of agricultural systems
(Bommarco et al. 2013), including
pollination and natural control of pests
(Naylor & Ehrlich 1997;
Kremen & Chaplin-Kramer 2007). If
sustainable intensification is to achieve its goals we need detailed
knowledge on how to manage agricultural landscapes to ensure optimal
provision of these services in the long-term
(Gagic et al. 2017;
Kleijn et al. 2019). Landscape
structure (here defined as the composition and configuration of
different land cover types) has been repeatedly identified as a key
driver of ecosystem service delivery. However, whilst many studies have
demonstrated relationships between the amount and configuration of
natural or semi-natural habitats and abundance or species richness of
beneficial invertebrate communities
(Chaplin-Kramer et al. 2011;
Blitzer et al. 2012;
Potts et al. 2016;
Woodcock et al. 2016) and service
indicators such as crop pest populations
(Bianchi et al. 2006;
Chaplin-Kramer et al. 2011;
Thies et al. 2011;
Rusch et al. 2013;
Dainese et al. 2019;
Haan et al. 2019), very few have
directly examined effects on crop yield
(Holland et al. 2016;
Holland et al. 2017;
Karp et al. 2018). Of those that
do (e.g. Martin et al. 2016;
Martin et al. 2019), most focus on
absolute measurements of crop yield averaged over short periods of time.
However, average yields are not necessarily indicative of a systems’
long-term sustainability or ‘resilience’.
Holling (1973) defined resilience as a
“measure of the persistence of systems and of their ability to absorb
change and disturbance”. Whilst there has been considerable debate over
the use of resilience concepts in ecological systems
(Carpenter et al. 2001;
Klein et al. 2003;
Myers-Smith et al. 2012;
Standish et al. 2014;
Béné et al. 2016), the guiding
principle is to consider not just the absolute quantity of a single
function (e.g. crop yield), but also to consider this function in terms
of its ability to persist over time by resisting and recovering from
perturbations (Oliver et al.2015). In the case of crop yield, such perturbations may come in the
form of extreme weather events, or outbreaks of pests or diseases. These
can have substantial impacts on producer livelihoods even if average
yields are high (GFS 2015). Resilience is
underpinned by the environmental processes supporting agricultural
production, e.g. climate, soil health, abundance and diversity of
beneficial organisms. The need to identify and develop resilient
cropping systems as a key component of sustainable intensification has
been embraced in both research (Fischeret al. 2006; Altieri et al.2015; Bullock et al. 2017) and
policy (Defra 2011,
2018b, a),
but the question of how landscapes and the ecosystem services they
deliver affect the resilience of agricultural systems (particularly in
terms of the stability of crop yields over time) remains as a key
knowledge gap preventing the widespread uptake of sustainable
intensification {Kleijn, 2019 #1}.
In this paper we explore the relationships between landscape structure
and crop yield resilience. We used a ten-year time series of wheat
yields from a national survey of farms in England to derive metrics
relating to different aspects of resilience. We analysed relationships
between these metrics and aspects of landscape structure known to affect
the provision of biodiversity-mediated ecosystem services. We
hypothesised that:
- All metrics of resilience would show a positive relationship with area
and connectivity of arable land, given the known higher yields in
intensively arable areas of England
- All metrics would, accounting for the above, show a positive
relationship with the amount of semi-natural habitat acting as a
reservoir of beneficial organisms providing ecosystem services
- Resilience metrics would differ in the strength of these relationships
given the different time-scales over which they are derived
Methods
Yield data from a national survey
Wheat yield data were obtained from Defra’s cereals and oilseeds
production survey, part of the annual June survey of agriculture and
horticulture in England (Defra 2018c).
The latter collects detailed information about the structure of the
agricultural industry, as is compulsory under UK legislation
(Agricultural Statistics Act 1979). The survey uses a stratified random
sampling approach in which farm holdings are classified on the basis of
size defined by the theoretical labour requirement to run the farm. Full
details of the survey methodology can be found in
Defra (2018c). Data were available for
2008 to 2017, giving 10 years of data on average winter wheat yield per
farm and coordinates giving the location of each farm to 1km. Locations
were mostly obtained from subsidy claims, where farms register the
location of the land for which they are claiming payment. However, in a
minority of cases (<1%) location was inferred from farm
postcode (Defra 2018c).
Data were cleaned to remove data flagged by Defra as potentially
erroneous, as well as missing and zero yield values and land used to
produce whole-crop silage harvests. Zero values may be informative where
they indicate crop failure pre-harvest, however, they may also arise
from a lack of measurement and so cannot be used with confidence.
