Content uploaded by Sanne Van Den Berge
Author content
All content in this area was uploaded by Sanne Van Den Berge on May 20, 2021
Content may be subject to copyright.
Soil carbon of hedgerows and ‘ghost’ hedgerows
Sanne Van Den Berge .Pieter Vangansbeke .Lander Baeten .
Thomas Vanneste .Fien Vos .Kris Verheyen
Received: 17 July 2020 / Accepted: 26 April 2021
ÓThe Author(s), under exclusive licence to Springer Nature B.V. 2021
Abstract Agroforestry can contribute significantly
to carbon sequestration in agricultural lands, as carbon
accumulates both in tree biomass and the soil. One of
the oldest, yet declining, forms of agroforestry in
Europe are hedgerow-bordered fields. An analysis of
historical maps of our study area in Belgium shows
that 70% of the hedgerow network was cleared since
1960, creating a large number of ‘ghost’ hedgerows.
We selected arable fields next to hedgerows, ’ghost’
hedgerows and grass strips to study how hedgerow
trees influence SOC stocks and how much of these are
still present after hedgerow clearing. SOC stocks to a
depth of 23 cm reached up to 81.7 ±28.8 Mg C ha
-1
in hedgerows, storing a considerably larger amount of
soil carbon compared to grass strips (56.6 ±14.5 Mg
Cha
-1
). These built-up stocks were completely gone
in ’ghost’ hedgerows (57.9 ±14.1 Mg C ha
-1
). In the
fields adjacent to hedgerows, SOC stocks were only
slightly (and insignificantly) increased compared to
stocks in fields with grass strips (56.4 ±6.3 vs
55.6 ±5.0 Mg C ha
-1
) with an exponential decay
up to 30 m from the margin. This trend was still
limitedly detectable in ’ghost’ hedgerowbordered
fields, however stocks were not elevated anymore
(53.9 ±6.1 Mg C ha
-1
). Since 1960, 4 957 ±1
664 Mg C from the soil alone were released back into
the atmosphere due to hedgerow removal in the study
area. The implementation of a strict hedgerow
conservation policy would thus be a highly effective
climate change mitigation measure in agricultural
landscapes.
Keywords Soil Organic Carbon Field margin
Hedgerow conversion Climate mitigation Legacy
effects
Introduction
Soils are the biggest terrestrial reservoir of carbon on
the planet (IPCC 1990; Jobba
´gy and Jackson 2000).
They hold 3.8 times more organic carbon than all the
plants and trees in the world (Jacobson et al. 2000;
Nair et al. 2010) and store three times as much organic
carbon as the atmosphere (De Stefano and Jacobson
2018; Lal 2004a). Soils also act as sources of carbon,
with microbes and other soil organisms releasing
annually about ten times the yearly carbon emissions
from fossil fuel burning (IPCC 1996; Schlesinger and
Andrews 2000). Being both a sink and source of
atmospheric carbon, any variation in the soil carbon
Supplementary information The online version contains
supplementary material available at (https://doi.org/10.1007/
s10457-021-00634-6).
S. Van Den Berge (&)P. Vangansbeke
L. Baeten T. Vanneste F. Vos K. Verheyen
Forest & Nature Lab, Department of Environment, Ghent
University, Campus Gontrode, Geraardsbergsesteenweg
267, 9090 Melle, Belgium
e-mail: sanne.vandenberge@ugent.be
123
Agroforest Syst
https://doi.org/10.1007/s10457-021-00634-6(0123456789().,-volV)(0123456789().,-volV)
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
pool has an important impact on the global carbon
budget (Stockmann et al. 2013).
Changes in land use and land cover rank second
behind the combustion of fossil fuels as the most
important anthropogenic drivers for increasing atmo-
spheric carbon concentrations (Prentice et al. 2001;
Vermeulen et al. 2012). Commonly, arable lands act as
a source of greenhouse gasses (CO
2
,CH
4
, and N
2
O),
having lost an important portion of their original SOC
(i.e. carbon stocks in the pre-agricultural soils, e.g.
forest soils) (Stavi and Lal 2013). However, when soil
carbon conservation practices are implemented, agri-
cultural soils have a promising carbon sequestration
potential. The Effort Sharing 2021–2030 Regulation
(REGULATION (EU) 2018/842), in line with the
COP21 Paris Agreement (UNFCCC 2015), includes
agricultural practices aiming at reducing net green-
house gas emissions (Albrecht and Kandji 2003; Lal
2004a). Agroforestry, that is, the integrated manage-
ment of trees and permanent vegetation on croplands
or grasslands (European Commission 2013), is high-
lighted as one of the agricultural practices with the
greatest potential for climate change mitigation and
adaptation (Aertsens et al. 2013; Hart et al. 2017).
In agroforestry systems, carbon sequestration
occurs in the aboveground and belowground tree
biomass, but also in the soil. Agroforestry trees grow
in an open environment, resulting in higher growth
rates of the individual trees compared with forest trees
(Van Den Berge 2021). The carbon storage in the
biomass of trees and shrubs is of that magnitude that
several authors have argued to include hedgerow and
tree row inventories in the national greenhouse gas
inventories (Black et al. 2012; Van Den Berge et al.
2021). As to soil organic carbon (SOC), agroforestry
systems may sequester more carbon than annual
cropping systems through litterfall, root exudation or
soil erosion control by the perennial woody vegetation
(Lenka et al. 2012; Montagnini and Nair 2004; Pardon
et al 2017). Those inputs can help to stabilize soil
organic matter and decrease biomass decomposition
rates, increasing SOC stocks (Lal 2004b; Sollins et al.
2007).
Even though agroforestry systems have a high
potential to function as soil carbon sinks, quantitative
estimates of SOC stocks for a variety of agroforestry
systems, especially in the temperate regions, remain
scarce (Cardinael et al. 2015; Nair et al. 2009,2010).
The effect of trees on SOC build-up is dependent on
the agroforestry system design (e.g. alley cropping,
boundary plantings, scattered trees), the tree species
(e.g. in terms of litter quality, root system type), the
age of the trees, the type of cultivated crops in the
fields, soil characteristics and climatic factors (De
Stefano and Jacobson 2018; Dignac et al. 2017).
Therefore, each SOC study in agroforestry systems
should accurately describe these important explana-
tory variables.
One of the oldest and traditional forms of agro-
forestry systems are hedgerows bordering arable fields
and grasslands. The establishment of hedgerows has a
century-long tradition in Central and Western Europe,
where they served as sources of timber, firewood and
fruits, functioned as property boundaries, restrained
livestock and protected crops from wind, floods,
droughts and erosion (Baudry et al. 2000; Marshall
and Moonen 2002). In present agroforestry systems,
they continue to deliver these ecosystem services,
while capturing atmospheric carbon in biomass and
soil. During the twentieth century, many hedgerows
have disappeared—i.e. ‘ghost’ hedgerows were cre-
ated—due to the mechanisation and intensification of
agriculture in Europe (Baltensperger 1987). Espe-
cially between the 1950s and 1980s, when land
consolidation processes were implemented, hedge-
rows were cleared. Locally or even at the national
scale, more than 70% of hedgerow networks were
removed (e.g. Bazin and Schmutz 1994; Litza and
Diekmann 2020). Therefore, it is not only relevant to
study how trees in agrosystems influence present-day
SOC stocks, but also to look into SOC legacy effects of
cleared hedgerows.
Here we study the effect of hedgerows and ‘ghost’
hedgerows on the SOC stocks in arable fields and field
margins. The term ‘ghost’ refers to the fact that while
the trees have been removed, the original position of
the hedgerow is still visible in the landscape, e.g.
based on current parcel structure, the presence of a
ditch. We selected arable fields bordered by a hedge-
row, a ‘ghost’ hedgerow and a grass strip. The grass
strip in each case acts as a control; this way the
hedgerow (legacy) effect can clearly be distinguished
from effects caused by other edge effects (e.g.
presence of grassy vegetation, effects related to slight
differences in tillage). To our knowledge, this is the
very first study that looks into SOC legacy effects of
hedgerows in arable fields. We aim to answer the
following questions:
123
Agroforest Syst
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
1. What is the influence of a (‘ghost’) hedgerow on
the SOC concentration and SOC stocks in the field
margins? That is, comparing margins with hedge-
row or ‘ghost’ hedgerow against grass strips.
