The potential of hedgerow reintroduction to
improve soil ecosystem delivery in agricultural
landscapes.
Abstract
The maintenance or reintroduction of hedgerows in agricultural landscape has been gaining attention as a natural intervention to improve ecosystem delivery in agricultural landscapes (Holden et al., 2019). The effect of hedgerow reintroduction on several above-ground ecosystem services such as biodiversity, pollinator abundance and biological weed & pest control has been shown in multiple studies, especially on a landscape scale (Dainese et al., 2017, Bátary et al., 2010). However, research on the ability of hedgerows to improve soil-ecosystem services has been lacking. This research investigates the potential of hedgerow reintroduction in areas with relatively low hedgerow densities, focussing on the Netherlands specifically. The hedgerow densities were calculated for three different areas in Europe, one of which was situated in the Netherlands, the other two in France and the UK, and quantifies the effect that this hedgerow density has on four indicators of soil ecosystem delivery: Organic carbon content, water content, sediment interception & nitrogen interception. Higher hedgerow densities were shown to lead to an increase in organic carbon content, sediment interception & nitrogen interception, indicating that higher hedgerow densities improves the delivery of soil-ecosystem services associated with these properties. A decline in water content was seen with an increase in hedgerow density, showing potential of hedgerows to improve water holding capacity of soils in areas with sufficient precipitation, such as the Netherlands. The results of this research
indicate that hedgerows also have the potential to improve soil-ecosystem service delivery significantly, and should be considered as a possible natural intervention to improve the sustainability of the agriculture in the
Netherlands.
Author: Bram Avezaat
Date: 01-07-2020
Location: Amsterdam
Introduction
The intensification of agriculture has had numerous impacts on the environment, including loss of biodiversity (Tilman et al., 2001), water pollution (Schwarzenbach et al., 2010), and soil
degradation (Gibbs & Salmon, 2015). Both pasture and cropland cultivation has shown to lead to a decrease of soil organic carbon (SOC), caused by the removal of organic matter during harvest, and improved decomposition of organic matter through tillage (Smith, 2008). As a consequence of this loss of soil organic matter and compaction caused by the use of heavy machinery, the water holding capacity and infiltration of agricultural soils has decreased remarkably (Chirstensen & Johnston, 1997, Soane & van Ouwekerk, 1995). Furthermore, most intensively used agricultural fields are prone to soil erosion, causing losses of fertile topsoil (Montgomery, 2007). Also, excess usage of fertilizer causes loss of nutrients into the groundwater, and potentially causing eutrophication of nearby surface waters and posing risk to human health (Nixon, 1995, Erisman et al., 2013).
There has been increasing demand for interventions that allow agriculture to provide more ecosystem services than just provision services, which, so far, have been the priority of most agricultural areas (Bennett, 2009). An approach to such an intervention that has been gaining more attention over the last decades is the implementation of hedgerows and field margins to agricultural fields, as (re-)introducing these landscape features into agricultural landscapes could improve the provisioning of multiple ecosystem services, while not hindering the agricultural use of the land (Holden et al., 2019). Hedgerow implementation has been shown to increase biodiversity as it provides a more heterogeneous landscape in the otherwise monoculture- dominated agricultural landscapes (Batáry et al., 2010). Furthermore, hedgerows have been shown to influence natural pest & weed control and pollinator abundance, especially when reintroduction is done on the landscape-scale (Dainese et al., 2017).
However, research on the effect of hedgerows on soil quality, and their related ecosystem services has been limited (Holden et al., 2019). In general, ecosystem services provided by soils have received less attention than ecosystem services provided by above-ground natural capital, even though they are widely acknowledged as forming the basis of many of the ecosystem services provided (Dominati et al, 2010). As the effects of hedgerows on above-ground biodiversity has been shown to be best on landscape level (Dainese et al., 2017), this paper will attempt to quantify the effect of hedgerows on the delivery of soil-ecosystem services on a landscape level, to further improve the understanding of the potential of large-scale hedgerow reintroduction. This estimated effect will be discussed for the Netherlands specifically, as large scale hedgerow removal has occurred in the Netherlands since the end of the second world war due to land consolidation and reintroduction is considered as an option to improve the sustainability of agriculture in this area (Rijkswaterstaat & Staatsbosbeheer, 2013). This was done by selecting three study areas in North-Western Europe and calculating their respective hedgerow densities and the ecosystem delivery associated with these densities. North West Europe was chosen as the most relevant area for this study because running projects associated with INTERREG NWE, a European territorial programme with the aim to improve sustainability and innovation in North West Europe, regarding the potential of hedgerow reintroduction in agricultural landscapes to improve functional biodiversity and
ecosystem service delivery are also conducted in this area (INTERREG NWE, n.d.). To make sure the results of this research are as relevant and useful as possible for these and any other associated projects, the same area was selected.