Following cleaning, a total of around 22,000 samples were available
across the ten years (mean 2204 per year).
Because a new random sample of farms is drawn each year, few locations
had consecutive data across the 10 years. In order to analyse yield
variation over time and to ensure anonymity of the farm returns we
aggregated yield data to the 10km x 10km grid cell (‘hectad’). This
gives an average yield per year per hectad, accounting for local spatial
variation between farms and farming practices. From this dataset,
hectads were identified with sufficient samples per year across the time
series for analysis of resilience (see Supplementary Material Appendix 1
for details). All data handling and analysis was performed in R
(v3.4, R Core Team 2017).
Constructing metrics of resilience
Recent functional approaches have proposed that resilience of a system
can be estimated by looking directly at patterns of temporal and spatial
variability of the function which the system delivers and its response
to known perturbations (Oliver et
al. 2015). This approach is well-suited to cropping systems as the
function (i.e. yield) is easy to measure and widely surveyed. Some
studies have begun to explore links between environmental drivers and
aspects of yield resilience (Di Falco &
Chavas 2008; Gaudin et al. 2015;
Iizumi & Ramankutty 2016). However, such
studies often focus on only a single metric of resilience. Given the
complexity of resilience as a concept
(Donohue et al. 2016;
Ingrisch & Bahn 2018;
Kéfi et al. 2019), with multiple
facets derived from the absorptive (resistance), adaptive (recovery) and
transformative (reorganisation) capacities of the system
(Béné et al. 2016;
Ingrisch & Bahn 2018), reductions to a
single metric may be insufficient to understand the effects of landscape
structure on resilience of crop yields
(Isbell et al. 2015). For every
hectad with sufficient data, we calculated three metrics capturing
different aspects of resilience:
- Relative function across the time series . Average difference
between per hectad annual yield and average national annual yield
(Fig. 1A). This combines the average magnitude and variability of
yield over the time series, taking account of surpluses (when per
hectad yield exceeds national average yield) and deficits (vice
versa ), in line with the functional resilience metric proposed by
Oliver et al. (2015).
- Yield stability around a moving average . Inverse of absolute
percentage difference between yield in any one year and the average
yield over the years either side (Fig. 1B), averaged across the time
series (Iizumi & Ramankutty 2016).
This metric is sensitive to fluctuation of yield over shorter
timescales and incorporates aspects of resistance and recovery.
- Resistance to a specific event . Exceptionally heavy spring
and summer rainfall in 2012 caused a variety of issues for UK wheat
farming (Defra 2012;
Impey 2012), resulting in a mean
national 14% drop in yield from the previous four years (as
calculated from survey data). To understand resistance to this event
we quantified the inverse of the proportional drop in yield shown in
2012 from the pre-2012 mean (Fig. 1C). This metric focuses on the
resistance of crop yield to specific, short-term perturbation.
All three metrics were calculated in such a way that larger values imply
greater resilience (i.e. the use of inverse values). We did not use
coefficient of variation, because Carnuset al. (2014) found it to be a potentially poor metric for
exploring relationships between biodiversity and stability of ecosystem
functions, despite its frequent use as such
(e.g. Piepho 1998;
Hautier et al. 2015;
Ray et al. 2015).
Accounting for climate and soil effects
In order to explore how our metrics of yield resilience are influenced
by landscape structure, we first sought to control for other potentially
confounding variables. These included spatial variation in
meteorological and soil variables. The way in which these influence crop
yields is complex, depending on interactions between temperature,
sunlight and rainfall and the growth stage of the crop. We therefore
condensed these variables into a single metric of potential yield. We
used a simple model to estimate potential yield from temperature,
precipitation and solar radiation
(Agri4Cast data, Biavetti et al.2014) and soil water holding capacity
(Bell et al. 2018), based on the
approaches of Sylvester-Bradley and
Kindred (2014) and Lynch et al.(2017), and benchmark values for wheat in
Sylvester-Bradley et al. (2015).