2. How far does the influence of a (‘ghost’) hedgerow
reach into the adjacent agricultural field? That is,
comparing the spatial pattern of SOC concentra-
tion on margin-to-field transects between hedge-
row and ‘ghost’ hedgerow systems against grass
strips.
3. How much SOC is stored in the hedgerows and
hedgerow-bordered fields in the study area, and
how much is lost due to the removal of hedgerows
since 1960?
Materials and methods
Study area
Our study area is situated in the countryside of
Turnhout (51°1901800N, 4°5601500E), a Belgian munic-
ipality with a total area of 56 km
2
, 16.42% of which are
forests and 55.86% are agricultural lands (based on the
CORINE methodology, European Environment
Agency 2013). The climate is temperate with a total
annual rainfall of 755 mm and an average temperature
of 10.1 °C (KMI 2019). Turnhout is part of the
Campine region, which is characterized by aeolian
sandy soils deposited during the last glacial period
(Tavernier and Mare
´chal 1972). The sandy soils are
moderately wet and have a clear iron and/or humus B
horizon, alternated with loamy sand soils without a
developed profile (Sevenant et al. 2002). The altitude
varies between 18 and 35 m above sea level and the
prevailing wind direction is South-Southwest (KMI
2019).
Site selection
In Belgium, most hedgerows are human-created
systems of closely spaced shrubs and trees that form
the boundary to fields. Historically, hedgerows were
common linear features in the study area for multiple
purposes. In the fifteenth century they functioned as
sources of fuel wood when the peat reserves in the
bogs were depleted (Verboven et al. 2004). Trees and
shrubs were also planted as wind breaks along arable
land near drifting sands to prevent wind damage and
sand deposition (Buis 1985; Kint et al. 2010). Hedge-
rows functioned as livestock barriers in the heathlands,
and peatlands and marshes were drained via wooded
banks with ditches on both sides (Burny et al. 2013;
Van Elst 1916).
To be able to study the effect of hedgerows and
‘ghost’ hedgerows on the SOC stocks in the field
margins and the fields, we selected sites consisting of
an arable field bordered by a hedgerow, a ‘ghost’
hedgerow and a grass strip (further referred to as
‘triplets’), following the design of Pardon et al. (2017).
We used the topographical map of the ‘Geografische
Data-Infrastructuur’ (1:25,000) by the Flemish gov-
ernment of the period 1960–1967 (hereafter referred to
as from 1960) as the reference map—this was the first
complete map of the whole study area where hedge-
rows were clearly visible—to start the selection of
triplets. All ‘original hedgerows’ were digitized in
ArcMap (ESRI 2018) and their presence was verified
on eight subsequent maps up to and including the
Aerial photograph 2016 (Google Satellite 2018).
When hedgerows were present on the first map
(1960), but had disappeared in one of the later maps,
they were digitized as ‘ghost’ hedgerows.
The selected set of study sites comprised ten fields
bordered by hedgerows, ten fields bordered by ‘ghost’
hedgerows and six fields bordered by grass strips
(n = 26), combined in six triplets and four duets (i.e.
when no grass strip could be found). All field margins
were at least 50 m in length and were adjacent to an
arable field in conventional cultivation. Within triplets
/ duets, all field margins had a similar orientation and
fields had no other surrounding trees or shrubs, and
had the same soil type and the same crop type.
Cropping was done using conventional ploughing and
power-harrowing to establish the seed beds, mainly in
a four year rotation system. The selected fields were
cultivated with maize (Zea mays subsp. Mays, 77%),
English ryegrass (Lolium perenne, 11%, cultivated as
roughage), potato (Solanum tuberosum, 4%) or pump-
kins (Cucurbita pepo, 8%). Soils ranged from dry to
wet sandy soils and dry to wet loamy sand soils
(according to the Belgian soil classification system;
Tavernier and Mare
´chal 1972) (Table S1).
The selected ‘ghost’ hedgerows dated from the
period between 1960–1971 (30%), the period
1971–1990 (60%), and one ‘ghost’ hedgerow dated
from 1990–1995. The selected hedgerows were at
least 58 years present as a field margin—since 1960—
123
Agroforest Syst
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
and had structural dimensions width 3.5 ±1.0 m,
length 121.5 ±46.9 m and height 21.4 ±2.0 m. All
hedgerows were boundary plantings (i.e. no alley
cropping in the fields) and had a multi-storied structure
(at least three distinguishable layers: herb, shrub and
tree layer). The most frequent species in the tree and
shrub layer were silver birch (Betula pendula) (present
in 90% of the hedgerows), pedunculate oak (Quercus
robur) (70%), black cherry (Prunus serotina) (70%)
and goat willow (Salix caprea) (50%) (Table S1).
From previous research we know that the population
structure of the hedgerows in the study area shows a
positively skewed (right-skewed) diameter distribu-
tion (Van Den Berge 2021), similar to the diameter
distribution of an uneven-aged forest stand, in which
regeneration causes a high number of individuals in
the smaller diameter classes (Shorohova et al. 2009).
Soil sampling and soil analysis
In September 2018, we took soil samples in all fields
around the time of crop harvest.In each field, three
transects were sampled perpendicular to the field
margin according to the sampling design of Pardon
et al. (2017). The transects were set out in the middle
of the field margin at a distance of 20 m from each
other. Each transect consisted of six sampling posi-
tions at distances 0, 2, 5, 10, 20 and 30 m from the field
margin (Fig. 1). At each position, we took a mixed soil
sample composed of three samples from the mineral
soil (excluding surface litter accumulations) to a depth
of 23 cm (i.e. the plough layer, following Pardon et al.
2017) using a 25-mm internal diameter gouge auger.
Additionally, we took two soil samples to determine
the bulk density at 0 and 20 m in each transect with a
Kopecky ring (volume of 100 cm
3
) at 10–15 cm depth
(Fig. 1). In each field margin (i.e. at 0 m) and each
field at 20 m, a soil profile to a depth of 1 m was taken
in the central transect for a soil profile description and
the depth of the soil A-horizon (when present) was
measured. In total, 468 mixed soil samples
(26 9396), 156 Kopecky samples (26 9392)
and 52 soil profiles (26 9192) were taken.
All soil samples were oven-dried at 40 °C for 48 h.
Dry soils were sieved (\1 mm) to obtain a uniform
composition and to remove stones and coarse mate-
rials. The SOC concentration (%) was determined by a
CNS analysis with a vario MACRO cube (Elementar,
Germany). Kopecky samples were dried a second time
for 24 h at 105 °C and weighted.
Calculations and data analysis
For all sampling points in the field margins (i.e. at a
distance of 0 m in hedgerows, ‘ghost’ hedgerows and
grass strips) and all sampling points at a distance of
20 m from the margins, the soil bulk density (qb,kg
m
-3
) was determined by dividing the dry mass of the
Kopecky sample by its volume. The SOC stock (kg C
m
-2
) was determined by multiplying the SOC con-
centration (%) with the bulk density and the thickness
of the soil layer (i.e. 0.23 m) using Eq. 1:
SOC stock kg C m2
¼SOC %ðÞqbkg m3
0:23 mðÞ
ð1Þ
To compare SOC concentration, bulk density and
SOC stock among the different field margin types, we
used the one-way analysis of variance (ANOVA) and
the post-hoc Tukey Honest Significant Differences test
via respectively the aov function and the TukeyHSD
function in the stats package in R (R Core Team 2020).
This analysis was done separately for the soil data
measured in the field margins and in the fields at a
distance of 20 m from the margin.
To investigate the distance decay in SOC concen-
tration from the margin into the field, we used linear-
mixed effects models via the lme function in the nlme
package (Pinheiro et al. 2018). Distances to the field
margin were transformed by taking the natural loga-
rithm to linearize the exponential decay. The distance
to the field margin, the field margin type (i.e.
hedgerow, ‘ghost’ hedgerow and grass strip) and their
interaction were included as fixed effects in the
models. To account for the hierarchical structure and
non-independence of the data within triplets/duets and
within transects, we included ‘triplet/duet’ and ‘tran-
sect’ as nested random effects (In R syntaxis the model
was formulated as: SOC concentration *log(dis-
tance to field margin) * field margin type ?ran-
dom =*1|triplet code/transect code). The
marginal R
2
and conditional R
2
of the models
(Nakagawa and Schielzeth 2013) were calculated
using the function r.squaredGLMM in the library
MuMIn (Barton 2019).