One of the chosen study areas is situated in the Netherlands, namely the Flevopolder. The other two study areas are in Brittany, France, and Eastern UK and will be used to compare hedgerow
densities, and the amount of soil ecosystem service delivery associated with these densities to the situation in the Netherlands to provide an overview of the potential of hedgerow reintroduction in the Netherlands. The areas were selected based on three criteria: soil texture, land use and elevation level to obtain areas that were similar. The Flevopolder area has a total area of 974.5 Km2, the area in Eastern UK has a total area of 981.2 Km2 and the area in Brittany was slightly smaller than the other two areas with an area of 868.9 Km2.
By using the calculated hedgerow densities, the effect of hedgerows on four indicators of soil quality will be quantified. The soil properties that will be considered are: Soil Organic Carbon, water content, sediment- and nitrogen interception from the surface flow. The selection of these properties is based on a framework for classifying soil ecosystem services proposed by Dominati et al., (2010). Soil organic carbon content is an important indicator of soil fertility, as it impacts water holding capacity, soil structure and nutrient availability (Christensen & Johnston, 1997). Furthermore, the soil carbon pool is the largest terrestrial sink of carbon and is therefore crucial for mitigating the effects of climate change (de Coninck et al., 2018). Water content is an important indicator of the water holding capacity of the soil, which drastically impacts flood susceptibility, especially on a landscape scale. Furthermore, the susceptibility of an area to water erosion due to surface flow is largely dependent on the ability of the soil to store water (Holden et al., 2019). The ability of hedgerows to intercept sediment from the surface flow is another good indicator of the potential of hedgerows to limit erosion in an area by limiting the amount of topsoil that is lost (van Vooren et al., 2017). Lastly, nitrogen interception from the surface flow by hedgerows can help limit the amount of nutrient losses from agricultural lands, which have been associated with several risks to human health and the environment (Erisman et al., 2013). Data on these properties will be selected for hedgerow and cropland soils to quantify the effect of hedgerow abundance on the selected soil properties on a landscape level. After the quantified effect of hedgerows on these properties have been calculated, the implications of the results for the delivery of soil-ecosystem services will be discussed, and the potential of large-scale hedgerow reintroduction in the Netherlands based on these quantifications will be considered.
Methods
The three study areas were selected based on soil texture, land use and elevation level to obtain comparable study areas. Another criteria for the study areas was that they all had to be situated in the area defined as North Western Europe by Interreg North West Europe to make the results as relevant as possible for the associated projects.
Loading and pre-processing the data
Potential study areas were selected based on three criteria: soil texture, land use and elevation level. For each of these criteria, an individual raster file was used. Two of the maps, soil texture and land use, covered the entire Earth’s surface. The elevation (DEM) map was divided into 1000 by 1000 km tiles, and in order to cover the entire area of North Western Europe, 6 of these tiles had to be downloaded and added into the ArcGIS project (Copernicus, n.d.). A polygon was drawn around approximately the same area as the area given by INTERREG NWE1 to create a feature class to be used as clipping extent. Next, the soil texture, land use and elevation map were clipped to fit the area of interest . For the soil texture and land use map, no further pre-processing was needed.
For the DEM map, the 6 different clipped tiles were merged into one raster using the “Mosaic to new raster” tool to allow this raster to be used in further analyses.
The last dataset that had to be loaded in was the small woody features dataset by Copernicus (2015). This dataset contains polygons indicating woody, linear and small patchy elements, referred to as small woody features (SWF) and in addition also includes additional woody features (AWF) that do not meet the geometric requirements, but are connected to SWF features. In this research, both SWF and AWF were considered to represent hedgerows in the landscape.
