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Eutrophication assessments in water management to quantify nutrient loads and identify mitigating measures seldom include the contribution from horse facilities. This may be due to lack of appropriate methods, limited resources, or the belief that the impact from horses is insignificant. However, the recreational horse sector is growing, predominantly in multi-functional peri-urban landscapes. We applied an ecosystem management approach to quantify nutrient loads from horse facilities in the Stockholm Region, Sweden. We found that horses increased the total loads with 30–40% P and 20–45% N, with average area-specific loads of 1.2 kg P and 7.6 kg N ha ⁻¹ year ⁻¹ . Identified local risk factors included manure management practices, trampling severity, soil condition and closeness to water. Comparisons of assessment methods showed that literature standard values of area-specific loads and water runoff may be sufficient at the catchment level, but in small and more complex catchments, measurements and local knowledge are needed.
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RESEARCH ARTICLE
Managing multi-functional peri-urban landscapes: Impacts
of horse-keeping on water quality
Linda Kumblad , Mona Petersson, Helena Aronsson,
Patrik Dinne
´tz, Lisbet Norberg, Camilla Winqvist, Emil Rydin,
Monica Hammer
Received: 4 May 2023 / Revised: 25 September 2023 / Accepted: 16 October 2023
Abstract Eutrophication assessments in water
management to quantify nutrient loads and identify
mitigating measures seldom include the contribution from
horse facilities. This may be due to lack of appropriate
methods, limited resources, or the belief that the impact
from horses is insignificant. However, the recreational
horse sector is growing, predominantly in multi-functional
peri-urban landscapes. We applied an ecosystem
management approach to quantify nutrient loads from
horse facilities in the Stockholm Region, Sweden. We
found that horses increased the total loads with 30–40% P
and 20–45% N, with average area-specific loads of 1.2 kg P
and 7.6 kg N ha
-1
year
-1
. Identified local risk factors
included manure management practices, trampling
severity, soil condition and closeness to water.
Comparisons of assessment methods showed that
literature standard values of area-specific loads and water
runoff may be sufficient at the catchment level, but in small
and more complex catchments, measurements and local
knowledge are needed.
Keywords Eutrophication Land use modelling
Local measures Nutrient load assessment
Water runoff modelling
INTRODUCTION
Eutrophication due to excessive inputs of nitrogen (N) and
phosphorus (P) is one of the most serious environmental
threats for many lakes and coastal areas, including Lake
Ma
¨laren (Sweden) and the Baltic Sea (Boesch et al. 2006;
Elmgren et al. 2015; HELCOM 2018a,b; Drakare et al.
2022; Vigouroux and Destouni 2022; VISS 2022). The
nutrients originate from a range of different sources in
different types of land use, and are often higher in areas
with much arable land and high animal density (Hong et al.
2012,2017; Svanba
¨ck et al. 2019). Common nutrient
abatement measures to mitigate eutrophication are
improved urban wastewater treatment, improved manure
management and reduction in fertilizer application in
agriculture (Hong et al. 2017; McCrackin et al. 2018a;
Andersson et al. 2022).
Mitigation measures according to the Water Framework
Directive should involve stakeholders and be based on a
catchment perspective (EC 2000). Hence, measures must
be adapted to local circumstances, where changes in land
use may require the development of local capacity building
and management strategies. One important nutrient source
is the peri-urban transition zone characterized by a diverse
and fragmented land use and an increasing demand for
recreational ecosystem services (Antrop 2004; Orsini
2013). In the peri-urban areas, agriculture for food pro-
duction is replaced with recreational land use, including
allotment gardens, golf courses and equestrian facilities
(Bomans et al. 2011; Elga
˚ker 2012; Zasada et al. 2013;
Plieninger et al. 2016).
In this study, we focus on the impact from horse facilities
on water quality. The horse sector in European and North
American countries is growing. In EU, the number of
equestrians increases with approximately 5% per year, and
currently, there are at least 6 million hobby and sport horses
in Europe (European Horse Network 2023). In Sweden, the
number of horses exceeds the number of dairy cows
(Swedish Board of Agriculture 2022). Around 76% of the
approximately 350 000 horses are kept in peri-urban and
urban environments (Swedish Board of Agriculture 2017). In
contrast to livestock management, the nutrient load from
horse activities has only been included in management plans
and action programmes in Sweden to a very limited degree,
ÓThe Author(s) 2023
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https://doi.org/10.1007/s13280-023-01955-9
but studies indicate that nutrient leakage from horse pad-
docks can have negative impact on water quality (Airaksinen
et al. 2007; Parvage et al. 2011). There are management
differences between livestock husbandry and horse-keeping
that may affect nutrient load (Hammer et al. 2017). Cattle are
grazing in pastures during the vegetation period but are
usually kept indoors during the winter. According to Swed-
ish animal protection regulations, horses must spend at least
1 h outdoors in a paddock where they can move freely each
day year around. This increases the risk of over-grazing,
extensive trampling damages and the deposition of horse
manure and urine in paddocks also during winter (Hammer
et al. 2020; Viksten et al. 2016). Also, horses are present in
the surrounding landscape to a higher degree than cattle,
since horseback riding along bridleways is an important part
of recreational horse-keeping.
Accumulated anthropogenic P in the landscape remains
mobile for decades and contribute to eutrophication of
surface waters (McCrackin et al. 2018b). Horse manure
that is not regularly collected from paddocks increases the
risk of P losses from manure heaps to soil and water
(Airaksinen et al. 2007; Aronsson et al. 2022). Also, the
long-term accumulation of P in the soil of intensively used
paddocks, especially in areas for feeding and defecation
(Airaksinen et al. 2007; Parvage et al. 2013), will con-
tribute to increased risk of P losses due to the strong
relationship between increasing soil P content (soil P sat-
uration) and the amount of water-soluble P in the soil
(Heckrath et al. 1995;Bo
¨rling et al. 2004). Thus, a high
density of horses on land formerly used as arable fields or
for cattle grazing probably increases nutrient loads to
adjacent waters, through surface runoff, or drain tiles,
causing eutrophication (Airaksinen et al. 2007; Hammer
et al. 2017; Parvage et al. 2011,2013).
To design and target effective mitigation measures and
encourage local mitigation activities, there is a need of field
data and local knowledge of actual nutrient losses, both for
individual paddocks, as well as on farm and catchment
levels. This includes management and mitigation practices
regarding paddocks and manure management at the farm
level as well as an understanding of the role of farm
location in the landscape and catchment.
In this study, we apply an ecosystem management per-
spective to transforming land use patterns in multi-functional
peri-urban landscapes, focusing on the effects of increased
horse-keeping on nutrient loads and water quality. The aims
were to investigate if equine facilities contribute to nutrient
loads to adjacent surface waters, identify the risk factors for
nutrient leakage and to identify, test and evaluate a suit-
able method to quantify nutrient loads from horse-keeping in
the context of other diffuse and point sources in peri-urban
landscapes. To do this, we used calculations based on liter-
ature standard values as well as field measurements and
water runoff modelling for sub-catchments including horse
activities on five horse farms located in four different
catchment areas in the Stockholm Region, Sweden.