123
Agroforest Syst
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Taking into account the SOC stocks in the field
margins and the decreasing trend in SOC concentra-
tion from margin to field, we calculated SOC stocks
per hectares of (‘ghost’) agroforestry parcels and
control parcels (i.e. with grass strips). In accordance
with Hossain et al. (2015), we used the relationship
between the observed soil bulk density and SOC to
predict the bulk density for the sampling points where
it was not measured using Eq. 2:
qbgcm
3
¼1:669 e0:107 SOC %ðÞ ð2Þ
The model [Eq. (2)] was fitted using the non-linear
least squares regression (NLS) implemented in the R
package nlme (Pinheiro et al. 2018, Figure S1).
Starting values for the model parameters were adapted
from Hossain et al. (2015).
The calculated SOC stock values were then bench-
marked against values for temperate agroforestry
systems found in the published literature (search terms
‘‘soil organic carbon’’, ‘‘carbon storage’’ and ‘‘carbon
sequestration’’ in combination with ‘‘hedgerows’’,
‘‘tree rows’’ and ‘‘agroforestry’’ via Web of Science;
followed by a selection of studies conducted in the
temperate climate zone). Benchmarking our results
provided insight in how different agroforestry systems
contribute to the SOC stock build-up in agricultural
lands, especially since published SOC stock values in
such systems are still scarce.
To estimate the lost soil carbon potential in the
study area since 1960 (i.e. the carbon that would have
been sequestered if the ‘ghost’ hedgerows would not
have been cleared) we have calculated the SOC stock
(Mg C) for each lost hedgerow since 1960 (based on
data from ‘ghost’ hedgerows in 2016) and we have
calculated what the SOC stock would be for that
hedgerow if it were still there (based on the data from
the remnant hedgerows in 2016). For each ‘ghost’
hedgerow, the ‘lost soil carbon potential’ is then the
difference between those two values. By taking the
sum on a landscape scale, we have estimated the lost
soil carbon potential since 1960. The length of all
hedgerows in the study area in 1960 and 2016 was
Fig. 1 Location of soil sampling points in the margins and
fields bordered by a hedgerow (he), a ‘ghost’ hedgerow (gh) and
a grass strip (gr). At the level of the black rectangles three soil
samples (0.23 m depth) were combined in one mixed soil
sample. Yellow rectangles represent the locations were both a
Kopecky sample at 10–15 cm depth and a mixed soil sample
were taken. At the level of the blue rectangles a mixed soil
sample, a Kopecky sample and the soil profile to a depth of 1 m
to were taken. Right: example of a field bordered by a hedgerow
(upper image) and a field bordered by a ‘ghost’ hedgerow (lower
image). Fields bordered with ‘ghost’ hedgerows and grass strips
look similar
123
Agroforest Syst
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
computed with QGIS and an average width of
3.5 ±1.0 m was assumed. In addition, for each
(‘ghost’) hedgerow, the SOC stocks in the field buffers
of 2, 5, 10, 20 and 30 m parallel to the field margins
were calculated and added to the SOC stocks and
losses in the study area.
All analyses were performed in R version 4.0.0 (R
Core Team 2019) and graphs were made using the
ggplot2 package (R Core Team 2020; Wickham
2016).
Results
Comparison among field margins
The distribution of SOC concentration values in our
dataset was characterised by a mean (±SD) value of
2.08% (±0.98). In the field margins the SOC
concentration of the three field margin types were
significantly different from each other. The concen-
tration in hedgerow margins (3.31 ±1.41%) was
significantly higher than in ‘ghost’ hedgerow margins
(1.85 ±0.46%, ANOVA p \0.001) and grass strips
(1.89 ±0.37%, p\0.001), whereas the latter two
were not significantly different. In the fields at 20 m
distance from the margins, no significant differences
in SOC concentrations were found between the three
field margin types (Fig. 2a).
Bulk density in our data set had a mean (±SD)
value of 1342 (±199) kg m
-3
(i.e.
1.3 ±0.2 g cm
-3
). The bulk density was significantly
lower in hedgerow margins (1127 ±220 kg m
-3
)
than in ‘ghost’ hedgerow margins
(1369 ±162 kg m
-3
,ANOVA analysis p \0.001)
and in grass strips (1297 ±170 kg m
-3
,p\0.01).
Bulk densities in ‘ghost’ hedgerow margins and grass
strips were not significantly different from each other.
In the fields at 20 m distance from the margins, no
significant differences in bulk densities were found
between the three field margin types (Fig. 2b).
The SOC stock in our dataset had a mean (±SD)
value of 61.4 (±20.6) Mg C ha
-1
. The SOC stock was
significantly higher in hedgerow margins
(81.7 ±28.8 Mg C ha
-1
) than in ‘ghost’ hedgerow
margins (57.9 ±14.1 Mg C ha
-1
,ANOVA
p\0.001) and in grass strips (56.6 ±14.5 Mg C
ha
-1
,p\0.001). SOC stocks in ‘ghost’ hedgerow
margins and grass strips were not significantly
different from each other. In the fields at 20 m
distance from the margins, a small significant differ-
ence in SOC stocks between fields bordered by
hedgerows (59.5 ±14.9 Mg C ha
-1
) and fields bor-
dered by ‘ghost’ hedgerows (50.5 ±13.3 Mg C ha
-1
)
was found (ANOVA p \0.05); no other significant
differences in SOC stocks at 20 m distance in the
fields between the field margin types was found
(Fig. 2c).
SOC concentration changes from margin to field
The model predicting the changes in SOC concentra-
tion with distance to field margin and field margin type
had a total explanatory power of 50% (conditional
R
2
= 0.5) and the part related to the fixed effects alone
was 20% (marginal R
2
= 0.2). The SOC concentra-
tions in the hedgerow margins, that is, at distance zero
along the transect, was significantly higher compared
with grass strips (difference in the intercept = 0.76,
SE =0.14, p \0.001; Fig. 3). The SOC concentra-
tions in the ‘ghost’ hedgerow margins was not
different from the grass strips (differences in inter-
cept = -0.12, SE =0.14, p =0.392). The effect of
distance to field margin on the SOC concentration in
the fields was different for the three field margin types
(Fig. 3). In fields with grass strips, the SOC concen-
trations did not change significantly with distance
along the transects (slope =-0.02, SE =0.04,
p=0.717). For ‘ghost’ hedgerow-bordered fields,
the effect of distance to field margin on the SOC
concentration was slightly more negative compared
with grass strips (difference in slope = -0.06, SE =
0.06), however this effect was not significant com-
pared to fields with grass strips. For hedgerow-
bordered fields, SOC concentration showed a signif-
icantly more negative exponential decrease with
increasing distance from the margin compared to
c
Fig. 2 Soil organic carbon concentration (Fig. 2a), bulk density
(Fig. 2b) and soil organic carbon stock (Fig. 2c) to a depth of
23 cm in hedgerow (he), ‘ghost’ hedgerow (gh) and grass strip
(gr) margins and adjacent fields at a distance of 20 m from the
margin. The boxes indicate the 25% and 75% quartile, with a
median line and 2 whiskers showing the 10% and 90% quantiles.
Significant differences in soil characteristics between the field
margin types are indicated with asterisks (p-value level based on
ANOVA ***: p B0.001; *: p \0.05)
123
Agroforest Syst
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
123
Agroforest Syst
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
fields with grass strips (differences in slope = -0.36
SE =0.06, p \0.001).
SOC stocks and losses in the landscape
The total length of all hedgerows in the study area
(region of 5 600 ha) in 1960 was 180 km (1467
hedgerows) covering 63.0 ±18 ha. In 2016, this was
a network of 54 km (382 hedgerows), covering
18.9 ±5.4 ha. About 70% of the total hedgerow
length (74% of the hedgerows) was lost during the
period 1960–2016, with the main hedgerow destruc-
tion situated between 1960 and 1990 (Fig. 4).
Per running kilometre and for the upper 23 cm of
the soil the SOC stock in hedgerows, ‘ghost’ hedge-
rows and grass strips (with a mean width of
3.5 ±1 m) was 28.6 ±10.1, 20.3 ±4.9 and
19.8 ±5.1 Mg C km
-1
, respectively (Table 1). At
the parcel level, taking into account the SOC stocks in
the margins and the decreasing SOC concentrations
from margin to field, the SOC stocks were slightly
higher in the hedgerow-bordered fields compared to
the ‘ghost’ hedgerow-bordered fields and the fields
bordered by grass strips (Table 1). In the ‘ghost’
hedgerow-bordered fields, these built up SOC stocks
were completely gone (Table 1).