Selecting appropriate study areas
To select three appropriate study areas, a weighted overlay analysis was performed. This was done by reclassifying each of the raster datasets into a scale of 1-10, with favourable conditions having relatively high scores and unfavourable ones having low scores. Each of the criteria is then given a relative weight, so that the some criteria can have a larger influence than others (see table 1). Ideally, each of the study areas is located on a soil with mostly loam, on a flat, low elevation surface and with mostly cropland and pastures. Therefore, “Loam”, “Agricultural land” and “Low elevation” were given the highest scores. In contrast, “Non-agricultural areas”, “high elevation” and soils consisting of little to no loam were given the lowest scores. “Loamy sand”, “Silt loam” and “Clay loam” were also given a relatively high score of 8 because of their high loam content, and “Sandy loam” a value of 5 caused by the smaller amount of loam present. In the land use dataset, the “mosaic cropland” was given a value of 5, as at least 50% of the area of grid cells with this attribute is supposed to be cropland according to the metadata. Soil type and elevation were given the same relative weights of 0.25, while land use was given a higher weight of 0.5. Land use was given a higher weight because this research focusses solely on the effects of hedgerows in agricultural landscape, while loamy soils and low elevation areas were only preferred. Therefore land use was deemed to be more important for study area selection than soil texture and elevation level. Each of the three criteria
maps were reclassified according to their respective scores using the “Reclassify” function. After this was done, the raster calculator was used to create a new raster showing the best suitable study areas in North Western Europe, with grid cells containing favourable conditions possessing a high score, and ones with unfavourable conditions having a low score (fig. 1).
Scores Elevation
Soil
Texture
Land Use
0
No
values No values No values
1
≤4498.60 ClayAll other classes (e.g. Urban, Natural vegetation)
2
≤3974.66 - -3
≤3450.73 - -4
≤2926.80 - -5
≤2402.86 Sandy loam Mosaic cropland (<50% natural vegetation)6
≤1878.93 - -7
≤1355.00 - -8
≤831.07 Loamy sand, Clay loam, Siltloam -
9
≤307.13 - -10
≤97.77 Loam All cropland classes, GrasslandWeight
0.25 0.25 0.5Table 1: The scores assigned to each of the
attributes in the three raster datasets and the relative weight of each of the criteria.
A) B)
B) C)
Figure 1: Map showing the most suitable study areas (A), the study area in the Flevopolder, The Netherlands (B), the study area close to Hull, UK (C) and the study area in Brittany, France (D).
Based on the values shown on this map, three study areas were identified. The three areas are located in the Flevo polder, The Netherlands (fig.1, B) ), in east England, above the city Hull (fig.1, C) and in the coastal area of Brittany, France (fig. 1, D). As can be seen in the referenced figures, urban areas were excluded as much as possible from the study areas, in order to make the analysis as relevant as possible for agriculture. In terms of soil texture, all three areas were situated on mostly loam. Elevation was slightly higher than optimal in small parts of the area in France, while the areas in England and The Netherlands were both in Brittany while the Flevo polder and the area in the UK were both situated in the optimal elevation class. As for land use, all three areas were used mainly for agriculture, with the exception of a small coverage of tree vegetation in the areas in France and the Netherlands. The SWF datasets were clipped to fit these three selected areas.
Quantifying soil-ecosystem services provided by hedgerows.
To quantify the effect of hedgerows in each of the study areas. The hedgerow density had to be calculated first. This was done by the following equation (1):
Hedgerow Density (HD %) = Hedgerow Area / Total Area* 100% (Equation 1)
The total area and area occupied by hedgerows of each location was calculated using the “Calculate geometry” function in ArcGIS. The hedgerow densities were then used to quantify the effect of hedgerows in each study area on the chosen soil properties; water content, soil organic carbon, nitrogen and sediment interception from the surface flow. Data on these properties was selected from recent studies on the effects of hedgerows on these properties. Studies were selected based on whether the area of data collection was similar to the study area in terms of soil texture, elevation and land use. Furthermore, meta-analyses were preferred as the data from these studies was deemed to be more reliable because of the large sample sizes. After a literature review, data from two studies were used. For the quantification of water content, data from Holden et al. (2019) was used, as the characteristics of the study site of this research were very relevant, and the results were in alignment with the data needed for this report. For the other soil properties, a meta-analysis on the effect of hedgerows on soil ecosystem delivery by van Vooren et al., (2017) was used, which used multiple studies. All sources of data and maps are shown in table 2.