MATERIALS AND METHODS
Study area
The study was performed in Ekero
¨municipality, 10 km
from Stockholm city centre (Fig. 1). Ekero
¨has ca 29 000
inhabitants and is one of the most horse-dense municipal-
ities in Sweden with ca 2000 horses in around 130 facili-
ties, predominantly for recreational horse-keeping (Ekero
¨
municipality 2018). The municipality is recognized for its
high natural and cultural values and is comprised of an
archipelago in Lake Ma
¨laren (57% land, 43% water),
which discharges into the heavily eutrophicated coastal
areas of the Baltic Proper (Walve et al. 2018). Lake
Ma
¨laren is an important public water supply for the
Stockholm region, and currently, the ecological status is
good to moderate (VISS 2022). The land use (13% urban
area, 43% forest, 30% arable land, 14% open land) follows
the soil distribution where soils in the upper part of the
terrain are forested, and arable land is located in the lower
areas where clay soils dominate (Fig. 1). Small catchments
dominate the runoff, drained by ground water and small
ditches and streams. Most of the arable land is artificially
tile drained. In the cold temperate climate, the runoff is
normally highest in the winter with a peak in the early
spring (February–March) and lowest in the summer during
the vegetation period (July–August).
Horse facilities and sampling sites
Five horse facilities in four water catchments (A–D) were
identified via map analyses and field visits. In total, 18
sampling sites for water were selected. The sampling sites
represent sub-catchments ‘upstream’’, ‘at/close to’’, or
‘downstream’ horse facilities, or a reference area with
only forest. For each water sampling site, the sub-catch-
ment size and the area used for different land use were
calculated, and the runoff was measured and modelled.
Paddocks in the vicinity of sampling sites were monitored
by visual observations year-round and the status of the
vegetation cover, trampling and waterlogging was docu-
mented. We use the word paddock for fenced areas of
varying sizes, where horses, alone or in smaller groups,
spend parts of the day all year-round. The grass cover in
paddocks varied between fully covered to bare. Grazing
areas refer to larger pastures, mainly used for summer
grazing (June–August), where the horses can spend a few
weeks to months feeding on the grass.
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All five horse facilities included in the study are livery
yards for private horses, averaging ca 35 horses per facility
and horse density of ca 4.5 horses ha
-1
, but varied substan-
tially between the facilities and over the year (Table 1).
Information was gathered about the horse-keeping practices
including feeding regimes, daily and seasonal turn-out routi-
nes in paddocks and summer grazing areas, as well as the
manure management, mucking regimes, overflow events and
ditch-cleaning activities that have taken place. The horses are
mainly fed in the stables with combinations of roughage and
different concentrates, minerals and vitamins, usually adapted
to the individual horse and level of activity. The pasture in the
paddocks is a minor supplement, except for summer grazing
periods. The horse facilities represent different settings of
pastures, paddocks and stable facilities, mixed with other land
use in their respective catchment areas.
Phosphorus accumulation in the soil
To assess the risk of P leaching from paddocks, P accu-
mulation in the soil of horse paddocks was studied in one
of the catchments (A). Two adjacent horse paddocks (ca
Sampling site
Stable
Horse farm
Paddock
Ditch
Sub-catchment
Catchment
Arable land
Open land
Urban green area
Forest
Urban area
Water
Balc Sea
Sweden
Ekerö municipality
Fig. 1 Location of Stockholm region and Ekero
¨municipality with a schematic illustration of a catchment divided into sub-catchments for each
sampling site. To keep the farms anonymous the name of the sampling site is fictive and just illustrates the study design. Landcover data
downloaded from the Swedish Environmental Protection Agency (open data)
ÓThe Author(s) 2023
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2.6 horses ha
-1
) with 30–50 years of horse-keeping, and a
grazing area used for 2 years were sampled. The clay
contents of the soils were 27–39%. An adjacent arable field
(54% clay) and a nearby forest soil were used as reference
areas. The paddocks had a poor or no (near the entrance)
vegetation cover at sampling while the grazing area had
intact grass cover all year around.
Samples from different parts of the grazing areas were
taken in September (0–10 cm depth) and in November
(0–10, 10–20 and 20–30 cm depth) 2020 as composites
sample consisting of 8 sub-samples.
The soil was analysed for total nitrogen (N), total
phosphorus (P), total carbon (C), ammonium-lactate sol-
uble P (P-AL), hereafter referred to as easily soluble P, and
CaCl
2
-soluble P, a proxy for P directly available for
leaching. Also, ammonium-lactate soluble iron (Fe) and
aluminium (Al) were analysed to estimate the degree of P
saturation of the soil (DPS), calculated according to Ule
´n
(2006). Total P and P-AL, Fe, and Al were analysed with
ICP, the later after extraction with ammonium-lactate at pH
3.75 (Egne
´r et al. 1960). Total N and C were analysed with
Leco. For estimation of directly leachable P (Blomba
¨ck
et al. 2021), soil samples were extracted with CaCl
2
(0.01 M) and analysed colorimetrically (Murphy and Riley
1962).
Identification of land use, catchment areas
and water runoff
Catchment areas, flow direction and flow accumulation
were identified using altitude data from Lantma
¨teriet
(Swedish mapping authority) and hydrological calculation
models available in ArcGIS Pro 2.5. Delimitation of water
divides for sub-catchments was done manually based on
the GIS-model result (Fig. 1). Six different land use classes
(urban area, urban green area, arable land, pasture, open
land and forest) were identified and extracted from
CORINE landcover data from 2012. The paddocks and
grazing areas for each horse facility, as well as the number
of houses with private sewers were mapped using aerial
photos from 2019 (Lantma
¨teriet) (Fig. 2).
The runoff model
The runoff was calculated using the HBV model described
in Bergstro
¨m(1992) and Lindstro
¨m et al. (1996) based on
the general water balance of precipitation, evapotranspi-
ration, runoff, and storage. No lakes were present in the
studied catchment areas. The model divides the catchment
area into sub-catchments and the input data were daily
temperature and precipitation, which were downloaded
from the closest weather station (SMHI open data). The
potential evapotranspiration was calculated using the
average daily temperature, and the sun inclination for the
day of the year according to Penman (1948). To estimate
the actual evapotranspiration, an interception routine was
applied described in Lindstro
¨m et al. 1996). The water
storage capacity of the forest canopy was set to 2 mm, and
the storage capacity of open land was set to 0 mm.
Each sub-catchment was divided into two separate model
units, one for forest (sandy glacial till), and the other for open
land (glacial/post-glacial clay) based on landcover, and soil
type, e.g. parent material (SGU open data). Each model units
contributing separately to the sub-catchment runoff. The
field capacity was set to 244 mm for the till, and 366 mm for
the clay based on a study by Rodhe et al. (2006). The idea was
to keep the runoff model simple using parameters available
from studies with similar conditions (region and climate).
The pros and cons of the simple setup of the HBV model are
discussed in relation to other hydrological models in a paper
by Seibert and Bergstro
¨m(2022). A weakness of the model
was the possibility to calibrate the surface runoff continu-
ously during the measurement period in the previously
ungauged catchments. Two field methods were used
depending on the site condition, the float method (velocity-
area principle) and the volume per time method. The float
method measures the surface velocity and was re-calculated
to mean velocity for the channel section using a coefficient
(e.g. 0.8), then multiplied with the area of the cross-section
(Davids et al. 2019). The volume per time method was used
for sites where the cross-section allowed collecting the total
runoff into a container, measuring the time to fill the known
volume, and no further calculations were needed. Field
measurements were scattered during the sampling period in
relation to occasions with precipitation, occasionally sam-
pling was not possible at all sites when ditches were partly
frozen and not reachable, or dry. Another problem was
human impact such as ditch cleaning.