The total SOC stock in the hedgerow network and
its surrounding area up until 30 m in the fields was 66
030 ±7 175 Mg C in 1960. In 2016, the same area
stored 61 072 ±5 511 Mg C (19 809 ±2 152 Mg C
in the soil in and around still existing hedgerows and
41 263 ±5 073 Mg C in the soil in and around the
‘ghost’ hedgerows). Due to the hedgerow removal, 4
957 ±1 664 Mg C from the soil alone were released
back into the atmosphere.
Fig. 3 Changes in SOC concentrations (%) with distance from
the field margin for fields with hedgerow margins (he), ‘ghost’
hedgerow margins (i.e. cleared hedgerows, gh) and grass strips
(gr). Coloured lines show the overall trend in changes in SOC
concentrations within fields (based on linear mixed-effects
model predictions), with grey lines illustrating the trends for
individual fields (based on field-level linear model); The grey
zones indicate the 95% confidence intervals for the predictions.
Note that the distance on the x-axis is displayed on a log-scale
123
Agroforest Syst
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Fig. 4 Hedgerows present in 2016 and ‘ghost’ hedgerows—i.e.
cleared hedgerows since 1960—in the study area. Map showing
the location of the study area in the province of Antwerp in the
north of Belgium. The municipality of Turnhout is presented in
detail with indication of hedgerows and ‘ghost’ hedgerows on
the Aerial photograph of 2016. Hedgerow destruction is
displayed in detail in the table. Source topographical map and
Aerial photographs: Geografische Data-Infrastructuur and
Google Satellite (2018), respectively
123
Agroforest Syst
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Discussion
High SOC stocks in hedgerows, severe losses
in ‘ghost’ hedgerows
In the hedgerow field margins, SOC concentration and
stock were substantial and almost twice as high
compared to grass strips, whereas bulk density was
clearly lower (Fig. 2). SOC stocks are gradually built
up under hedgerows. Leaves, seeds and nuts, tree fine
roots, pruning residues and the herbaceous vegetation
growing in the hedgerows contribute to a higher input
of organic carbon to the soil compared to a treeless
field margin (Cardinael et al 2018; Peichl et al. 2006).
The carbon rich organic matter accumulates in the
hedgerows, where it is protected from water and wind
erosion (Brandle et al. 2004; Schoeneberger et al.
2012). In hedgerows, the microclimate conditions—
i.e. an increased soil moisture and lower temperatures
below the tree canopies in summer (Shi et al. 2018;
Vanneste et al. 2020a) result in slower carbon
decomposition compared to the adjacent croplands,
all contributing to higher SOC stocks under
hedgerows.
In the ‘ghost’ hedgerows, these built up SOC stocks
were almost completely gone over a period of
maximum 59 and minimum 24 years (Fig. 2). In fact,
the SOC concentration was clearly lower in the ‘ghost’
hedgerows compared to the grass strips. During
hedgerow clearing, the soil was probably heavily
disturbed and compacted by machinery to remove the
woody vegetation (Holden et al. 2019;Sa
´nchez et al.
2010), which was visible in the soil profile (i.e. more
compacted A-horizons, Table S1) and the higher bulk
density in ‘ghost’ hedgerows compared to hedgerows
and grass strips (Fig. 2). Such soil disturbance leads to
increased mineralisation and dissolution of the stored
soil carbon into the atmosphere. Moreover, these
compacted soils gradually lose the ability to store
carbon, as soil compaction causes soil degradation and
leaching of nutrients (Prentice et al. 2001).
SOC (legacy) gradients from (‘ghost’) hedgerow
into bordering fields
In the fields bordered by hedgerows, SOC concentra-
tions showed an exponential decay away from the
hedgerow margin (Fig. 3). The concentration surplus
in the adjacent fields thus decreased sharply towards
the field centre. This rather abrupt decay of SOC
concentration can be explained by at least two drivers.
Firstly, most of the tree litter falls straight down and
ends up in the close proximity of the hedgerows.
Moreover, with their windbreak function, hedgerows
slow down wind flow further reinforcing this pattern
because there is less horizontal air displacement
(Wiesmeier et al. 2018). Secondly, sandy soils—as
encountered in our study region—lack an inorganic
colloidal phase and are typified by low concentrations
of organic carbon and plant nutrients (Carlyle 1993).
Also, SOC in sandy soils is typically more labile than
that accumulated in the clay-rich soils, since few
interactions occur between fine mineral surfaces and
SOC (Dal Ferro et al. 2020). The soil organic matter
that is deposited in the field will thus not easily result
in elevated SOC concentrations as carbon will not be
bound easily and the soil capacity to preserve it is
limited (Bot and Benites 2005; Simonetti et al. 2017).
Table 1 SOC stocks to a depth of 23 cm for hedgerow
margins, ‘ghost’ hedgerow margins and grass strips and their
adjacent arable fields (including the field margin ?30 m from
boundary into parcel). SOC stocks are expressed in Mg carbon
per hectares ±standard deviation
Study system SOC stock
(Mg C ha
-1
)
Field margin Hedgerow
‘Ghost’ hedgerow
Grass strip (Control)
81.7 ±28.8
57.9 ±14.1
56.6 ±14.5
Arable field ?field margin Hedgerow
‘Ghost hedgerow’
Grass strip (Control parcel)
56.4 ±6.3
53.9 ±6.1
55.6 ±5.0
123
Agroforest Syst
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
This decreasing trend in SOC concentration from
the margin to the field was to a very limited extent still
detectable in fields bordered by ‘ghost’ hedgerows
(Fig. 3). However, the elevated SOC concentration in
the adjacent fields apparently disappeared quickly
after hedgerow removal, and was almost gone in a
period of maximum 59 years. With the removal of the
hedgerow trees, the seasonal litter inputs are taken
away and the built up SOC stocks dissolve again
accelerated by tillage practices to which the plough
layer is subjected.
SOC stocks in study area and losses due
to hedgerow clearing
In our studied agroforestry system ‘hedgerow-bor-
dered fields’, the bulk of the SOC surplus owing to the
presence of hedgerow trees is situated in the hedge-
rows themselves (Table 1). The additional SOC stocks
in the fields adjacent to the hedgerows (compared to
fields bordered by grass strips) was relatively low,
mostly because of the sharp exponential decline in
SOC concentrations when moving away from the
margin as the litter remains near the hedgerow owing
to wind attenuation (see 4.2). However, even the subtle
carbon enrichment in the plough layer in sandy soils of
our hedgerow-bordered fields is important from an
agricultural point of view. Within the soil system,
carbon controls the hydrologic, biological and chem-
ical soil functioning (Monreal et al. 2018) and this is
especially of importance in agriculture lands where
carbon is a key factor in the functioning and produc-
tivity of the soil system (Pausch and Kuzyakov 2018;
Pellegrini et al. 2018). Moreover, increasing SOC
concentration in arable lands is to be pursued under the
increasing frequency and intensity of droughts due the
changing climate, as a large concentration of soil
carbon contributes to the resistance and resilience of
an ecosystem to buffer the effects of droughts (Lei et al
2020).
The severe hedgerow removal in the study area over
the last 60 years can likely be attributed to land
consolidation practices of the early 700s in Belgium
(Fig. 4). These practices had the purpose to generate
larger fields that could be managed more efficiently
(Bazin and Schmutz 1994; Hermy and De Blust 1997).
The SOC stocks that took decades to build under the
hedgerow trees largely disappeared with the hedge-
rows. Approximately 5 000 Mg C was released back
into the atmosphere. Adding the carbon that was stored
in the aboveground biomass, carbon loss varies
between 8.737 and 33.030 Mg C (Van Den Berge
2021), representing almost 20% of the current carbon
storage in the forests of Turnhout (i.e. 175 628 Mg C,
based on the average numbers for forest C storage
published by Arets et al. 2020). The total carbon loss
due to the hedgerow removal equals emissions of 32
066 to 121 221 Mg CO
2
, respectively, corresponding
to half of the yearly amount of CO2 emissions in the
municipality (i.e. 240 000 Mg CO
2
, IOK 2020).