Table 2: Sources for the maps and data on soil properties that were used in this research.
Data Sources
DEM map
Produced by the Copernicus institute, with
funding by the European Union (Copernicus, n.d.).
Soil texture map
Produced by Esri, in collaboration with the Food and Agriculture Organization (FAO) (Esri, 2019).
Land use map
Produced by Esri in collaboration with the European Space Agency (ESA) (Esri, 2019).
Water content
Holden et al., (2019)Soil Organic Carbon content
Paudel et al., (2012), Walter et al., (2003), Bambrik et al., (2010), Cardinali et al., (2014), D’Acunto et al., (2014), Wotherspoon et al., (2014) Oelbermann et al., (2006) and Sharrow and Ismail (2004)
All collected data was added to an excel sheet, in which the difference between values of the soil properties of arable soil and soil underneath hedgerows was calculated. Lastly, the hedgerow density of each study area was used to quantify the effect of hedgerows on the soil properties. All data used was in percentages. The influence of hedgerows on water content, nitrogen interception and sediment interception were calculated in percentages using equation 2, where the “Mean effect” is the effect of hedgerows that was reported for the specific soil property . The mean effects that were reported for these three properties were: a 21% decrease of water content in hedgerow soils compared to cropland soils (Holden et al., 2019), 69% interception of nitrogen from surface flows and 91% interception of sediment from surface flows (van Vooren et al., 2017)
Effect (% Increase/Decrease) = Hedgerow Area / Total Area * Mean effect (%) (Equation 2) The effect on soil organic carbon stocks was converted from percentages to Kg/hectare. This was done by estimating the bulk density of the soil in each area by using data and a method for estimating bulk density by Rawls (1983). Bulk densities were assumed to depend only on organic matter content and soil texture. Rawls (1983) reported a mean organic matter content by weight of loam soils (Hull) of 0.52%, with sandy loam soils (Brittany and the Flevo polder) having a mean organic matter content of 0.71% From these percentages, the percentage of organic carbon content could be estimated using the commonly used conversion factor of 1.72 for the conversion of organic matter to organic carbon (DPIRD, 2019). Using the calculated percentages of organic carbon content and the estimated bulk densities, the carbon content in Kg per M3 was be calculated by using
equation 3. Note that this equation calculates the estimated organic carbon content in each area without the effect of hedgerows.
OC Content [Kg M-3] = Estimated Bulk Density [Kg M-3] * OC fraction in soil (Equation 3)
Now, by multiplying the calculated organic matter content without hedgerows with the reported effect of hedgerows on organic carbon stock, which is a 22% increase (van Vooren et al., 2017), the increase of organic carbon in Kg per hectare was calculated (table 4). Note that the effect was only calculated for the first 30 cm of the soil, as all data on the effect of hedgerows on soil organic carbon stocks was only measured for the first 30 cm (van Vooren et al., 2017). Further elaboration on the method to convert the effect of hedgerows on the soil organic carbon stock from percentages to a weight per area unit is given in
Appendix A.
Nitrogen interception
Wang et al., (2012) and Schoonover et al., (2005)Sediment interception
Schmitt et al., (1999), Duchemin & Hogue (2009), Borin et al., (2005), Schoonover et al.,, (2005), Borin et al., (2010), Udawatta et al., (2011), Yang et al., (2015) Leguédois et al., (2008) and Uusi- Kämppä & Jauhiainen (2010).
Results
Hedgerow Densities
By using the equation given in the methods section, hedgerow densities were calculated for each of the study areas (table 3). The density was highest in the Brittany area (9.7%), and lowest in the Flevo polder (3.0%). Using the hedgerow densities, the effect of these densities on the four selected soil properties was calculated. Note, when an increase or decrease is mentioned in these results, an increase compared to a situation without any hedgerows (thus where hedgerow density would equal 0%) is meant.
Soil Organic Carbon
Soil organic carbon stocks increased in all three study areas, the effect being highest in Brittany (increase of 370.4 Kg per hectare). The effect in the Hull and Flevo polder area are very similar (increase of 120.1 and 116.5, respectively), despite difference in hedgerow density (fig. 2). This can be explained by the amount of organic matter that was assumed to be present without the added effect of hedges in loam soils (Hull), which was lower compared to sandy loam soils (Brittany and Flevo polder) (table 4). For further details, see Appendix A.