To validate the model, the modelled runoff was com-
pared with the estimated runoff for the two regions, where
the field sites were situated (SMHI open data,
SVAR_2016_3). The SVAR estimation for the studied
period was 160 mm and 161 mm. The model values for
water runoff varied between ca 85 mm in areas dominated
by forest, up to ca 135 mm in areas with more arable land.
Table 1 Surface area of catchment, area for paddocks and grazing,
number of horses and horse densities in the studied water catchments
Water
catchment
Catchment
area (ha)
Area for paddock
and grazing [ha
(%)]
Number of
horses (#)
Horse
density
(#/ha)
A 52 14 (28%) 30 2.1
B 81 6 (7%) 30 5.0
C 74 13 (18%) 45 3.4
D 21 3 (16%) 25 7.6
Average 57 9 (16%) 33 4.5
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Both the SVAR estimation and the model had approxi-
mately 30% drier conditions during the studied period in
relation to the 30-year average. The validation suggests a
slight underestimation by the runoff model.
Two approaches to estimate the nutrient load
from catchments with horse facilities
We used two different approaches to estimate the nutrient
loads, with the attempt to compare and evaluate the pos-
sible quantification methods for water quality assessment
in areas with a high density of horses (Table 2).
The ‘Calculation’ approach
Nutrient loads estimated with the ‘Calculation’ approach
were based on standard values on area-specific load (kg
ha
-1
year
-1
) derived from type-specific nutrient concen-
trations (mg l
-1
) for land use types (Table 3), the runoff (mm
year
-1
) from the respective sub-catchment and the propor-
tion of respective land use (%) (Table 2). The load from
paddocks was assumed to be twice the load for arable land
based on the findings by Parvage et al. (2011), where the
mean concentration of P in the paddock was three times
higher than the arable land, and the easily soluble P in the soil
was close to two times higher in the paddock. The contri-
bution from small sewers was estimated from type and age of
respective sewage solution, the number of persons per
household, literature values on nutrient content in the
wastewater (Jo
¨nsson et al. 2005), reduction effectiveness of
sewage solutions (Palm et al. 2002;Hu
¨binette 2009) and an
assumed nutrient retention (30%) between the outlet from
the respective sewage system and the recipient water
(Hansson et al. 2019). To investigate the impact of horse
facilities on the nutrient load, two scenarios were compared
for the calculation approach: the ‘today-scenario’ and the
‘no-horse-scenario’’, where an alternate land use to pad-
docks was assumed based on the type of soil and present land
use in the non-horse areas with similar conditions (Fig. 2).
The ‘Field measurement’ approach
Nutrient load estimates from ‘Field measurements’ were
based on empirical data on nutrient concentrations and
modelled water runoff (Table 2). Water samples were
collected approximately every 2 to 3 weeks from March
Fig. 2 Surface area (ha), number of houses and persons, sampling sites and land use distribution of the catchments of the horse farms, including
paddocks and assumed land use if they were not used as paddocks
ÓThe Author(s) 2023
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2020 to March 2021 (in total 18 occasions), at periods of
sufficient water flow for sampling. During high runoff, the
sampling was more frequent. The water samples were
analysed for total phosphorus (P) and nitrogen (N), as well
as phosphate P, ammonium N and nitrate–nitrite N, here-
after referred to as dissolved P and N, respectively. All
water samples were analysed at Erken Research Laboratory
at Uppsala University using spectrophotometrically meth-
ods (SIS 1996,2004,2005). The water samples were kept
dark on cooler blocks in a cooler box (aiming at 2–8 °C)
from sampling to analysis that was performed the same
day.
The annual total P and N loads were calculated as the
sum of the daily loads. Daily loads were derived from an
interpolation between the 18 field measurements with local
polynomial regression fitting using the loess function in R
4.0.5 (R Core Team 2021) with span 0.75, polynomial
degree = 2, and Gaussian residual distribution. The annual
mean concentration of total P and N were calculated as the
ratio of the annual total P and N load and the annual runoff
(Table 2). To investigate the potential sources of the
measured nutrient loads, we used the estimates from the
‘Calculation’ approach, and nutrient load from paddocks
was assumed to be the residual load of the total load and
the sum of other sources.
Statistical analyses
Differences in element contents of the soil (0–10 cm depth)
in the three paddocks at the first sampling, and differences
Table 2 Summary of how variables were derived in the ‘‘Measurement’ and the ‘Calculation’ approach, respectively, at the three different
levels of scale; sampling site, sub-catchment and catchment. NP stands for nutrient, i.e. total and dissolved N and P. Blue text represents data
collected or derived from site-specific measurements and modelling, whereas orange text represents literature data
Table 3 Standard values used for type-specific nutrient concentration
(mg l
-1
) for total P (TP) and total N (TN) load for different land uses
Type-specific
concentration
References
mg
TP l
-1
mg
TN l
-1
Forest 0.013 0.52 Ejhed et al. (2016), Hansson
et al. (2019)
Open land 0.026 1.50 Ejhed et al. (2016)
Arable land 0.280 3.00 SMED (2019)
Paddock Assumption ‘two times arable land’’
Urban area 0.135 1.67 StormTac (2021)
Urban green
area
0.140 1.05 StormTac (2021)
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between different soil depths at the second sampling, were
tested with one-way ANOVA (p\0.05). Tukey’s post hoc
test was used for examining the pairwise differences
between depths. Associations among variables were tested
with regression analysis. All statistical analyses on soil data
were performed with SAS JMP Pro 15.
Measurements of environmental N and P load were
analysed as functions of the different land use types present
in each sub-catchment with linear mixed models using
package lme4 (Bates et al. 2015) in R 4.0.5 (R Core Team
2021). Log total P or N load, mean total P or N concen-
tration, dissolved P or N concentration and square root of
total P or N load per hectare were analysed individually as
response variables in eight different models. For all mod-
els, we included the total sub-catchment area, location of
sampling site within the farm, nutrient load from private
sewers and the different land use types estimated as their
proportion of the total sub-catchment area as fixed factors.
We sampled at several locations within each farm; there-
fore, all models also included farm as a random factor to
control for the correlative structure within farms. All full
models included all fixed and all random factors (Eq. 1,
example of full model). We used pvalues and AIC values
for a backwards selection procedure to select the simplest
most informative final model (Eq. 2, example of final
model). A non-significant variable dropped was considered
uninformative if it did not conflict with the change in AIC
and instead resulted in a less informative model with fewer
significant explanatory variables.
log total P load Sub-catchment Area þLocation1
þP load from Sewers þPaddock2þArable land2
þOpen land2þForest2þUrban area2
þUrban green area2þ1jFarmðÞ
3
ð1Þ
where
1
Location around horse facility [upstream, at/close
to, downstream],
2
Proportion of the total sub-catchment
area,
3
Random factor treating all measurements from the
same farm as dependent.
log total P load Location þP load from Sewers
þPaddock þOpen land þ1jFarmðÞ
ð2Þ
RESULTS
The presentation of the results is organized in an increasing
order of scale and complexity, starting with the results
from the field measurements of P in soil and N and P in
ditch waters in the 18 sampling sites. Based on the two
methods described in the methods section, estimates are
then presented for nutrient load at the sub-catchment-level,
as well as at the catchment level, including the influence of
the horse facilities and other land uses.