The model has three main stages: 1) estimation of green area index as a
function of accumulated growing degree days, 2) estimation of
intercepted solar radiation and water-limited conversion to biomass 3)
apportioning of accumulated biomass to grain yield. The model also
accounts for vernalisation (Spink et
al. 2000), drought and waterlogging
(Olgun et al. 2008). A full
description of the potential yield model is available in Supplementary
Material, Appendix 2. For each of the three resilience metrics, the
equivalent metric for potential yield was included as a covariate in
statistical models (see section 2.5) to account for climatic and soil
effects on yield. We also accounted for any further impacts of regional
variation in soils and climate beyond those explicitly in the potential
yield model by assigning each hectad to an environmental zone, using a
pre-existing classification (Bunceet al. 2007), which we then included as a random effect in
statistical models (see section 2.5).
Landscape composition and configuration
We used a satellite-derived UK land cover map
(LCM2015, 25m raster version, Rowlandet al. 2017) to determine the composition and configuration of
land cover types within each hectad. We analysed three main land cover
classes: arable land, semi-natural habitats and semi-natural grasslands.
The first of these allowed us to test our first hypothesis. We refined
the LCM2015 arable class by intersecting with mapped high-grade
agricultural land (Natural England 2012),
as the resultant class (‘high-grade arable land’) showed a higher
correlation with mean yield over time in preliminary analyses than total
area of arable land alone (Pearson’s correlation; r =0.35, p
<0.001; r =0.31, p =0.001, respectively, n =135 in both
cases). Semi-natural habitats included broadleaf woodland, semi-natural
grassland, heathland and wetland as these are known to affect sources
and flows of ecosystem services which affect crop production
(Tscharntke et al. 2005;
Rand et al. 2006;
Blitzer et al. 2012;
Rusch et al. 2013;
Holland et al. 2017;
Martin et al. 2019). We analysed
semi-natural grasslands separately as these are structurally more
similar to arable land and may be especially important in the provision
of ecosystem services to agricultural landscapes
(Duflot et al. 2015;
Bengtsson et al. 2019).
For each land cover class we calculated three metrics of landscape
composition and configuration. These were: percentage area per hectad,
edge:area index (a measure of fragmentation) and mean distance to the
nearest patch (a metric of isolation). Calculations were made in ArcGIS
(v10.4, ESRI, CA). These three metrics are widely used in studies
assessing the impacts of landscape structure on ecological processes
(Chaplin-Kramer et al. 2011;
Haan et al. 2019;
Martin et al. 2019) and have been
demonstrated to capture much of the potential variation in landscape
composition and configuration (Riitterset al. 1995). We did not analyse land cover diversity, as
preliminary analysis showed this to be driven (at the hectad scale)
largely by the presence of other land cover types (e.g. urban,
coniferous woodland, improved grassland).
Statistical analysis and modelling
All statistical analyses were undertaken in R
(v3.4.0 - 3.5.3, R Core Team 2017). We
used linear mixed models constructed in the nlme package
(Pinheiro et al. 2017). All models
included a spherical spatial autocorrelation structure, which
preliminary analyses found to increase model fit as determined by
Akaike’s Information Criterion adjusted for small sample sizes (AICc).
For each resilience metric (relative function, yield stability,
resistance) we constructed a global model containing the random effect
of environmental zone and all other explanatory variables as fixed
effects (i.e. cover, fragmentation and isolation of each of high-grade
arable, semi-natural habitats and semi-natural grasslands, plus
potential yield from climate and soil data). To identify the ‘best’
performing subset of explanatory variables from the global model we ran
all possible subsets using the MuMIn package
(Barton 2016) and ranked models using
AICc. Models were constrained to contain the intercept, random effect
and potential yield variable. Where ΔAICc amongst the top ranked models
was <2, the model with the smallest number of parameters was
selected as the ‘best’ model. We then tested for non-linear
relationships by adding quadratic terms, and for interactions by adding
all pairwise interaction terms to the model, retaining them if ΔAICc
>2. We confirmed the explanatory power of the ‘best’ model
by calculating pseudo-R2 values and checked for
potential overfitting using a 200-fold cross-validation test, comparing
the pseudo-R2 of ‘best’ model to the distribution of
pseudo-R2 obtained from cross validation. The ‘best’
model was checked for normality of residuals and homoscedasticity using
diagnostic plots.
Because there is the potential for the ‘best’ model to exclude important
predictors, where several models had ΔAICc <2, we used a
multi-model inference approach (Grueberet al. 2011; Harrison et
al. 2018) to check that model averaged coefficients obtained from all
possible subsets of the global model confirmed those in the final model.
Results
All metrics showed relationships with at least two landscape variables,
but differed in their precise relationships with landscape composition
and configuration, confirming our final hypothesis (details in sections
3.1- 3.3). Cross-validation of pseudo-R2 did not
suggest overfitting for any model (Table 2) and inclusion of interaction
terms did not significantly increase model fit for any metric.
