A framework for designing multi-functional cover crops
By J STORKEY1, T F DÖRING2,3, J A BADDELEY4, R COLLINS5, S RODERICK6, R
STOBART7, H E JONES8,6 and C WATSON4
1
Rothamsted Research, Harpenden, Herts AL5 2JQ
The Organic Research Centre, Elm Farm, Hamstead Marshall, Berks RG20 0HR
3
Humboldt University Berlin, Albrecht-Thaer-Weg 5, 14195 Berlin, Germany
4
SRUC, Craibstone Estate, Aberdeen AB21 9YA
5
IBERS, Aberystwyth University, Gogerddan, Aberystwyth, Ceredigion SY23 3EB
6
Duchy College, Rosewarne, Cambourne, Cornwall TR14 0AB
7
NIAB TAG, Morley Office, Wymondham, Norfolk NT18 9DF
8
Reading University, Whiteknights, Reading, Berkshire, RG6 6AH
2
Summary
Cover crops are sown to provide a number of ecosystem services including nutrient
management, mitigation of diffuse pollution, improving soil structure and organic
matter content, weed suppression, nitrogen fixation and provision of resources for
biodiversity. Although the decision to sow a cover crop may be driven by a desire to
achieve just one of these objectives, the diversity of cover crops species and mixtures
available means that there is potential to combine a number of ecosystem services
within the same crop and growing season. Designing multi-functional cover crops
would potentially help to reconcile the often conflicting agronomic and
environmental agendas and contribute to the optimal use of land. We present a
framework for integrating multiple ecosystem services delivered by cover crops that
aims to design a mixture of species with complementary growth habit and
functionality. The optimal number and identity of species will depend on the services
included in the analysis, the functional space represented by the available species
pool and the community dynamics of the crop in terms of dominance and coexistence. Experience from a project that applied the framework to fertility building
leys in organic systems demonstrated its potential and emphasised the importance of
the initial choice of species to include in the analysis.
Key words: ecosystem service, functional traits, competition, species mixtures
Introduction
One consequence of the advent of inorganic fertilisers and pesticides has been the
simplification of crop rotations with crop choice being driven more by short term market
forces as opposed to agronomic considerations to do with regulating pest, weed and disease
pressure and maintaining soil health. However, it is increasingly being recognised that this
current, chemical based, model of intensive agriculture may not be sustainable because of the
erosion of ecosystem services provided by resilient soils and diverse beneficial insect
populations (Baulcombe et al., 2009; Brown et al., 2011). One way to mitigate the negative
consequences of intensification is to diversify cropping systems, reducing the risks of
selecting for epidemics of economically damaging pests, weeds and diseases adapted to single
crops, reducing the pressure on soils and providing more diverse habitats for non-crop
biodiversity (Smith et al., 2010; Davis et al., 2012). In this context, cover crops present an
opportunity to deliver multiple ecosystem service in the same crop and growing season as
several species can be grown together (Schipanski et al., 2014; Storkey et al., 2015). A large
number of species are currently grown as cover crops with many more potential mixtures of
species available. These species and mixtures will vary in their capacity to deliver different
ecosystem services and methodologies and decision support tools are required to identify the
optimal species choice or mixture for a given purpose and context. This will depend on what
the cover crop is required to deliver and the ‘habitat template’ in terms of soil type,
establishment conditions and growing window.
We present a framework developed as part of the LegLINK project (‘Using legume based
mixtures to enhance nitrogen use efficiency and economic viability of cropping systems’) that
ran between 2009 and 2012 (HGCA Project Report No. 513). The focus of the project was on
fertility building leys in organic systems with the aim of better managing the release of
nitrogen from green manures by integrating species with a higher lignin and polyphenol
content. However, the general principles used in the project to design the optimal system
could be applied to any cover crop to integrate multiple ecosystem services, including those
included in conventional systems.
Materials and methods
The initial step in designing a multi-functional cover crop is to review all candidate species.