Phosphorus accumulation in the soil and related risk
of P losses
There was an accumulation of organic matter and nutrients
close to the soil surface of the paddocks (0–10 cm depth),
compared to soil further down (10–30 cm depth). The
accumulation in the top 10 cm was significant for total N
and C and P-CaCl
2
, but not for P and P-AL (Table 4). In
the samples taken at 0–10 cm depth, there was a strong
significant positive relationship between the pool of easily
soluble P (P-AL) and the degree of soil P saturation (DPS)
(Fig. 3), especially at entrances and in defecation places. In
these areas, and at the entrance of the 50-year-old paddock,
P directly available for leaching (P-CaCl
2
) constituted
0.1–0.5% of the easily soluble pool of P at 0–10 cm depth.
Despite the long history of horse-keeping in the 50-year-
old paddock, soluble P and DPS were lower than in the
other paddocks (Fig. 3).
N and P concentrations in ditch water
The mean nutrient concentrations in the ditch water of the
sub-catchments varied substantially, from 10 to 60 lgPl
-1
and 500 to 900 lgNl
-1
in the reference areas with 100%
forest, to 500 lgPl
-1
and nearly 8000 lgNl
-1
in sub-
catchments with horse farms surrounded by small agri-
cultural fields, forest and open land including summer
grazing areas (Fig. 4a). The mean concentrations of both P
and N increased significantly with increasing proportion of
paddocks in the landscape (Table 5). There were also
significantly elevated P concentrations in sub-catchments
with a high abundance of arable land and open land
(Table 5). The lowest concentrations were found in the
reference areas and sub-catchments with large portion of
forest.
Proportion dissolved N and P
Of the total load of nutrients, the highest proportions of
dissolved nutrients were found in sub-catchments where
land use was dominated by paddocks, arable land and
houses with private sewers (Fig. 4b, Table 5). The lowest
proportions were found in the reference areas as well as in
C1 and C2 (12–25% dissolved P, 1–48% dissolved N),
where forest is the main land use, and where all houses
were connected to the municipal water and sewage system
(Fig. 4b). There was a significant positive effect from the
proportions of paddocks, arable land and open land, on
dissolved P, and a significant negative effect from the
proportion forest cover on dissolved N (Table 5).
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Nutrient loads (measured) from paddocks
and the studied sub-catchments
Nutrient loads from sub-catchments with a large proportion
of paddocks, arable land and/or houses with private sewers
had higher area-specific nutrient loads (C0.15 kg P
ha
-1
year
-1
,C2.0 kg N ha
-1
year
-1
) than the other sub-
catchments (\0.15 kg P ha
-1
year
-1
,\2.0 kg N ha
-1
-
year
-1
) (Fig. 4c), with a significant positive effect of the
proportions of paddocks, arable land and open land
(Table 5). In sub-catchments with a higher estimated
annual P load from private sewers, there were also a sig-
nificantly higher P and N load in the ditch water (Table 5).
The area-specific nutrient load for horse facilities varied
between the sub-catchments. However, on average, the
load was similar between the ‘measurement’ and ‘‘cal-
culation’ approach for P (1.2 kg P ha
-1
year
-1
), but not for
N (7.6 kg N ha
-1
year
-1
and 16.7 kg N ha
-1
year
-1
,
respectively) (Table 6).
N and P load and source distribution in the studied
areas at the catchment/landscape level
The ‘measured’’ total annual nutrient loss from the studied
sub-catchments to the recipient ranged from 5 to 20 kg P
and from 50 to 320 kg N (Fig. 5), where the variability was
due to size and land use. This corresponded to 0.15–0.40 kg
Pha
-1
year
-1
and 1.6–6.1 kg N ha
-1
year
-1
. The main
nutrient sources were arable land, paddocks and sewage
from private homes (Fig. 5). Forest and open land con-
tributed less, and they often covered a large part of the sub-
catchments.
In catchments A, B and D, there were less than 15%
difference between the measured and the calculated loads,
whereas for catchment C, the total load estimated from
‘measurements’’ was ca 40% lower than estimated from
‘calculations’’.
Assessment at the catchment scale of the source distri-
bution of the ‘measured’ loads suggested that paddocks
contributed with 30–40% of the annual P load and 20–45%
of the N load in the A, B and D sub-catchments. This was
similar to the difference between the calculated ‘‘today-
scenario’ and ‘‘no-horse-scenario’’, suggesting that the
presence of horse facilities increased the nutrient load with
approximately 15–30% P and 10–25% N. The lowest
increase (10–15%) was found in sub-catchment B. This
sub-catchment is very large and has a fairly small pro-
portion of paddocks and a large proportion of fields. Arable
fields contribute with large nutrient loads, resulting in a
relatively small impact from horse facilities on the total
nutrient load.
DISCUSSION
Eutrophication of inland and coastal waters is the result of
multiple nutrient sources (HELCOM 2018a,b), and land
use is an important factor affecting the nutrient loss and
water quality in the catchments (e.g. Hong et al. 2012;
Ka
¨ndler et al. 2017; de Wit et al. 2020; Djodjic et al. 2021).
To reach the environmental goals of no eutrophication,
there is a need to assess the nutrient loads at different scale
levels, and provide means for local landowners to actively
participate in mitigation measures. This study was per-
formed in a multi-functional peri-urban landscape with a
mixture of land uses and anthropogenic activities, such as
farming, equine facilities, recreational trekking areas, nat-
ure reserves and residential areas with private sewers,
gardens and roads. Despite the complexity of patchy
landscapes, our results showed that horse activities can
enhance the nutrient transport considerably and should be
included in eutrophication assessments, especially for
horse-dense peri-urban areas. The nutrient concentrations
increased in the water downstream of the paddocks and the
proportion of dissolved nutrients increased with increased
proportion of land used for paddocks and grazing areas;
soil sampling in paddocks confirmed a risk of P losses from
these areas due to accumulation of P in the soil. The
amounts of soluble P, directly available for leaching,
roughly correspond to be 0.6–1.1 kg ha
-1
(if soil density
equals 1300 kg m
-3
), which is considerably higher than the
arable field (0.2 kg ha
-1
). Further, also bridleways and
summer grazing areas should be considered in assessment
of the effects of horses on water quality, as horses, in
contrast to livestock, move around in the landscape outside
fenced areas. Site visits revealed horse manure on tracks
Table 4 Mean values for all paddocks, including entrance areas, of total N (TN, %), total C (TC, %), total P (TP, mg kg
-1
), ammonium-lactate
soluble P ‘easily soluble P’ (P-AL, mg kg
-1
), calcium cloride soluble P ‘leachable P’’ (P-CaCl
2
,mgkg
-1
) and degree of P saturation of the soil
(DPS, %), at three depths, standard deviations in parentheses. Significant differences (p\0.05) are indicated by letters
Depth
cm
TN
%
TC
%
TP
mg kg
-1
P-AL
mg kg
-1
CaCl
2
-P
mg kg
-1
DPS
%
0–10 0.24
a
(0.05) 2.8
a
(0.46) 1016
a
(101) 18
a
(8.0) 0.60
a
(0.31) 31
a
(12)
10–20 0.16
b
(0.03) 1.8
b
(0.49) 894
a
(96) 13
a
(7.2) 0.28
ab
(0.14) 25
a
(10)
20–30 0.12
b
(0.04) 1.34
b
(0.41) 879
a
(88) 12
a
(3.5) 0.17
b
(0.08) 22
a
(6.5)
123 ÓThe Author(s) 2023
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Fig. 3 Relationship between ammonium-lactate soluble phosphorus in the soil (P-AL) in paddock, arable field and forest and degree of
phosphorus saturation (DPS) (y= 4.5 ?0.35x,R
2
= 0.8, p\0.0001). Three of the soil samples were collected at entrances of paddocks and one
under a manure heap in a paddock
Fig. 4 a Annual mean concentration (flow weighted) of total P and total N (lgTPl
-1
,lgTNl
-1
), bannual average of the relative amount of
dissolved P (% DIP) and dissolved N (% DIN) of total P and N, and carea-normalized annual total P and N load (kg TP ha
-1
year
-1
,kgTN
ha
-1
year
-1
) at the different sampling sites in catchment A–D and reference areas 1–3
ÓThe Author(s) 2023
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and walkways, which indicate that horse activities possibly
have contributed to the observed effects.