Relative function across the time series
The ‘best’ model for this resilience metric included a strong, negative
effect of fragmentation (i.e. edge: area ratio) of high-grade arable
land (Table 1, Fig. 2A). This suggests that resilience according to this
metric is highest where land is farmed in large, spatially contiguous
blocks (because edge: area ratio is lowest where patches are both large
and regular in shape). Once the effect of arable land is accounted for
(see Table 1), relative function showed a positive relationship with
both the area and fragmentation of semi-natural habitats (Table 1, Fig.
2A), suggesting that relative function is increased by semi-natural
habitat extent, especially when these habitats are dispersed throughout
the landscape (i.e. showing greater ‘edginess’; e.g. Fig. 3A). Relative
function also showed a strong, positive, non-linear relationship with
modelled potential yield, suggesting a major influence of climate and
soil type, up to a point when yield becomes limited by other factors.
Results from model averaging strongly supported the coefficients in the
‘best’ model, with weights of >=0.68 (Table 2).
It should be noted that relative function across the time series showed
a strong, positive correlation with mean yield (Pearson’s correlation, r
=0.99, p <0.001, n =135), whereas the other two metrics did
not (r =-0.29, p <0.04; r =<0.001, p =0.95,
respectively, n =135 in both cases).
Yield stability around a moving average
Yield stability showed a positive relationship with cover of high-grade
arable land and a negative effect of semi-natural habitat fragmentation
in the ‘best’ fitted model (Table 1, Fig. 2B). This suggests that yields
are more stable in areas with a higher coverage of arable land but that
yields are also more stable in areas where semi-natural habitats are
both extensive and (unlike the previous metric of relative function)
spatially contiguous (e.g. Fig. 3B). Again, this was only significant in
the best-fitted model given the effect of arable land, not in individual
models. The relationship with modelled potential yield stability was
much weaker than between relative function and potential function,
suggesting that areas with more variable climate did not necessarily
experience the most variable yield, with landscape factors potentially
having a greater moderating effect. Results from model averaging (Table
2) suggested a slightly weaker support for a single ‘best’ model,
although the weights for cover of arable land and semi-natural habitat
fragmentation were still high (>0.62)
Resistance to a specific event
Resistance was the only one of the three metrics not to show a positive
relationship with area of high-grade arable land in the ‘best’ model
(Table 1) and there was no support from model averaging to suggest such
a relationship (Table 2). Instead, resistance showed a strong, positive
relationship with cover of semi-natural grassland and a strong negative
relationship with distance from semi-natural grassland (Table 1). This
suggests that landscapes showing the highest resistance to the poor
conditions of 2012 were those with large extents of semi-natural
grassland and where a high proportion of arable land was in close
proximity to this grassland (e.g. Fig. 3C), independent of the quantity
of arable land. Although resistance showed a significant, positive
relationship with modelled potential resistance in individual models
(Table 1), suggesting that the severest drops in yield were in areas
which experienced the poorest weather conditions for yield, this
relationship was not significant in models which accounted for the
positive effects of semi-natural grassland, suggesting that these
effects can mitigate against climatic impacts. Support from model
averaging for the coefficients in the ‘best’ model was high (Table 2).
Discussion
Relationships with semi-natural habitat area
All three metrics showed significant, positive associations with the
area of semi-natural habitats, once the effects of arable land (see
section 4.3), soils and climate were accounted for, supporting our
hypothesis that semi-natural habitat has an important role in
contributing to the resilience of cropping systems to environmental
perturbation.