Table 4: Table showing the values concerning the equations for estimating the increase in OC Content.
Figure 2: Increase of soil organic carbon (SOC)
stocks.
Table 3: Total area and Hedgerow area of each of the study areas in Km2 and their respective
Water Content
Water content has been shown to be lower in soils underneath hedgerow soils (Holden et al., 2019). Consequently, water content decreased as hedgerow densities increased. The relatively high hedge density in Brittany led to an estimated decrease of 2.07% volumetric water content in the entire area. The effect in the Hull and Flevo polder area was less (0.93% and 0.65% decrease, respectively), as was expected by the lower hedge densities in these areas (fig. 3).
Nitrogen interception
Nitrogen interception from the surface flow has been shown to be significantly greater in hedgerow soils, with an average interception of 69% underneath
hedgerows (van Vooren et al., 2017). Consequently, higher hedgerow densities led to greater interception rates. The quantified effect on the entire study areas were increases of 6.7%, 3.0% and 2.1% for Brittany, Hull and the Flevo polder, respectively (fig. 4).
Sediment interception
Similar to nitrogen interception, sediment interception from surface flow was shown to be increased by hedgerow presence, with an average interception rate of sediment by hedgerows of 91%. The increase of sediment interception rates due to hedgerow density in the study areas are estimated to be 8.7%, 3.9% and 2.7% for Brittany, Hull and the Flevo polder, respectively (fig. 4).
Discussion
High hedgerow densities on the landscape level have been shown to have a positive effect on above-ground biodiversity, pollinator abundance and pest & weed control (Dainese et al., 2017). This research has further expanded the knowledge on the effect of hedgerow abundance on ecosystem delivery by quantifying the effect of hedgerow densities on 4 indicators of soil-ecosystem service delivery (Dominati et al., 2010). Hedgerow density was shown to vary greatly between the study
Figure 3: Percentage decrease in water
content of the area.
Figure 4: Percentage increase in sediment and
areas. This indicates that reintroduction of hedges on a landscape level should be considered, especially in areas with relatively low densities such as the Flevo polder and much of the agricultural land of the Netherlands. Hedgerow density was shown to be positively correlated with each of the soil properties, indicating that reintroduction of hedgerows has significant potential to improve the delivery of soil ecosystem services, in addition to ecosystem services provided above ground.
Organic carbon stocks increased as hedgerow densities increased, leading up to a 370 Kg/ hectare increase with a hedge density of 9.7%. Carbon stored in soils is the largest component of the terrestrial carbon pool. However, up to 100 pG soil organic carbon has been lost due to expansion and intensification of agriculture (Jarecki & Lal, 2003). Restoring the amount of carbon stores
through increased carbon sequestration in agricultural soils has been mentioned by several initiatives and organizations as a crucial part of mitigating the effects of climate change (UNFCCC, 2015, de Coninck et al., 2018) and hedgerow reintroduction could be a part of the solution. In addition to mitigating climate change, an increase of the organic carbon stock also improves soil quality. Organic carbon (as a part of organic matter) has been shown to improve water retention, nutrient availability and soil structure and is therefore crucial for food security (Christensen & Johnston, 1997). The decrease of water content that was observed in hedgerow soils could lead to a higher water storage potential during storm events, as more pore space of the soil is free to retain water. In line with this hypothesis, Holden et al., (2019) found that the time to peak volumetric water content was
significantly greater in hedgerow soils (3.5 hours) compared to arable (2.7 hours) and pasture (2.2 hours) soils. Additionally, hedge soils were found to have greater hydraulic conductivity than arable and pasture soils, showing that surface flow was less likely to occur in landscapes with high
hedgerow densities (Holden et al., 2019). The combination of these results suggests that a higher abundance of hedgerows in the landscape can help increase water retention during storm events, limit surface water flow and consequently reduce the amount of erosion that takes place. The total amount of rainfall and the intensity of rain events is expected to increase in the Netherlands as a consequence of climate change, thus increasing the risk of both floods and runoff erosion (Beersma et al., 2014). Hedgerows can be an effective intervention to adapt to these changing precipitation patterns, and therefore reduce future damage to agricultural lands caused by climate change.