Assessing local nutrient loads in complex, multi-
functional catchments
Significant influence of land use pattern on water quality
has also been found in other studies, where factors for
increased impact included livestock farming and arable
land, densely populated areas and horse farming (e.g. Woli
et al. 2004;Ka
¨ndler et al. 2017; Vrebos et al. 2017; de Wit
et al. 2020; Djodjic et al. 2021). Landscape composition
(proportion of land uses) and configuration (spatial
arrangement) are also important factors for nutrient trans-
port (Casquin et al. 2021).
However, the impact of horses on eutrophication seems
often to be overlooked or underestimated, probably due to
the fact that nutrient losses from diffuse nutrient sources
are difficult to assess and requires considerable resources.
Further, as horse-keeping for recreation is a relatively new
and growing sector, compared to agriculture, there has
been a lack of awareness and regulations directed at horse
facilities, where manure removal and recirculation can be a
considerable cost (Hammer et al. 2017).
Nutrient loads from horse facilities in our study (1.2 kg
Pha
-1
year
-1
and 7.6 kg N ha
-1
year
-1
) were based on
field measurements during 1 year in four catchments with
horses, combined with site-specific modelling of the water
runoff (Table 6). These values are similar for N but higher
for P, compared to values from long-term measurements in
a catchment in the same part of Sweden (0.5 kg P ha
-1
-
year
-1
and 6.3 kg N ha
-1
year
-1
) that is dominated by
arable land, and have few houses and no livestock or horses
(Linefur et al. 2022). Poor manure management can be one
explanation for the higher P load from areas with horse
facilities, and a plausible explanation for why this was not
accompanied with increased N load could be volatile losses
of N. Ammonia losses from urine, which contains the main
part of excreted N in the form of ammonium, are supposed
to be considerable when placed on the soil surface, espe-
cially under conditions with summer temperatures (Som-
mer and Hutchings 2001). Moreover, N losses by
denitrification will occur from systems where nitrate is
enriched (Fowler et al. 2013). A study of horse paddocks
without vegetation in comparison with hay field and
grassland showed that larger amounts of nitrate in the
paddock soil was followed by increased losses of nitrous
oxides (Makinde 2020).
Table 5 The effect from surrounding landcover types on nutrient loadings from horse farms to sub-catchments in Ekero
¨municipality,
Stockholm County, Sweden. All response variables (rows) were analysed with sub-catchment area, sampling location, nutrient load from private
sewer and all landcover types as fixed factors and farm as random factor in linear mixed models. Columns report the result from the fixed
landcover types and nutrient load from private sewers in the final most informative models following model selection. All effect values are
estimated regression coefficients. Significant differences among sub-catchment areas and site locations are not reported. Landcover types lacking
significant effect in all models are omitted
Variable Paddock Arable land Open land Forest Sewers
Load (kg total P year
-1
) 1.52* ns 1.90* ns 0.77***
Load (kg total N year
-1
) 0.85°ns ns ns 0.02**
Load per ha (kg total P ha
-1
year
-1
) 0.63*** 0.38* 0.63** ns ns
Load per ha (kg total N ha
-1
year
-1
) 1.43** 0.75* ns ns ns
Mean concentration (lg total P l
-1
) 442.90*** 348.39*** 457.16*** ns ns
Mean concentration (lg total N l
-1
) 3942.48* ns ns ns ns
Dissolved P (% DIP) 0.58*** 0.45*** 0.43*** ns ns
Dissolved N (% DIN) ns ns ns -0.4* ns
ns p[0.10, °p\0.10, *p\0.05, **p\0.01, ***p\0.001
Table 6 Average (minimum–maximum) values of area-specific total N and P load (kg ha
-1
year
-1
) and type-specific total N and P concen-
tration (mg l
-1
) for horse facilities, derived from the ‘‘measurement’ and the ‘calculation’ method from seven sub-catchments in three
catchments
Area-specific load Type-specific concentration
kg TP ha
-1
year
-1
kg TN ha
-1
year
-1
mg TP l
-1
mg TN l
-1
Measurement method 1.24 (0.4–3.2) 7.60 (3.1–22.3) 1.05 (0.4–2.7) 6.38 (2.7–18.9)
Calculation method 1.19 (0.8–1.7) 16.66 (11.3–23.2) 1.01 (0.7–1.5) 14.00 (10.9–20.2)
123 ÓThe Author(s) 2023
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The four catchments in the study represented different
mixes of land use, from mainly rural open land to forest-
dominated and mixed land use, including more urban and
residential areas. To assess nutrient loads and target suit-
able mitigation measures, we applied and compared two
approaches, one mainly relying on field measurements with
one based on calculations using standard values. Overall,
the two approaches gave similar results for P but not for N,
suggesting that the assumption for the calculation approach
that horses contribute ‘two times arable land’ was valid
for P, but overestimated for N. Further, the methods pro-
duced similar results for three of the four catchments, both
at the catchment and at the sub-catchment levels, implying
that both methods are applicable for this type of
assessments.
At the catchment levels, the difference of the total
nutrient loads between the two approaches was less than
15% for three of the four catchments. The largest dis-
crepancy (40%) was found for the patchiest catchment (C),
possibly due to the limitation of standard values to handle
high land use complexity. Catchment C has one sub-
catchment with numerous single-family homes and block-
houses, two sub-catchments with quite a few detached
houses with private sewers, and walk ways and open areas
for recreational use that runs through several sub-catch-
ments. Also, substantial ditch cleaning took place in one of
the sub-catchments, possibly affecting the outcome. The
contribution from paddocks in catchment C was likely
underestimated, especially for N. Nearly 20% of the land
was covered by paddocks and several significant effects
from paddocks were detected (Table 5).