The most probable mechanism underpinning this relationship is that
semi-natural habitats provide reservoirs of organisms providing
beneficial ecosystem services (Martinet al. 2019). These include those involved in natural
pest-control, which not only directly predate upon pests (e.g. aphids,
slugs) but also on disease vectors (e.g. aphids for barley yellow dwarf
virus). Reductions in pest pressure may also have further beneficial
effects in terms of reducing plant stress, which in turn affects
susceptibility to fungal diseases
(Rosenzweig et al. 2001). There
are many studies demonstrating positive relationships between quantity
and proximity of semi-natural habitat and the abundance and richness of
natural enemies (Duelli et al.1990; Tscharntke et al. 2005;
Bianchi et al. 2006;
Ricketts et al. 2008;
Chaplin-Kramer et al. 2011;
Thies et al. 2011;
Rusch et al. 2013;
Holland et al. 2016;
Martin et al. 2016;
Holland et al. 2017). However,
within these broad groupings there are a great diversity of organisms,
each of which may have their own, complex relationships with landscape
structure and one another (Plantegenestet al. 2007; Chaplin-Krameret al. 2011; Martin et al.2013; Martin et al. 2016;
Karp et al. 2018). This complexity
means that the generally positive effects of semi-natural habitat on
natural enemy abundance and diversity frequently do not always translate
to increased predation of crop pests or enhanced yields
(Martin et al. 2013;
Mitchell et al. 2014;
Tscharntke et al. 2016;
Karp et al. 2018;
Martin et al. 2019). By examining
landscape effects on yield of a single crop, over a long time period, we
focussed on the outcome of this suite of complex interactions. The
positive relationships with area of semi-natural habitat evident in our
results should thus be more robust than those with individual natural
enemy groups (Chaplin-Kramer et al.2011; Martin et al. 2016) and
although we do not have direct evidence for the mechanisms underpinning
these relationships, demonstrable links between semi-natural habitat and
aspects of crop yield are the most directly compelling evidence for
farmers of the importance of semi-natural habitat for natural pest
control (Holland et al. 2017;
Kleijn et al. 2019).
Relationships with semi-natural habitat configuration
The inherent complexities of the relationships between habitats, natural
enemies, pests and crops are also likely to be responsible for the
varied responses to semi-natural habitat configuration
(Haan et al. 2019). The highest
levels of yield over time were delivered by landscapes with a high
coverage of arable land with semi-natural habitats distributed
throughout the landscape, whilst yield stability and resistance were
driven more strongly by the presence of unfragmented semi-natural
habitats, especially grasslands. Other studies have observed that the
effect of landscape configuration is highly context-dependent
(Haan et al. 2019). For example,
on the one hand, increased fragmentation maximises the boundary length
over which organisms can move between semi-natural habitats and arable
land (Tscharntke et al. 2005;
Rand et al. 2006;
Blitzer et al. 2012). On the other,
it lessens the value of individual habitat patches by reducing area and
increasing isolation (Mitchell et
al. 2015). This trade-off occurs simultaneously for beneficial
organisms and the pests and diseases which they help to control
(Plantegenest et al. 2007;
Karp et al. 2018). It is therefore
unsurprising that by comparing metrics of resilience calculated over
different timescales the balance between these factors shifts, from the
more arable-dominated, fragmented landscapes which maximise a range of
factors helping to stabilise yield over longer terms, to landscapes with
extensive, unfragmented semi-natural grassland which best support a more
specific subset of functions underpinning resistance to a particular
perturbation (see section 4.4).
Relationships with high-grade arable land area
Two metrics confirmed our hypothesis that resilience would be higher in
areas with a higher coverage of arable land. Higher relative function
(i.e. relative difference between local and national yield across the
time series) was strongly associated with landscapes characterised by
large, spatially contiguous (i.e. unfragmented) areas of high-grade
arable land. Because relative function correlated strongly with mean
yield across the time series, it is probable that this association
arises as given that farming systems in England have long developed to
exploit those areas most suitable for crop production
(Chambers & Mingay 1966) and these areas
typically also receive the greatest investment in agricultural inputs.
This result has recently been demonstrated at pan-European scales by
Martin et al. (2019), with higher
average yields in landscapes combining a high percentage of arable land
with a high edge density of semi-natural habitats.
The relationship of arable land to yield stability was weaker, whilst
resistance to the poor weather of 2012 showed no evidence of a positive
relationship with arable land. This suggests that as timescales become
shorter, and the focus of the metric shifts from maintaining high
average yield to reducing temporal fluctuations, the arable component of
the landscape becomes less important in comparison to other factors.
This exemplifies why higher mean yield over time is not necessarily
indicative of a sustainable system nor of the benefits derived by
agriculture from ecosystem services (Benton
& Bailey 2019).
Differences between resilience metrics
It is clear that the three resilience metrics differed in the strength
and direction of their relationship with landscape structure. Most
striking was the general trend for an increased importance of
semi-natural habitat as metrics were derived from shorter portions of
the time series. There are two (non-exclusive) explanations for this.