The potential of hedgerows to limit the vulnerability of agricultural lands to erosion was further confirmed by the shown effect on sediment interception from the surface flow. Hedgerows have a very large influence on sediment interception, intercepting 91% of sediment from the surface flow on average (van Vooren et al., 2019). This large effect was clearly seen on the landscape level. In Brittany, sediment interception was increased by 8.7%, compared to an increase of 2.7% in the Flevo polder. A decrease of sediment loss of 9% per runoff event can have significant impacts on the long-term fertility of the soil, as more top soil is being conserved. However, it should be noted that the topography of the Flevo polder and Hull were relatively flat, while there was a larger elevation gradient in Brittany. Agricultural lands on slopes are more susceptible to runoff erosion and therefore the effect of hedgerows on sediment interception is expected to be more pronounced in these areas. Furthermore, the rate of interception is dependent on the direction of the water flow. If the water flow dispersed around the hedgerows, the system will be less effective, as was noted by van Vooren et al., (2019). Therefore, local topography should be taken into account when
Lastly, hedgerows were shown to have a significant impact on intercepting nitrogen from the surface flow (van Vooren et al., 2019). This further illustrates the benefits that hedgerow presence can provide humans as well as ecosystems, as excess nitrogen has been correlated with several risks for human health and the environment (Erisman et al., 2013). Excess nitrogen in the form of nitrate (NO3) in groundwater has been shown to pose a risk to human health. Furthermore, excess nitrogen input into surface waters leads to excess nutrient availability, causing harmful algal blooms (Erisman et al., 2013). These blooms have several detrimental effects on the environment, such as the release of toxic compounds, oxygen depletion through sedimentation of dead algal biomass and as a
consequence, trophic cascades (Camargo & Alonso, 2006, Rabalais et al., 2002). In the Netherlands, while improvements have been made in reducing the amount of nutrient input into surface waters from agricultural land, excess nitrate is still causing significant damage to the environment (Fraters et al., 2016). Hedgerow densities had a pronounced effect on the amount of nitrogen that was
intercepted from the surface flow, therefore reducing nitrogen losses into surface waters.
Furthermore, hedgerows have been shown to take up nitrate from shallow groundwater, indicating that the intercepted nitrogen is used by the vegetation, thereby not polluting groundwater (Grimaldi et al., 2012).
In conclusion, in addition to the widely documented effects of hedgerows on the landscape scale non-soil ecosystem services such as pollination, pest & weed control and overall biodiversity (Dainese et al., 2017, Sutter et al., 2018), hedgerows have been shown to have significant positive impacts on the delivery of soil ecosystem services. This further broadens the knowledge base on which policy decisions regarding reintroduction of heterogeneous landscape elements such as hedgerows should be based. The results of this study show that hedgerows could provide a natural intervention to lessen the consequences of many of the problems that modern agriculture faces today, such as susceptibility to erosion (Holden et al., 2019), biodiversity loss (Tilman et al., 2001), a decrease of soil organic carbon stores (Jarecki & Lal, 2003) and losses of nutrients into aquatic ecosystems (Erisman et al., 2013). As hedgerow density is relatively very low in most parts of the Netherlands, hedgerow reintroduction should be considered as a natural intervention to improve the delivery of ecosystem services.
Further research should focus on integrating knowledge of the impact of hedgerows on ecosystems and their effect on economical properties such as crop yield to fully illustrate the trade-offs and potential that reintroduction of these landscapes elements have. Additionally, this research only focussed on 4 soil properties and their corresponding ecosystem services. The effects of
hedgerows on soil biota, soil structure, aggregate stability and many more soil properties are relevant to discuss, but were beyond the scope of this research. Furthermore, this research was limited by the use of data from other studies, and no on-site data was collected. This could influence the results greatly, as these are estimates of the actual situation, and further use of data from this paper should therefore be done carefully. The accuracy of the potential study area map (fig 1.) was assessed using a method proposed by Hengl (2006). At the largest scale (1:20,000,000), a grid size between 50 Km and 2 Km was recommended. Level 8 pyramids were built in the potential study area map, with the resolution at the finest scale being 25m. As the grid size doubles with each pyramid level (Esri, n.d.) , the grid size at the highest pyramid level is 25*28 = 6400 m, thus fitting within the recommended range.