In addition to challenges related to handling patchiness
and land use complexity in eutrophication assessments,
there are also other considerations regarding the suit-
ability of these methods as a basis for mitigation and
management. It is costly and resource demanding to
quantify nutrient loads from diffuse nutrient sources with
field measurements, and is often associated with uncer-
tainties and a natural variability to consider. The catch-
ment areas for representative sampling sites need to be
Fig. 5 Estimated annual phosphorus and nitrogen load (kg TP year
-1
, kg TN year
-1
) in the four catchment areas; measured, calculated today-
scenario (with horse activities) and calculated no-horse scenario (without horse activities). Source distribution for measured load was derived
from standard values, site-specific runoff and proportion land use, for all nutrient sources but paddock, which was assumed to be the residual
load. The calculated load from paddocks (today-scenario) assumes an area-specific load twice the load from arable land
ÓThe Author(s) 2023
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identified and the water runoff was measured or mod-
elled. Flow proportional water sampling is often prefer-
able in order to improve the accuracy of the estimations
(Schleppi et al. 2006), but not always possible. Using
standard values for runoff for the region instead of the
runoff for each sub-catchment derived from the site-
specific water flow model resulted in ca 30–40% higher
N and P loads in this study, stressing the importance of
adequate estimates of the water runoff. In addition to
geographic scale, the time scale is also important. Sea-
sonal and annual weather variations cause large varia-
tions in nutrient transport in waterways, especially due to
variations in precipitation and water runoff (Ezzati et al.
2023). The year of our study was drier than normal with
30% lower precipitation than the average for 1981–2021
(693 mm year
-1
). This most probably resulted in lower
runoff compared to an average year and probably also in
lower total transport of nutrients due to less efficient
outwashing of water-soluble N and P during drier
conditions.
Using models based on area-specific coefficients is
relatively simple, straight forward, and a commonly used
approach (Metson et al. 2017; HELCOM 2022) and is
considered to be a practical decision support tool for
assessing the impacts of land use on water quality
(Palviainen et al. 2016). However, using area-specific load
coefficients introduce uncertainties, and it is important to
select representative values of both source-specific coef-
ficients and water runoff. This comparative study suggests
that assessments using standard values can provide a fair
overview of nutrient sources and losses for the catchment
level, especially in combination with high physical pres-
ence and local knowledge to gain a better understanding
of landscape under assessment.
Another finding in the study concerns the impact of
private sewers on nutrient load in the catchments. Although
there were relatively few houses in all the studied catch-
ments, we found significant effects of private sewers on the
nutrient load. Just as horses, humans have high proportions
of dissolved N in urine and P in feces (Mihelcic et al. 2011;
O
¨gren et al. 2013; Weir et al. 2017), and most of the
households relied on infiltration beds or other ground-based
sewage solution systems in which reduction efficiencies
may vary. Consequently, in addition to horses, private
sewers are important to consider in nutrient load assess-
ments, especially in small patchy catchments.
In the reference areas of the study sites, all dominated by
forest, the nutrient concentrations were higher than
expected, possibly due to the influence of nutrient sources
and activities such as bridleways, various outdoor recre-
ation activities and walking of dogs. Such sources con-
tribute to composite water samples and override the low
nutrient concentrations from the forests.
Risk factors for nutrient loads—assessments
across multiple scales
The nutrient concentrations in the ditch water varied con-
siderably over the study period. Increased amounts of
soluble P in soil surface layers in paddocks were identified,
which also constitute a risk to enhanced losses of dissolved
and particle-bound P, both through subsurface leaching and
surface runoff. Erosion mainly occurs when there is poor
vegetation and when infiltration of water is reduced, during
wet conditions or snow melting periods (Norberg et al.
2022).
To investigate the reasons and identify the potential risk
factors for enhanced nutrient transport, the elevated values
were cross-checked for possible explanations in field
observations during sampling. The most elevated values
could be directly related to the presence of horses. The
ground cover in most paddocks used for daily turn-out was
heavily affected by horse trampling and lacked rooted
vegetation on substantial parts most of the year (Fig. 6).
Conditions were worst during the wet period in November–
April, with open mud, puddles and no vegetation. In May–
October, conditions were drier and some parts of the pad-
docks were covered with poor and patchy vegetation. At
the entrance of the paddocks, the ground was vegetation
free the whole year, and there was also high manure load
throughout the period in these areas, which also was in
accordance with the results from the soil sampling (Fig. 3).
Enclosures that were used only for summer grazing with a
low density of horses had a constant vegetation cover. The
most evident risk factor observed during sampling was
trampling in the vicinity of ditches (sampling site), espe-
cially during or just after rain, as well as during snow
melting periods. Other non-horse related reasons for ele-
vated nutrient transport were ditch cleaning, establishments
of new ditches and overflooding wells of bio-treatment
sewage plants.
The high value and competition of land close to cities
can result in persistent high densities of horses in the
paddocks. As a result, these paddocks often lose their
vegetation cover that can prevent erosion and trap nutri-
ents. Risk factors affecting the potential for nutrient
leaching could be identified on different scales: locally in
the paddocks or in the vicinity, on the horse farm, sub-
catchment and landscape levels. Closeness to water courses
as well as how the paddock and horse farm are placed in
the landscape can affect the concentrations in ditch water.
This stresses the need for frequent manure removal as
shown by, e.g. Aronsson et al. (2022) and the need for
measures to reduce trampling damages in paddocks in the
strive for an intact vegetation cover. Also, composted horse
manure can be used as a resource for improved soil fertility
in crop production (Bernal et al. 2009). Hence, there are a
123 ÓThe Author(s) 2023
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Fig. 6 Risk factors for nutrient losses in horse paddocks. AFTrampled grounds reduce the nutrient retention. CEManure piles left in the
paddocks, poor drainage and ditches running through paddocks. BNutrients from paddocks located next to ditches can readily be transported to
lakes or coastal areas. FThe vegetation cover lost due to trampling by horses, is often worst at entrances and feeding spots. (Photos
by L. Kumblad)
ÓThe Author(s) 2023
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range of important and practical management measures
that belong to the local farm level that can improve the
water quality at the catchment level.
From horse farm to landscape—assessments
across multiple scales
Analyses of anthropogenic nutrient fluxes for the Baltic
Sea catchment show a strong linear relationship between
the anthropogenic nutrient input and riverine nutrient
fluxes, and compliance to the Baltic Sea Action Plan
(HELCOM 2021) would imply substantial changes in the
agricultural sector (Hong et al. 2012,2017; McCrackin
et al. 2018a). As horse facilities were not included in the
Baltic Sea nutrient accounting analyses, and result from
this study clearly shows that horses may contribute con-
siderably in some areas, the net anthropogenic nutrient
input to the region is possibly underestimated. Compar-
isons of the average annual P and N loads from horses (ca
0.5 g P m
-2
,3gNm
-2
) and humans (ca 0.005 g P m
-2
,
0.1 g N m
-2
) in the studied catchments in this study show a
ca 100 times higher P load and 40 times higher N load from
horses than from humans. The calculations were based on
the number of horses and humans living in the respective
catchments, excretion values for sport horses (Malgeryd
and Persson 2013) and nutrient content in toilet wastewater
for humans (Jo
¨nsson et al. 2005), with reduction due to
sewage treatment (Palm et al. 2002 and Hu
¨binette 2009).
Horse farms in peri-urban areas can thus be considered as
nutrient hot-spots, as most of the fodder is imported to the
catchment, the manure is seldom recycled within the
catchment, and intense trampling contributes to increased
nutrient losses from the soil. To prevent further accumu-
lation and losses of nutrients, the recycling of manure and
human sewage need to be more efficient (McCrackin et al.
2018a; Svanba
¨ck et al. 2019; Pihlainen et al. 2020), and to
enhance the efficiency in environmental and economic
outcomes, abatement work should focus on the dissolved
and thus biologically available P (Iho et al. 2023).