Firstly, as alluded to in section 4.2, a smaller subset of ecosystem
service components are likely to confer resistance to a specific extreme
event than those which maintain yield over longer timescales
encompassing a range of different environmental fluctuations. Therefore
relationships with specific landscape structure variables are both
stronger and more specific over shorter timescales, for example, the
relative importance of semi-natural grassland rather than semi-natural
habitat for the resistance metric. Grasslands are more similar to arable
land than other semi-natural habitats (e.g. woodland, wetland), both
structurally and in terms of community composition and may have
particularly significant effects on reservoirs of beneficial species in
the landscape (Duflot et al. 2015;
Bengtsson et al. 2019), presumably
including those which are particularly important in conferring
resistance to the specific perturbation we explored here.
Secondly, it is likely that many effects of landscape structure are only
made obvious when extreme events occur, given the current reliance of
English agriculture on the prophylactic use of agrochemicals
(Hillocks 2012) which may, under normal
circumstances, mask (or even suppress) potential benefits from ecosystem
services (Gagic et al. 2017). In
our case, the poor agronomic conditions of 2012 have been attributed to
a ‘perfect storm’ combination of factors, with a cold and wet spring
which reduced plant growth and grain formation, promoted outbreaks of
disease and delayed harvest (Defra 2012;
Impey 2012;
Met Office 2013). As mentioned in section
4.2, the precise mechanisms controlling the relationships between
resistance and semi-natural habitat are likely to vary with spatial and
temporal context (Haan et al.2019). For example, a particular extreme (e.g. high rainfall) might
increase populations of specific pests (e.g. molluscs) and so resistance
will be driven by the landscape factors which most affect their
predators (e.g. carabids). However, another year with different
conditions (e.g. drought) might promote another set of pests. These
would be in turn controlled by different natural enemies which may
respond to landscape structure in different ways
(Martin et al. 2019).
Despite this complexity, reductions in resistance or short-term
stability are indicative of where agricultural systems are vulnerable in
terms of the failure of farming practices to fully compensate for
environmental fluctuations, which might be missed by longer-term
measures. Of course, this is only useful if the perturbations to which
resistance is studied are representative of those predicted to occur in
the future. In the case of our study, not only are extreme weather
events likely to become more frequent
(Rosenzweig et al. 2001;
Trnka et al. 2014), but other
perturbations in the agricultural system may have similar consequences,
including anthropogenic factors such as the loss of pesticide active
ingredients in response to regulatory changes
(Hillocks 2012). Such shifts may of
necessity make farmers increasingly reliant on natural pest control and
thus the effects of landscape context may become increasingly important.
Conclusions and implications for landscape management
Our results confirm the current consensus that semi-natural habitats in
arable landscapes have a role for society that extends beyond simply
supporting agricultural biodiversity. These habitats were correlated
with crop yield resilience, as measured by three different metrics, and
as such have the potential to enhance the economic viability of farming
systems. We also show a particular importance of semi-natural habitats
in mitigating against extreme events, even if their impact on average
yield over time is more limited. At the scale we analysed (10km × 10km)
this is relevant to national or regional policy-making, which may
include agri-environmental funding for creating, restoring and
maintaining semi-natural habitats
(Critchley et al. 2004). Whilst
our results are not directly transferrable to the scale of the
individual farm, there is evidence that semi-natural habitats can help
increase yields (Pywell et al.2015; Tschumi et al. 2016a;
Tschumi et al. 2016b) and reduce
the impact of extreme events (Di Falco &
Chavas 2008) at finer spatial scales.
Our results also have a bearing on the relative merits of strategies
based on land-sharing (integrating food production and biodiversity
conservation on the same land) vs. land-sparing (spatially segregating
food production and biodiversity conservation). Whilst land-sparing is
often determined to be preferable in terms of maximising average
delivery of biodiversity and crop yield
(Kamp et al. 2015;
Ekroos et al. 2016;
Finch et al. 2019;
Lamb et al. 2019), it may not be
beneficial in term of crop yield resilience, if semi-natural and
agricultural landscapes become increasingly segregated beyond the scales
we examined here. This highlights an essential contrast between
immediate short-term agricultural production goals, and those of the
long-term stability of the system, both environmentally and
economically. Even within the scale of the landscapes we studied,
differences in the relative strength of the responses to arable land,
semi-natural habitat and its configuration suggest that there are
potential trade-offs to be made in managing landscapes for resilience
over shorter vs. longer timescales. Given the increased risk of extreme
events under climate change and concerns over our current reliance on
chemical management of pests, our finding that landscapes which most
enhance average yield over time are not necessarily those which confer
increased stability or resistance is an important challenge to address
in developing sustainable and resilient agricultural systems.