As we have recorded such a major hedgerow
decline over this short period, probably many more
hedgerows have disappeared before 1960. Our esti-
mated soil carbon loss due to hedgerow removal in the
study area is therefore undoubtedly an underestima-
tion. How fast the SOC stocks are built up again after
planting new hedgerows, remains to be quantified.
However, observed trends in SOC changes following
reforestation in agricultural fields tend to be weak and
imprecise (England et al. 2016; Prior et al. 2015). The
building up of a new equilibrium in 0–30 cm SOC
stock may take hundreds of years to reach, especially
when considering inputs from decomposition of
coarse tree roots (Ba
´rcena et al. 2014; Hibbard et al.
2003; Poeplau et al. 2011). It would be interesting for
future research to set-up time series to determine
build-up rates of SOC under newly planted hedgerows
and the rate of SOC loss when hedgerows are
removed. Considering the deeper soil layers to
estimate SOC stocks of (‘ghost’) hedgerows would
be interesting as well, as the SOC stocks might
preserve longer where tillage practices do not affect
the mineralisation process.
Published SOC stocks in agroforestry systems in
the temperate zone vary greatly (Table 2). In agro-
forestry systems with hedgerows / windbreaks, SOC
stocks are mainly elevated under or near the hedge-
rows themselves and less in the adjacent fields due to
the hedgerows’ wind shield function (Table 2).
Hedgerows are thus different systems than the often-
studied tree rows in temperate agroforestry systems. In
tree row bordered-fields, litter is taken into the fields
over a distance equal to the tree height (e.g. Cardinael
et al. 2015; Pardon et al. 2017), resulting in higher
litter inputs compared to hedgerow-bordered fields,
but lower SOC stocks in the tree rows themselves
(Table 2). In agroforestry systems with alley cropping,
there are several tree rows per field resulting in
123
Agroforest Syst
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Table 2 Overview of published SOC stock values in agro-
forestry systems in the temperate climate zone summarized
from a thorough literature review. Study area, climate and soil
specifications are clarified for each study as well as the
sampled soil depth and whether the study reports about
measured data (‘original data’) or about figures from literature
(see the reference list in the original studies for more
information). The agroforestry system is specified in the most
uniform way possible for each study to facilitate benchmark-
ing. SOC stocks are expressed in Mg carbon per hectares ±s-
tandard deviation when available
Study area Climate Soil Soil
depth
(cm)
Study Original
data /
based on
literature
Agroforestry system SOC stock
(Mg C ha
-1
)
Belgium Temperate
maritime
Soils ranged from
silt to (sandy)
loam
0–23 Pardon
et al.
(2017
Original
data
-Agroforestry parcel (arable
field bordered by tree row;
tree row not included in
calculation; 30 m from
boundary into parcel)
-45.1
-Control parcel (arable field
without trees; 30 m from
boundary into parcel)
-39.8
France Sub-humid
Mediterranean
Silty and
carbonated deep
alluvial
Fluvisols
0–29 Cardinael
et al.
(2015;
2018)
Original
data
-Tree row in alley cropping
system
-52.8 ±1.4
-Agroforestry parcel (arable
field with cultivated inter-
rows of 11 m ?tree
rows)
-40.3 ±0.5
-Control parcel (arable field
without trees)
-35.8 ±0.2
France Temperate
maritime
Silty loam
Luvisols,
carbonated silty
clay Luvisols
0–30 Cardinael
et al.
(2017)
Original
data
-Tree row in alley cropping
system
-49.8 ±1.9
-Agroforestry parcel (arable
field with cultivated inter-
rows of 26–29 m ?tree
rows of 2 m)
-43.7 ±1.3
-Control parcel (arable field
without trees)
-42.5 ±2.0
France Temperate
maritime
Carbonated silty
clay Luvisols
0–20 Cardinael
et al.
(2017)
Original
data
-Tree row in alley cropping
system
-67.7 ±1.1
-Agroforestry parcel (arable
field with cultivated inter-
rows of 14 m ?tree rows
of 2 m)
-60.9 ±0.9
-Control parcel (arable field
without trees)
-42.1 ±0.8
France Sub-humid
Mediterranean
Deep sandy loam
alluvial
Fluvisols
0–30 Cardinael
et al.
(2017)
Original
data
-Tree row in alley cropping
system
-48.8 ±1.7
-Agroforestry parcel (arable
field with cultivated inter-
rows of 10–13 m ?tree
rows of 2 m)
-41.6 ±0.9
-Control parcel (arable field
without trees)
-38.3 ±2.0
123
Agroforest Syst
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Table 2 continued
Study area Climate Soil Soil
depth
(cm)
Study Original
data /
based on
literature
Agroforestry system SOC stock
(Mg C ha
-1
)
France Temperate
maritime
Deep weathering
alteration
structures
overlain by
reworked
Weichselian
Aeolian loam
0–200 Follain
et al.
(2007)
Original
data
Ancient bocage landscape
in hilled landscape:
-Strip of 5 m of each side of
hedges
-159.4
-Strip of 10 m of each side
of hedges
-154.4
-Strip of 20 m of each side
of hedges
-146.8
-Landscape scale -141.4
UK Temperate
maritime
Stagnogleys
(slowly
permeable,
seasonally wet,
acid loam or
clay soil) or
brown earth
(freely draining,
slightly acid
loam soil)
0–15 Ford et al.
(2019)
Original
data
livestock-grazed pasture
(4.8 m from boundary
into parcel) adjacent to
-biotic (hedgerow) field
boundary
-66.2 ±0.9
-abiotic (stone wall) field
boundary
-72.4 ±2.5
-abiotic (fence) field
boundary
-72.0 ±2.8
UK Temperate
maritime
Average soil clay
content 23%
0–30 Falloon
et al.
(2004)
Based on
literature
-Hedge ?grass margin
-Cultivated arable land
(winter wheat)
-76.8
-46.2
Temperate
zone
Temperate Various 0–30 Shi et al.
(2018)
Based on
literature
-Agroforestry parcel in
alley cropping
-17.7
-Agroforestry parcel as
homegardens
-30.2
-Agroforestry parcel in
silvopastures
-36.8
-Agroforestry parcel with
windbreaks
-66.5
Various Various Various 0–30 Van
Vooren
et al.
(2017)
Based on
literature
-SOC stock within
hedgerow compared to
arable field
-22%
surplus
-SOC stock in the fields
next to hedgerows
compared to arable fields
(impact on stock extends
into the parcel until a
distance of
4.3 9hedgerow height)
-6%
surplus
Remark: meta-analysis not
for hedgerows s.s. (tree
rows in boundary planting
and alley cropping
included ?original data
for 6 hedgerows in
boundary planting in
Belgium)
123
Agroforest Syst
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
significantly higher SOC stocks compared to parcels
without trees (Table 2). In general, there is consider-
able uncertainty about the actual size of the SOC stock
in agroforestry systems. More research on SOC stocks
in agroforestry systems is urgently needed in order to
cover different agroforestry system, tree species, soil
type, soil management practice, cropping system and
climatic zone. Moreover, estimating both soil carbon
stocks and biomass stocks in agroforestry systems
would allow for a more holistic approach regarding the
climate change mitigation value of agroforestry sys-
tems. Also the comparison with other land use
changes, such as conversion of permanent grasslands
to arable fields, would be interesting to explore.
Permanent grasslands clearly have higher SOC stocks
compared to cropland. E.g. in Germany, croplands
store on average 61 ±25 Mg ha
–1
(in 0–30 cm soil)
while permanent grasslands store significantly more
(88 ±32 Mg ha
–1
) (Poeplau et al. 2020).
Hedgerows and other forms of agroforestry do not
only contribute to climate change mitigation due to the
sequestration of carbon in the soil and in their biomass,
they also increase a range of other regulating ecosys-
tem services in the agricultural landscape (Brandle
et al. 2004; Kay et al. 2019; Schoeneberger et al.
2012). Locally, they modify microclimate via buffer-
ing soil and air temperature, soil moisture and relative
air humidity (Shi et al. 2018; Vanneste et al. 2020a)
and reduce the risks of droughts via increasing water
infiltration and water storage under the trees (Ander-
son et al. 2009); offering decent climate change
adaptation measures in the countryside (Matocha et al.
2012; Stavi and Lal 2013). Also, their presence in the
landscape enhances biodiversity in various ways (e.g.