Mitigation measures to decrease nutrient loads need to
be viewed in an ecosystem management perspective in line
with the Water Framework Directive approach, integrating
stakeholders and managers at different scale levels from
the local field and farm to the catchment and river basins
(EC 2000; Hammer et al. 2011). Many horse facilities in
Sweden and other western countries are situated in the peri-
urban landscape with a matrix of different land use that
contribute with nutrients to the adjacent waters to various
extents (Elga
˚ker 2012). At the catchment level in our study,
it was shown that a higher proportion of paddocks
increased the mean nutrient concentrations in ditch water,
the amount of dissolved nutrients and the load of P and N
from the particular area. The present study also illustrates
the need to study nutrient loads at different scales, where
horse facilities, the proportion of arable land and open land
and the number of sewers from private households all
contribute to the nutrient load. Local knowledge is vital to
be able to understand the surroundings of the horse facil-
ities, manure practices and the location of paddocks within
the catchment and to elucidate different risk factors that
can affect the nutrient load, and if needed, can be managed.
For individual horse-keepers to be willing to take actions to
decrease the risk for nutrient leaching, solid estimates of
the actual contributions to nutrient load are needed (Fran-
ze
´n et al. 2016).
CONCLUSION
Horse-keeping facilities can contribute considerably to the
nutrient load to surface waters, particularly in multi-func-
tional peri-urban landscapes. Local risk factors include
manure management, trampling severity, soil condition,
closeness to water and where the facility is situated in the
landscape. Adequate methods are needed to distinguish the
impact from horses, from the impact of other nutrient
sources. In small, patchy and complex areas, measurements
from representative sampling sites are needed in combi-
nation with site-specific water runoff estimates, and local
knowledge or site visits. Literature standard values may
produce good estimates at the larger catchment level. The
impact of relatively small changes in the landscape, such as
expansions of paddocks, new residence houses or ditch
cleaning, may have a profound influence of the overall
nutrient transport at the local scale. Catchment-based
management should include all local sources, both in
monitoring and assessment programmes. The assessments
need to be applied at multiple scale levels, but adapted to
local conditions. To reduce the impact from horse-keeping,
guidelines and incentives for manure management that
enables a sustainable recirculation of nutrients need to be
improved and developed. Local engagement and commit-
ment are needed not only to identify and quantify the
problems, but also to find the appropriate solutions.
Acknowledgements This study was enabled by the financial support
from the foundation Thureus Forskarhem och Naturminne, Baltic-
Sea2020, BalticWaters and Formas, which we gratefully acknowl-
edge. The authors also would like to thank five anonymous horse farm
owners and a farmer for allowing us to perform the study on their
properties and providing valuable information on horse-keeping
routines and agricultural practises, Ekero
¨Municipality for informa-
tion on sewage solutions, as well as Gun Rudqvist, Richard Hopkins
and anonymous reviewers for valuable comments on the manuscript,
and Annika Tidlund for help with figures.
Funding Open access funding provided by Stockholm University.
123 ÓThe Author(s) 2023
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Ambio
Data availability Altitude—Lantma
¨teriet (FUK licence). Air
photo—Lantma
¨teriet (FUK licence). Landcover—Swedish Environ-
mental Protection Agency (open data). Meteorological and hydro-
logical data—temperature, precipitation, SVAR_2016_3—SMHI
(open data). Soil—Swedish Geological Survey (SGU) (FUK licence).
Declarations
Conflict of interest The authors have no relevant financial or non-
financial interests to disclose.
Open Access This article is licensed under a Creative Commons
Attribution 4.0 International License, which permits use, sharing,
adaptation, distribution and reproduction in any medium or format, as
long as you give appropriate credit to the original author(s) and the
source, provide a link to the Creative Commons licence, and indicate
if changes were made. The images or other third party material in this
article are included in the article’s Creative Commons licence, unless
indicated otherwise in a credit line to the material. If material is not
included in the article’s Creative Commons licence and your intended
use is not permitted by statutory regulation or exceeds the permitted
use, you will need to obtain permission directly from the copyright
holder. To view a copy of this licence, visit http://creativecommons.
org/licenses/by/4.0/.
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AUTHOR BIOGRAPHIES
Linda Kumblad (&) is an associate professor at the Stockholm
University Baltic Sea Centre. Her research interests include systems
ecology, and eutrophication assessment and management in coastal
areas.
Address: Baltic Sea Center, Stockholm University, 106 91 Stock-
holm, Sweden.
e-mail: linda.kumblad@su.se
Mona Petersson is a senior lecturer in Environmental Sciences. Her
research interest includes landscape analysis and river basin man-
agement.
Address: School of Natural Science, Technology, and Environmental
Studies, Department of Sustainability, Environment, and Global
Development, So
¨derto
¨rn University, 141 89 Huddinge, Sweden.
e-mail: mona.petersson@sh.se
Helena Aronsson is a senior lecturer at the Department of Soil and
Environment, SLU. Her research topic is plant nutrient management
in agriculture with focus on measures for reduced nutrient losses to
waters.
Address: Department of Soil and Environment, Swedish University of
Agricultural Sciences, Box 7014, 750 07 Uppsala, Sweden.
e-mail: helena.aronsson@slu.se
Patrik Dinne
´tz is a senior lecturer in Environmental Sciences. His
research interest includes landscape ecology, biodiversity and species
distribution in wetland and forest ecosystem.
Address: School of Natural Science, Technology, and Environmental
Studies, Department of Sustainability, Environment, and Global
Development, So
¨derto
¨rn University, 141 89 Huddinge, Sweden.
e-mail: patrik.dinnetz@sh.se
Lisbet Norberg is a researcher (PhD) at the department of Soil and
Environment, SLU. Her research interests include nutrient losses from
arable field and nutrient transports in the agricultural landscape.
Address: Department of Soil and Environment, Swedish University of
Agricultural Sciences, Box 7014, 750 07 Uppsala, Sweden.
e-mail: lisbet.norberg@slu.se
Camilla Winqvist is an environmental consultant with a background
in water management at the municipal level. She did her PhD at the
department of ecology at Swedish University of Agricultural Sci-
ences, SLU. Her research interests include ecosystem services in
arable landscapes.
Address: Rejlers Sverige AB, Stationsgatan 12, 753 40 Uppsala,
Sweden.
e-mail: camilla.winqvist@rejlers.se
Emil Rydin is an associate professor at the Stockholm university
Baltic Sea Centre, focusing on phosphorus turnover in the aquatic
environment.
Address: Baltic Sea Center, Stockholm University, 106 91 Stock-
holm, Sweden.
e-mail: emil.rydin@su.se
Monica Hammer is an associate professor in Natural Resources
Management. Her research interests include ecosystem governance
for sustainable social-ecological systems focusing on urban and peri-
urban landscapes.
Address: School of Natural Science, Technology, and Environmental
Studies, Department of Sustainability, Environment, and Global
Development, So
¨derto
¨rn University, 141 89 Huddinge, Sweden.
e-mail: monica.hammer@sh.se
123 ÓThe Author(s) 2023
www.kva.se/en
Ambio
... This decrease coincided with implemented measures such as picking up droppings, repairing manure storage facilities, introducing buffer strips along ditches, and fencing off horses from ditches, which was introduced in 2014 and 2015 (Owenius 2015). Such measures can be expected to reduce P concentrations, which often are higher in the vicinity of horse facilities (Kumblad et al. 2024). However, the lime filter (LF3) did not contribute to this decrease (Table 2; section Lime Filters). ...