In LegLINK, an expert group derived this ‘long list’ of species and compiled available data
on agronomic and biological traits (Table 1). However, we propose that a similar list
including all possible cover crop species that brings together all the available data on their
traits is required to provide the foundation for a comprehensive support tool for selecting
species. The long list compiled was then used to select candidate species for field trials. In
terms of optimising multi-functionality, it was desirable to capture as much functional
diversity within the available species pool within the constraints of only being able to include
a sub set within the field experiments. A Principal Components Analysis (PCA) was,
therefore run to identify a set of species that were spread across the multi-dimensional trait
space. This was combined with information of frost tolerance, and tolerance to grazing to
exclude species that were not suitable for the specific system being designed within the
LegLINK project. Again, we propose that a large scale multivariate analysis on a more
comprehensive dataset would be a useful exercise to identify functionally similar species or
groups that could form the basis of a species mix within the constraints of a specific cropping
system.
Scientific name
Common name
Anthyllis vulneraria
Onobrychis viciifolia
Trifolium pratense
Medicago sativa
Kidney vetch
Sainfoin
red clover
Lucerne
Large birdsfoot
trefoil
Black medic
Perennial
ryegrass
Lotus pedunculatus
Medicago lupulina
Lolium perenne
Maximum
height
(cm)
60
60
100
90
Seed
weight
(mg)
3.05
19.2
1.8
2.4
Rooting
depth
Grazing
tolerance
med/deep
med/deep
deep
deep
no
yes
moderate
yes
60
60
1
1.6
medium
medium
yes
yes
90
2
shallow
yes
White sweet
Melilotus alba
clover
Trifolium repens
white clover
Trifolium hybridum
Alsike clover
Lotus corniculatus
Birdsfoot trefoil
Vicia sativa
Winter vetch
Lupinus alba
White lupin
Lupinus luteus
Yellow lupin
Lupinus augustifolius Blue lupin
Lolium multiflorum
Italian ryegrass
Trifolium incarnatum crimson clover
Subterranean
Trifolium subteranneu clover
Trifolium
alexandrinum
Egyptian clover
Vicia hirsuta
Hairy vetch
Trigonella foenum
graecum
Fenugreek
Trifolium
resupinatum
Persian clover
Vicia lathyroides
Summer vetch
Lathyrus Pratensis
Meadow Pea
150
50
40
35
120
120
60
120
130
50
2.3
0.5
0.7
1.2
41
297.5
129.6
168
2.9
3.1
v deep
medium
med/deep
medium
med/deep
deep
deep
deep
shallow
medium
no
yes
moderate
yes
no
no
no
no
yes
no
20
6.7
deep
yes
45
30
2.8
4.9
med/deep
med/deep
moderate
no
20
11.9
medium
moderate
50
20
120
0.8
1.95
12.39
med/deep
med/deep
v deep
moderate
no
no
Table 1. Example of initial list of species with selection of agronomic and biological
traits that acts as a starting point for selecting optimal mix of species. We propose
developing a comprehensive resource that includes all potential species and traits.
Within the LegLINK project, there was the opportunity to screen the selected cover crop
species for a range of ecosystem services. Within the generic framework we are presenting
here, this will not ordinarily be possible. However, from experience gained from the
LegLINK project and using evidence from the wider literature it is possible to predict relative
performance in terms of the delivery of different services from functional traits. For example,
we would expect capture of nutrients to be associated with species with deep, well developed
rooting systems, good early establishment with large seed size and support of pollinators with
flowering traits. A number of these ecosystem service / functional trait relationships were
quantified in the LegLINK project (Storkey et al., 2015), Figure 1, and could be used to
predict multi-functionality of species and mixtures not tested within the project. Where a
mixture is being assessed, one would use the aggregated trait value that represents the
‘average’ value taking into account relative biomass:
n
trait agg   pi x trait i
i 1
where n = number of species in community, p relative proportion of species i and traiti =
trait value for species i.
600
Numbers of invertebrates (m-2)
550
R² = 0.8644
500
450
400
350
300
250
200
0.01
0.015
0.02
0.025
0.03
0.035
Specific leaf area (m2 / g)
Figure 1. Relationship between support of invertebrates (a resource for farmland birds
(Storkey et al., 2013)) and a functional trait, specific leaf area, for a range of legume cover
crop species. This and similar relationships could be used to predict the function of species
not included in the LegLINK analysis.