Dainese et al. 2015;S
ˇa
´lek et al. 2018; Van Den Berge
et al. 2018; Vanneste et al 2020b and many more). The
implementation of a strict hedgerow conservation and
restoration policy would thus not only be an effective
measure in climate change mitigation, it would also
generate significant co-benefits for local ecosystems
and biodiversity.
Conclusion
In hedgerow field margins, significant amounts of
carbon are stored in the soil, forming a main carbon
sink in our study area. However, the decline in the
hedgerow network in our study region was severe over
the last 60 years (1960–2016): 70% of the hedgerows
present in 1960 were cleared and 126 km of ‘ghost’
hedgerows were created. ‘Ghost’ hedgerows show no
remaining traces of the built-up SOC stocks in the field
margins. Hedgerow destruction since the 1960s
resulted in a lost carbon sequestration potential of
almost 5000 Mg C in the top 23 cm soils in our
5600 ha study area. Our results show that, in terms of
SOC stock management in agricultural areas, preserv-
ing existing hedgerows is an important climate change
mitigation measure to keep the stored carbon fixated in
the ground.
Acknowledgements The authors are grateful to Paul Pardon,
Robbe De Beelde, Kris Ceunen and Luc Willems for their help
during the fieldwork and to all the farmers who allowed us to
take soil samples in their fields. We also thank Inge Vermeulen
and Dirk Vandenbussche for providing access to the historical
maps of Turnhout and ArcMap, Luc Willems and Greet De
bruyn for conducting the chemical analyses of the soil samples,
and Jonathan Janssens for layouting the figures.
Authors’ contributions All authors contributed to the study
conception and design. Material preparation, data collection and
analysis were performed by Sanne Van Den Berge, Pieter
Vangansbeke, Thomas Vanneste and Fien Vos, under the
supervision of Lander Baeten and Kris Verheyen. The first draft
of the manuscript was written by Sanne Van Den Berge and all
authors commented on previous versions of the manuscript. All
authors read and approved the final manuscript.
Funding Financial support was provided by the European
Research Council to KV through the PASTFORWARD project
(ERC Consolidator Grant no. 614839) and to PV through the
FORMICA project (ERC Starting Grant no. 757833). TV was
supported by the Special Research Fund (BOF) from Ghent
University (01N02817).
Code availability Not applicable.
Availability of data and material Access to the data used and
analysed in this study will be given by the authors upon personal
request.
Declarations
Conflicts of interest The authors declare that they have no
known competing financial interests or personal relationships
that could have appeared to influence the work reported in this
paper.
123
Agroforest Syst
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
References
Aertsens J, De Nocker L, Gobin A (2013) Valuing the carbon
sequestration potential for European agriculture. Land Use
Policy 31:584–594
Albrecht A, Kandji ST (2003) Carbon sequestration in tropical
agroforestry systems. Agric Ecosyst Environ 99:15–27
Anderson SH, Udawatta RP, Seobi T, Garrett HE (2009) Soil
water content and infiltration in agroforestry buffer strips.
Agroforest Syst 75:5–16
Arets EJMM, van der Kolk JWH, Hengeveld GM, Lesschen JP,
Kramer H, Kuikman PJ, Schelhaas MJ (2020) Greenhouse
gas reporting of the LULUCF sector in the Netherlands.
Methodological background, update 2020. WOt Technical
report 168. Statutory Research Tasks Unit for Nature & the
Environment (WOT Natuur & Milieu), Wageningen UR,
Wageningen. https://edepot.wur.nl/517340
Baltensperger BH (1987) Hedgerow distribution and removal in
nonforested regions of the Midwest. J Soil Water Conserv
42:60–64
Ba
´rcena TG, Gundersen P, Vesterdal L (2014) Afforestation
effects on SOC in former cropland: oak and spruce
chronosequences resampled after 13 years. Glob Chang
Biol 20:2938–2952
Barton K (2019) MuMIn: Multi-Model Inference. R package
version 1.43.6. https://CRAN.R-project.org/package=
MuMIn
Baudry J, Bunce RGH, Burel F (2000) Hedgerows: an interna-
tional perspective on their origin, function and manage-
ment. J Environ Manage 60:7–22
Bazin P, Schmutz T (1994) La mise en place de nos bocages en
Europe et leur de
´clin. Revue forestrie
`re franc¸aise, Agro-
ParisTech, Nancy, France
Black K, Green S, Mullooley G, Poveda A (2012) Carbon
Sequestration by Hedgerows in the Irish Landscape.
Towards a national hedgerow biomass inventory for the
LULUCF sector using LiDAR Remote Sensing. Climate
Change Research Programme (CCRP) 2007–2013 Report
Series No. 32. Environmental Protection Agency (EPA),
Wexford, Ireland
Bot A and Benites J (2005) The importance of soil organic
matter. FAO soils bulletin 80. Publishing Management
Service, Information Division, FAO, Italy
Brandle JR, Hodges L, Zhou XH (2004) Windbreaks in North
American agricultural systems. Agrofor Syst 61:65–78
Buis J (1985) Historia Forestis: Nederlandse bosgeschiedenis.
HES Uitgevers, Utrecht
Burny J, Mennen V, Vanlook W (2013) Koersel. Van Neusen-
berg tot Spiekelspade: het historische landschap in het licht
van de plaatsnamen. Natuurpunt, Beringen, Belgium
Cardinael R, Chevallier T, Barthe
`s BG, Saby NPA, Parent T,
Dupraz C, Bernoux M, Chenu C (2015) Impact of alley
cropping agroforestry on stocks, forms and spatial distri-
bution of soil organic carbon—a case study in a Mediter-
ranean context. Geoderma 259–260:288–299
Cardinael R, Chevallier T, Cambou A, Be
´ral C, Barthe
`s BG,
Dupraz C, Durand C, Kouakoua E, Chenu C (2017)
Increased soil organic carbon stocks under agroforestry: a
survey of six different sites in France. Agr Ecosyst Environ
236:243–255
Cardinael R, Guenet B, Chevallier T, Dupraz C, Cozzi T, Chenu
C (2018) High organic inputs explain shallow and deep
SOC storage in a long-term agroforestry system—com-
bining experimental and modeling approaches. Biogeo-
sciences 15:297–317
Carlyle JC (1993) Organic carbon in forested sandy soils:
properties, processes, and the impact of forest manage-
ment. NZ J Forest Sci 23(3):390–402
Commission E (2013) Regulation (EU) No 1305/2013 on sup-
port for rural development by the European Agricultural
Fund for Rural Development (EAFRD) and repealing
Council Regulation (EC) No 1698/2005. Off J Eur Union
1698:487–548
Dainese M, Luna DI, Sitzia T, Marini L (2015) Testing scale-
dependent effects of seminatural habitats on farmland
biodiversity. Ecol Appl 25(6):1681–1690
Dal Ferro N, Piccoli I, Berti A, Polese R, Morari F (2020)
Organic carbon storage potential in deep agricultural soil
layers: Evidence from long-term experiments in northeast
Italy. Agr Ecosyst Environ 300:106967
De Stefano A, Jacobson MG (2018) Soil carbon sequestration in
agroforestry systems: a meta-analysis. Agroforest Syst
92:285–299
Dignac M-F, Derrien D, Barre
´P, Barot S et al (2017) Increasing
soil carbon storage: mechanisms, effects of agricultural
practices and proxies. A review Agron Sustain Dev 37:14
England JR, Paul KI, Cunningham SC, Madhavan D, Baker TG,
Read Z, Wilson BR, Cavagnaro TC, Lewis T, Perring MP,
Herrmann T, Polglase PJ (2016) Previous land use and
climate influence differences in soil organic carbon fol-
lowing reforestation of agricultural land with mixed-spe-
cies environmental plantings. Agric Ecosyst Environ
227:61–72
ESRI (2018) ArcGIS Desktop: Release 10.6.1 Redlands. Envi-
ronmental Systems Research Institute, CA
Falloon P, Powlson D, Smith P (2004) Managing field margins
for biodiversity and carbon sequestration: a Great Britain
case study. Soil Use and Manag 20:240–247
Follain S, Walter C, Legout A, Lemercier B, Dutin G (2007)
Induced effects of hedgerow networks on soil organic
carbon storage within an agricultural landscape. Geoderma
142:80–95
Ford H, Healey JR, Webb B, Pagella TF, Smith AR (2019) How
do hedgerows influence soil organic carbon stock in live-
stock-grazed pasture? Soil Use Manag. https://doi.org/10.