... 1. Doing the right thing proper targeting of nutrient sources (agriculture, OWTs, horse keeping) and selection of suitable mitigation measures is essential. In this regard, the decreasing trends in PO 4 -P and TP indicate correct targeting and mitigation of pollution sources within horse keeping operations as promising (Kumblad et al. 2024). Implemented measures (picking up droppings, repairing manure storage facilities, introducing buffer strips along ditches, and fencing off horses from ditches) targeting direct P sources at horse farms were more successful in decreasing P concentrations than the measures targeting to reduce P concentrations in the ditch (LF). ...
Article
Full-text available
Eutrophication of coastal areas is a global problem. A full-scale coastal remediation project was initiated in Björnöfjärden bay in the Stockholm archipelago in 2011. Measures to reduce external nutrient inputs from the surrounding catchment (15 km ² ) targeted agriculture, on-site wastewater treatment facilities, and horse keeping. The effects were evaluated at 22 water quality monitoring stations over 11 years (2012–2022) to determine temporal trends in nutrient concentrations, spatial correlations within and between monitored sub-catchments, and effects of individual mitigation measures at local and catchment scale. The effect of individual measures varied from no significant effect to significant nutrient decreases (21% reduction in dissolved P concentrations in one lime filter) or increases (11% higher concentrations in total P in one constructed wetland). However, few significant trends were detected at sub-catchment outlet stations. Tailored placement, design, dimensioning, and maintenance of implemented mitigation measures are needed to improve their nutrient retention effect.
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Nitrogen (N) and phosphorus (P) losses, via both surface runoff and subsurface drainage water, were monitored in an agricultural field in northern Sweden for 32 yr. The objective was to determine losses of N and P in a long‐term perspective in relation to meteorological factors and impacts of agricultural land use, with a focus on relative contributions of surface runoff and subsurface drainage water to N and P losses. In order to collect surface runoff water, an embankment was installed on three sides of the field, and the fourth side had an open ditch that drove runoff water to a measuring station. Subsurface water draining from the field was collected in a fishbone‐shaped drainage system that terminated at the measuring station. In 50% of years (16/32), mean annual concentration of total N (TN) was significantly higher in subsurface drainage water than in surface runoff water. An opposing trend was seen for total P (TP), with mean annual concentration being significantly higher in surface runoff water than in subsurface drainage water in all but 3 of the 32 yr monitored. Years with a barley crop had higher TN concentration in subsurface drainage water but no difference in surface runoff compared with years with ley. In contrast, years with barley had lower TN concentration in surface runoff than years with ley, with no difference in TP in subsurface drainage water.
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Coastal eutrophication is a major issue worldwide, also affecting the Baltic Sea and its coastal waters. Effective management responses to coastal eutrophication require good understanding of the interacting coastal pressures from land, the open sea, and the atmosphere, and associated coastal ecosystem impacts. In this study, we investigate how research on Baltic coastal eutrophication has handled these interactions so far and what key research gaps still remain. We do this through a scoping review, identifying 832 scientific papers with a focus on Baltic coastal eutrophication. These are categorized in terms of study focus, methods, and consideration of coastal system components and land-coast-sea interactions. The coastal component categories include coastal functions (including also socio-economic driver aspects), pressures that are natural (or mediated by a natural process or system) or directly anthropogenic, and management responses. The classification results show that considerably more studies focus on coastal eutrophication pressures (52%) or impacts (39%) than on characterizing the coastal eutrophication itself (20%). Moreover, few studies investigate pressures and impacts together, indicating that feedbacks are understudied. Regarding methods, more studies focus on data collection (62%) than on linking and synthetic methods (44%; e.g., modelling), and very few studies use remote sensing (6%) or participatory (3%) methods. Coastal links with land and open sea are mentioned but much less investigated. Among the coastal functions, studies considering ecological aspects are dominant, but much fewer studies investigate human aspects and the coastal filter function. Among the coastal pressures, studies considering nutrient loads are dominant, but much fewer studies investigate the sources of these loads, especially long-lived legacy sources and possible solutions for their mitigation. Overall, few studies investigate synergies, trade-offs and incentives for various solutions to address cross-scale multi-solution management.
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Hydrological catchment models are important tools that are commonly used as the basis for water resource management planning. In the 1960s and 1970s, the development of several relatively simple models to simulate catchment runoff started, and a number of so-called conceptual (or bucket-type) models were suggested. In these models, the complex and heterogeneous hydrological processes in a catchment are represented by a limited number of storage elements and the fluxes between them. While computer limitations were a major motivation for such relatively simple models in the early days, some of these models are still used frequently despite the vast increase in computational opportunities. The HBV (Hydrologiska Byråns Vattenbalansavdelning) model, which was first applied about 50 years ago in Sweden, is a typical example of a conceptual catchment model and has gained large popularity since its inception. During several model intercomparisons, the HBV model performed well despite (or because of) its relatively simple model structure. Here, the history of model development, from thoughtful considerations of different model structures to modelling studies using hundreds of catchments and cloud computing facilities, is described. Furthermore, the wide range of model applications is discussed. The aim is to provide an understanding of the background of model development and a basis for addressing the balance between model complexity and data availability that will also face hydrologists in the coming decades.
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Context Nitrogen (N) and phosphorus (P) exports from rural landscapes can cause eutrophication of inland and coastal waters. Few studies have investigated the influence of the spatial configuration of nutrient sources—i.e. the spatial arrangement of agricultural fields in headwater catchments—on N and P exports. Objectives This study aimed to (1) assess the influence of the spatial configuration of nutrient sources on nitrate (NO3⁻) and total phosphorus (TP) exports at the catchment scale, and (2) investigate how relationships between landscape composition (% agricultural land-use) and landscape configuration vary depending on catchment size. Methods We analysed NO3⁻ and TP in 19 headwaters (1–14 km², Western France) every two weeks for 17 months. The headwater catchments had similar soil types, climate, and farming systems but differed in landscape composition and spatial configuration. We developed a landscape configuration index (LCI) describing the spatial organisation of nutrient sources as a function of their hydrological distance to streams and flow accumulation zones. We calibrated the LCI’s two parameters to maximise the rank correlation with median concentrations of TP and NO3⁻. Results We found that landscape composition controlled NO3⁻ exports, whereas landscape configuration controlled TP exports. For a given landscape composition, landscape spatial configuration was highly heterogeneous at small scales (< 10 km²) but became homogeneous at larger scales (> 50 km²). Conclusions The spatial configuration of nutrient sources influences TP but not NO3⁻ exports. An ideal placement of mitigation measures to limit diffuse TP export should consider both the hydrological distance to streams and flow accumulation zones.
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This paper systematically reviews the literature on how to reduce nutrient emissions to the Baltic Sea cost-effectively and considerations for allocating these costs fairly among countries. The literature shows conclusively that the reduction targets of the Baltic Sea Action Plan (BSAP) could be achieved at considerably lower cost, if countries would cooperate to implement the least costly abatement plan. Focusing on phosphorus abatement could be prudent as the often recommended measures—wastewater treatment and wetlands—abate nitrogen too. An implication of our review is that the potential for restoring the Baltic Sea to good health is undermined by an abatement strategy that is more costly than necessary and likely to be perceived as unfair by several countries. Neither the BSAP nor the cost-effective solution meet the surveyed criteria for fairness, implying a need for side-payments.