Data on the growth habit and phenology of the range of candidate species would also be
useful for estimating the potential complementarity between species in terms of coexistence in
space and time. In LegLINK, a simulation model of plant competition was parameterised for
each species but it should be possible to derive general rules based on plant ecological
strategy and traits such as maximum height, rooting depth and flowering time to mix species
that would be expected to occupy different niches in the canopy.
Results
The species originally included in the candidate list for the LegLINK project fell into three
broad ecological strategies. These differences in functional characteristics were reflected in
the variation of delivery of the range of ecosystem services. Firstly, there was a group of large
seeded tall species, including Lathyrus pratensis (meadow pea), Vicia sativa (winter vetch)
and Onobrychis viciifolia (sanfoin). These species tended to establish well, grow quickly early
in the season but some did not respond well to mowing. Secondly, there was a group of
shorter, fast growing species with a high specific leaf area and small seed including Trifolium
repens, T.pratense and T.hybridum. These species grew back quickly after mowing and
supported high numbers of invertebrates but were relatively slow to establish. Finally, Lotus
pedunculatus and Medicago lupulina were short, slow growing and tended to have a higher
lignin and polyphenol content which meant residues broke down more slowly after
incorporation of the cover crop. Two species, Trifolium pratense (red clover) and Medicago
sativa (Lucerne) occupied the centre of the multi-dimensional trait space and were good ‘allrounders’. An analysis of the optimal multi-functional mix of the available species included
one of these all-rounders complemented by species from the extremes of the trait-space
(Storkey et al., 2015).
In designing the optimal mix, the seasonality of growth and growth habitat was important to
consider in the LegLINK data. Mixtures that performed well were those that combined a large
seeded, fast growing species early in the season with one that grew back quickly after mowing
late in the season. In this case, sequential flowering was also sometimes observed, extending
the overall period of flowering resources for pollinators. As well as complementary growth,
combining species also had benefits for yield of the following crop. Yield of a winter wheat
crop, grown without fertiliser inputs, after the incorporation of the cover crops in the autumn
was generally related to the accumulated biomass of the legumes. However, there were also
differences in the response of the components of yield to different species. For the same
amount of total crop yield, wheat following a fast growing, leafy species like white clover had
more ears but smaller grain than a crop following a woodier species such as Lucerne (Figure
2). This was explained by the different breakdown characteristics of the residue. The residue
of fast growing species broke down quickly, making nutrients available to the crop early in
the growing season, whereas, residue of species with higher lignin content broke down more
slowly meaning nutrients were still available during grain filling. This is a good illustration of
the potential for mixing species to gain additional benefit – a mix of white clover and lucerne
could maintain yield while reducing the risk of leaching of nutrients from the system.
Figure 2. Thousand grain weight and head density of wheat crop following incorporation of
different cover crops expressed as ranks. Cover crop species that produced high biomass
tended to result in higher crop yields but there was variation in the effect on different yield
components. AC- Alsike clover, BM – Black medick, BT – Birdsfoot trefoil, CC – Crimson
clover, LT – Large birdsfoot trefoil, LU – Lucerne, MP – Meadow pea, SC – Sweet white
clover, SF – Sanfoin, WV – Winter vetch. NM & IM are mixtures of all species either
inoculated or non-innoculated.
Discussion
There is a growing interest in integrating cover crops into conventional rotations for a variety
of reasons but a lack of empirical evidence to guide the choice and potential combination of
species. The framework we have developed based on the collation of data on functional traits
and agronomic characteristics of candidate cover crop species and the quantification of
relationships between traits and ecosystem service delivery has the potential to provide this
evidence and serve as a decision making tool. However, this will depend on a concerted effort
to bring together all of the information in the scientific literature and from growers experience
with many different species of cover crops into a single repository. This needs to be done in a
structured, objective way that identifies standard metrics across the crops that will allow them
to be compared like-for-like across a range of ecosystem services. Such an exercise will result
in novel combinations of crops for trialling in the field as well as the identification of
potential gaps in the functional space covered by species currently cultivated as cover crops
that could potentially be filled by additional species that have not, up to now, been considered
as candidate cover crops.
Acknowledgments
This work was sponsored by Defra and industry partners under the Defra Sustainable Arable
LINK programme (LK09106). Rothamsted Research is a national institute of bioscience
strategically funded by the Biotechnology and Biological Sciences Research Council.
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