1111/sum.12517
Google Satellite (2018) Map data Ó2018 Google
Hart K, Allen B, Keenleyside C, Nanni S, Mare
´chal A, Paquel
K, Nesbit M, Ziemann J (2017) Research for agri com-
mittee - the consequences of climate change for EU agri-
culture. Follow-Up To the COP21 - Un Paris Climate
Change Conference
Hermy M and De Blust G (1997) Punten en lijnen in het land-
schap. Stichting Leefmilieu, Schuyt & co, Van de Wiele,
Natuurreservaten, WWF, Instituut voor Natuurbehoud
Hibbard KA, Schimel DS, Archer S, Ojima DS, Parton W (2003)
Grassland to woodland transitions: integrating changes in
landscape structure and biogeochmistry. Ecol Appl
13:911–926
Holden J, Grayson RP, Berdeni D, Bird S, Chapman PJ,
Edmondson JL, Firbank LG, Helgason T, Hodson ME,
123
Agroforest Syst
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Hunt SFP et al (2019) The role of hedgerows in soil
functioning within agricultural landscapes. Agric Ecosyst
Environ 273:1–12
Hossain MF, Chen W, Zhang Y (2015) Bulk density of mineral
and organic soils in the Canada’s arctic and sub-arctic.
Information processing in agriculture 2:183–190
IOK (2020) Intercommunale Ontwikkelingsmaatschappij voor
de Kempen. Url: iok.be
IPCC (1990) In: Houghton JT, Jenkins GJ, Ephraums JJ (Eds.),
Climate Change. Cambridge University Press, Cambridge
IPCC (1996) Climate change 1995 The science of climate
change. Cambridge University Press, Cambridge
Jacobson MC, Charlson RJ, Rodhe H, Orians GH (2000) Earth
system science: from biogeochemical cycles to global
change. International Geophysics Series72 New York:
Academic Press
Jobba
´gy EG, Jackson RB (2000) The vertical distribution of soil
organic carbon and its relation to climate and vegetation.
Ecol Appl 10:423–436
Kay S, Rega C, Moreno G, den Herder M, Palma JHN, Borek R,
Crous-Duran J, Freese D, Giannitsopoulos M et al (2019)
Agroforestry creates carbon sinks whilst enhancing the
environment in agricultural landscapes in Europe. Land
Use Policy 83:581–593
Kint V, Geudens G, den Ouden J (2010) Bos op arme gronden.
In: den Ouden J, Muys B, Mohren F, Verheyen K (eds.)
Bosecologie en bosbeheer. Acco[s.l.]
KMI (2019) Koninklijk Meteorologisch Instituut van Belgie
¨,
Klimaatatlas. https://www.meteo.be/nl/klimaat/
klimaatatlas
Lal R (2004a) Soil carbon sequestration impacts on global cli-
mate change and food security. Science 304:1623–1627
Lal R (2004b) Soil carbon sequestration in natural and managed
tropical forest ecosystems. J Sustain For 21:1–30
Lei T, Feng J, Zheng C, Li S, Wang Y, Wu Z, Lu J, Kan G, Shao
C, Jia J, Cheng H (2020) Review of drought impacts on
carbon cycling in grassland ecosystems. Earth Sci, Front.
https://doi.org/10.1007/s11707-019-0778-4
Lenka NK, Dass A, Sudhishri S, Patnaik US (2012) Soil carbon
sequestration and erosion control potential of hedgerow
and grass filter strips in sloping agricultural lands of eastern
India. Agric Ecosyst Environ 158:31–40
Litza K, Diekmann M (2020) The effect of hedgerow density on
habitat quality distorts species-area relationships and the
analysis of extinction debts in hedgerows. Landscape Ecol.
https://doi.org/10.1007/s10980-020-01009-5
Marshall EJP, Moonen AC (2002) Field margins in northern
Europe: their functions and interactions with agriculture.
Agric Ecosyst Environ 89:5–21
Matocha J, Schroth G, Hills T, Hole D (2012) Integrating cli-
mate change adaptation and mitigation through agro-
forestry and ecosystem conservation. In: Nair PKR, Garrity
D (eds) Agroforestry—the future of global land use.
Springer, Dordrecht, pp 105–126
Monreal NLR, Lo
´pez-Vicente M, Romero MEN, Ojanguren R,
Ada
´n JAL, Errea P, Bellido NP (2018) Catchment based
hydrology under post farmland abandonment scenarios.
Cuadernos Invest. Geogr./Geogr. Res Lett 44:503–534
Montagnini F, Nair PKR (2004) Carbon sequestration: an
underexploited environmental benefit of agroforestry sys-
tems. Agrofor Syst 61(2):281–295
Nair PKR, Kumar BM, Nair VD (2009) Agroforestry as a
strategy for carbon sequestration. J Plant Nutr Soil Sci
172:10–23
Nair PKR, Nair VD, Kumar BM, Showalter JM (2010) Carbon
sequestration in agroforestry systems. In: Sparks DL (ed)
Advances in agronomy, vol 108. Academic Press, New
York, pp 237–307
Nakagawa S, Schielzeth H (2013) A general and simple method
for obtaining R2 from generalized linear mixed-effects
models. Methods Ecol Evol 4:133–142
Pardon P, Reubens B, Reheul D, Mertens J, De Frenne P,
Coussement T, Janssens P, Verheyen K (2017) Trees
increase soil organic carbon and nutrient availability in
temperate agroforestry systems. Agr Ecosyst Environ
247:98–111
Pausch J, Kuzyakov Y (2018) Carbon input by roots into the
soil: quantification of rhizodeposition from root to
ecosystem scale. Global Change Biol 24:1–12
Peichl M, Thevathasan NV, Gordon AM, Huss J, Abohassan RA
(2006) Carbon sequestration potentials in temperate tree-
based intercropping systems, southern Ontario, Canada.
Agroforest Syst 66:243–257
Pellegrini AF, Ahlstro
¨m A, Hobbie SE, Reich PB, Nieradzik LP,
Staver AC, Jackson RB (2018) Fire frequency drives dec-
adal changes in soil carbon and nitrogen and ecosystem
productivity. Nature 553:194
Pinheiro J, Bates D, DebRoy S, Sarkar D, R Core Team (2018)
_nlme: Linear and Nonlinear Mixed Effects Models_. R
package version 3.1–137,\URL: https://CRAN.R-project.
org/package=nlme[
Poeplau C, Don A, Vesterdal L, Leifeld J, VanWesemael B,
Schumacher J, Gensoir A (2011) Temporal dynamics of
soil organic carbon after land-use change in the temperate
zone–carbon response functions as a model approach. Glob
Chang Biol 17:2415–2427
Poeplau C, Jacobs A, Don A, Vos C, Schneider F, Wittnebel M,
Tiemeyer B, Heidkamp A, Prietz R, Flessa H (2020) Stocks
of organic carbon in German agricultural soils—Key
results of the first comprehensive inventory. J Plant Nutr
Soil Sci 183:665–681
Prentice C, Farquhar G, Fasham M, Goulden M, Heimann M,
Jaramillo V, Kheshgi H, Que
´re
´CL, Scholes R, Wallace D
(2001) The carbon cycle and atmospheric CO2. In:
Houghton et al. (eds) Climate Change 2001: The Scientific
Basis: Contribution of WGI to the Third Assessment
Report of the IPCC. Cambridge University Press, New
York, pp 185–237
Prior LD, Paul KI, Davidson NJ, Hovenden MJ, Nichols SC,
Bowman DJMA (2015) Evaluating carbon storage in
restoration plantings in the Tasmanian Midlands, a highly
modified agricultural landscape. Rangel J 37:477–488
R Core Team (2019) R: A language and environment for sta-
tistical computing. R Foundation for Statistical Comput-
ing, Vienna, Austria
R Core Team (2020) R: A language and environment for sta-
tistical computing. R Foundation for Statistical Comput-
ing, Vienna, Austria
S
ˇa
´lek M, Hula V, Kipson M, Dan
ˇkova
´R, Niedobova
´J, Gamero
A (2018) Bringing diversity back to agriculture: Smaller
fields and non-crop elements enhance biodiversity in
intensively managed arable farmlands. Ecol Ind 90:65–73
123
Agroforest Syst
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Sa
´nchez IA, Lassaletta L, McCollin D, Bunce RGH (2010)
Agroforest Syst 78:13–25
Schlesinger WH, Andrews JA (2000) Soil respiration and the
global carbon cycle. Biogeochemistry 48:7–20
Schoeneberger M, Bentrup G, de Gooijer H, Soolanayakana-
hally R, Sauer T, Brandle J, Zhou X, Current D (2012)
Branching out: agroforestry as a climate change mitigation
and adaptation tool for agriculture. J Soil Water Conserv
67:128A-136A
Sevenant M, Menschaert J, Couvreur M, Ronse A, Antrop M,
Geypens M, Hermy M, De Blust G (2002) Ecodistricten:
Ruimtelijke eenheden voor gebiedsgericht milieubeleid in
Vlaanderen. Deelrapport II: Afbakening van ecodistricten
en ecoregio’s: Verklarende teksten. Studieopdracht in het
kader van actie 134 van het Vlaams Milieubeleidsplan
1997–2001. In opdracht van het Ministerie van de Vlaamse
Gemeenschap, Administratie Milieu, Natuur, Land- en
Waterbeheer
Shi L, Feng W, Xu J, Kuzyakov Y (2018) Agroforestry systems:
meta-analysis of soil carbon stocks, sequestration pro-
cesses, and future potentials. Land Degrad Dev
29:3886–3897
Shorohova E, Kuuluvainen T, Kangur A, Jo
˜giste K (2009)
Natural stand structures, disturbance regimes and succes-
sional dynamics in the Eurasian boreal forests: a review
with special reference to Russian studies. Ann For Sci
66:201
Simonetti G, Francioso O, Dal Ferro N, Nardi S, Berti A, Morari
F (2017) Soil porosity in physically separated fractions and
its role in SOC protection. J Soils Sediments 17:70–84
Sollins P, Swanston C, Kramer M (2007) Stabilization and
destabilization of soil organic matter—a new focus. Bio-
geochemistry 85:1–7
Stavi I, Lal R (2013) Agroforestry and biochar to offset climate
change: a review. Agron Sustain Dev 33:81–96
Stockmann U, Adams MA, Crawford JW, Field DJ, Hena-
kaarchchi N, Jenkins M et al (2013) The knowns, known
unknowns and unknowns of sequestration of soil organic
carbon. Agric Ecosyst Environ 164:80–99
Tavernier R and Mare
´chal R (1972) Carte des Associations des
Sols 1/500.000. NGI, Brussels
UNFCCC (2015) Paris agreement, conference of the parties on
its twenty-first session. FCCC/CP/2015/L.9/Rev.1
Van Den Berge S (2021) Role of hedgerow systems for biodi-
versity and ecosystem services in agricultural landscapes.
PhD thesis, Ghent University, Ghent, Belgium
Van Den Berge S, Baeten L, Vanhellemont M, Ampoorter E,
Proesmans W, Eeraerts M, Hermy M, Smagghe G, Ver-
meulen I, Verheyen K (2018) Species diversity, pollinator
resource value and edibility potential of woody networks in
the countryside in northern Belgium. Agric Ecosyst Envi-
ron 259:119–126
Van Den Berge S, Vangansbeke P, Calders K, Vanneste T,
Baeten L, Verbeeck H, Krishna Moorthy SP, Verheyen K
(2021) Biomass Expansion Factors for Hedgerow-Grown
Trees Derived from Terrestrial LiDAR. Res, Bioenerg.
https://doi.org/10.1007/s12155-021-10250-y
Van Elst Ph (1916) Landbouwontginningen in de Kempen.
Drukkerij L, Braeckmans, Brecht
Vanneste T, Govaert S, Spicher F, Brunet J, Cousins SAO,
Decocq G, Diekmann M et al (2020a) Contrasting micro-
climates among hedgerows and woodlands across tem-
perate Europe. Agric For Meteorol 281:107818
Vanneste T, Govaert S, De Kesel W, Van Den Berge S, Van-
gansbeke P, Meeussen C, Brunet J et al (2020b) Plant
diversity in hedgerows and road verges across Europe.
J Appl Ecol 57(7):1244–1257
Verboven H, Verheyen K, Hermy M (2004) Bos en hei in het
Land van Turnhout. Een bijdrage tot de historische
ecologie. Eindrapport in opdracht van het Ministerie van de
Vlaamse gemeenschap, Monumenten & Landschappen en
het Vlaams Instuut voor het Onroerend Erfgoed
Vermeulen SJ, Campbell BM, Ingram JSI (2012) Climate
change and food systems. Annu Rev Environ Resour
37:195–222. https://doi.org/10.1146/annurev-environ-
020411-130608
Wickham H (2016) Ggplot2: elegant graphics for data analysis.
Springer-Verlag, New York
Wiesmeier M, Lungu M, Cerbari V, Boincean B, Hu
¨bner R,
Ko
¨gel-Knabner I (2018) Rebuilding soil carbon in degra-
ded steppe soils of Eastern Europe: the importance of
windbreaks and improved cropland management. Land
Degrad Dev 29:875–883. https://doi.org/10.1002/ldr.2902
Publisher’s Note Springer Nature remains neutral with
regard to jurisdictional claims in published maps and
institutional affiliations.
123
Agroforest Syst
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
1.
2.
3.
4.
5.
6.
Terms and Conditions
Springer Nature journal content, brought to you courtesy of Springer Nature Customer Service Center GmbH (“Springer Nature”).
Springer Nature supports a reasonable amount of sharing of research papers by authors, subscribers and authorised users (“Users”),
for small-scale personal, non-commercial use provided that all copyright, trade and service marks and other proprietary notices are
maintained. By accessing, sharing, receiving or otherwise using the Springer Nature journal content you agree to these terms of use
(“Terms”). For these purposes, Springer Nature considers academic use (by researchers and students) to be non-commercial.
These Terms are supplementary and will apply in addition to any applicable website terms and conditions, a relevant site licence or
a personal subscription. These Terms will prevail over any conflict or ambiguity with regards to the relevant terms, a site licence or
a personal subscription (to the extent of the conflict or ambiguity only). For Creative Commons-licensed articles, the terms of the
Creative Commons license used will apply.
We collect and use personal data to provide access to the Springer Nature journal content. We may also use these personal data
internally within ResearchGate and Springer Nature and as agreed share it, in an anonymised way, for purposes of tracking,
analysis and reporting. We will not otherwise disclose your personal data outside the ResearchGate or the Springer Nature group of
companies unless we have your permission as detailed in the Privacy Policy.
While Users may use the Springer Nature journal content for small scale, personal non-commercial use, it is important to note that
Users may not:
use such content for the purpose of providing other users with access on a regular or large scale basis or as a means to
circumvent access control;
use such content where to do so would be considered a criminal or statutory offence in any jurisdiction, or gives rise to civil
liability, or is otherwise unlawful;
falsely or misleadingly imply or suggest endorsement, approval , sponsorship, or association unless explicitly agreed to by
Springer Nature in writing;
use bots or other automated methods to access the content or redirect messages
override any security feature or exclusionary protocol; or
share the content in order to create substitute for Springer Nature products or services or a systematic database of Springer
Nature journal content.
In line with the restriction against commercial use, Springer Nature does not permit the creation of a product or service that creates
revenue, royalties, rent or income from our content or its inclusion as part of a paid for service or for other commercial gain.
Springer Nature journal content cannot be used for inter-library loans and librarians may not upload Springer Nature journal
content on a large scale into their, or any other, institutional repository.
These terms of use are reviewed regularly and may be amended at any time. Springer Nature is not obligated to publish any
information or content on this website and may remove it or features or functionality at our sole discretion, at any time with or
without notice. Springer Nature may revoke this licence to you at any time and remove access to any copies of the Springer Nature
journal content which have been saved.
To the fullest extent permitted by law, Springer Nature makes no warranties, representations or guarantees to Users, either express
or implied with respect to the Springer nature journal content and all parties disclaim and waive any implied warranties or
warranties imposed by law, including merchantability or fitness for any particular purpose.
Please note that these rights do not automatically extend to content, data or other material published by Springer Nature that may be
licensed from third parties.
If you would like to use or distribute our Springer Nature journal content to a wider audience or on a regular basis or in any other
manner not expressly permitted by these Terms, please contact Springer Nature at
onlineservice@springernature.com
A preview of this full-text is provided by Springer Nature.
Content available from Agroforestry Systems
This content is subject to copyright. Terms and conditions apply.