References
- Bambrick, A. D., Whalen, J. K., Bradley, R. L., Cogliastro, A., Gordon, A. M., Olivier, A., & Thevathasan, N. V.
(2010). Spatial heterogeneity of soil organic carbon in tree-based intercropping systems in Quebec and Ontario, Canada. Agroforestry systems, 79, 343-353.
- Batáry, P., Matthiesen, T., & Tscharntke, T. (2010). Landscape-moderated importance of hedges in
conserving farmland bird diversity of organic vs. conventional croplands and grasslands. Biological Conservation, 143, 2020-2027.
- Beer, A. (n.d.). A natural history of hedgerows. The Countryside Charity. Retrieved 24 June 2020, from
https://www.cpre.org.uk/discover/a-natural-history-of-hedgerows/
- Beersma, J., Bessembinder, J., Van den Hurk, B., & Lenderink, G. (2014). KNMI 14: Klimaatscenario's voor Nederland. KNMI publicatie.
- Bennett, E. M., Peterson, G. D., & Gordon, L. J. (2009). Understanding relationships among multiple
ecosystem services. Ecology letters, 12, 1394-1404.
- Borin, M., Passoni, M., Thiene, M., & Tempesta, T. (2010). Multiple functions of buffer strips in farming areas. European journal of agronomy, 32, 103-111.
- Borin, M., Vianello, M., Morari, F., & Zanin, G. (2005). Effectiveness of buffer strips in removing pollutants in runoff from a cultivated field in North-East Italy. Agriculture, ecosystems & environment, 105, 101-114. - Camargo, J. A., & Alonso, Á. (2006). Ecological and toxicological effects of inorganic nitrogen pollution in
aquatic ecosystems: a global assessment. Environment international, 32, 831-849.
-
Cardinali, A., Carletti, P., Nardi, S., & Zanin, G. (2014). Design of riparian buffer strips affects soil quality parameters. Applied soil ecology, 80, 67-76.
- Christensen, B. T., & Johnston, A. E. (1997). Soil organic matter and soil quality—lessons learned from
long-term experiments at Askov and Rothamsted. In Developments in soil science , 25, 399-430.
- de Coninck, H., A. Revi, M. Babiker, P. Bertoldi, M. Buckeridge, A. Cartwright, W. Dong, J. Ford, S. Fuss, J.-C. Hourcade, D. Ley, R. Mechler, P. Newman, A. Revokatova, S. Schultz, L. Steg, and T. Sugiyama, (2018), Strengthening and Implementing the Global Response. In: Global Warming of 1.5°C. An IPCC Special Report
on the impacts of global warming of 1.5°C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development, and efforts to eradicate poverty
- Copernicus (n.d.), EU-DEM V1.1. Retrieved 12 June 2020, from
https://land.copernicus.eu/imagery-in-situ/eu-dem/eu-dem-v1.1?tab=metadata
- Copernicus (2015), Small Woody Features 2015, Retrieved 12 June 2020, from
https://land.copernicus.eu/pan-european/high-resolution-layers/small-woody-features/small-woody-features-2015
-
D’Acunto, L., Semmartin, M., & Ghersa, C. M. (2014). Uncropped field margins to mitigate soil carbon losses in agricultural landscapes. Agriculture, Ecosystems & Environment, 183, 60-68.- Dainese, M., Montecchiari, S., Sitzia, T., Sigura, M., & Marini, L. (2017). High cover of hedgerows in the
landscape supports multiple ecosystem services in Mediterranean cereal fields. Journal of Applied Ecology, 54, 380-388.
- Dominati, E., Patterson, M., & Mackay, A. (2010). A framework for classifying and quantifying the natural
capital and ecosystem services of soils. Ecological economics, 69, 1858-1868.
- Department of Primary Industries and Regional Development (2019), What is soil organic carbon,
Retrieved 13 June 2020, from
https://www.agric.wa.gov.au/measuring-and-assessing-soils/what-soil-organic-carbon
- Duchemin, M., & Hogue, R. (2009). Reduction in agricultural non-point source pollution in the first year following establishment of an integrated grass/tree filter strip system in southern Quebec (Canada). Agriculture, Ecosystems & Environment, 131, 85-97.
- Schmitt, T., Dosskey, M. G., & Hoagland, K. D. (1999). Filter strip performance and processes for different vegetation, widths, and contaminants. Journal of Environmental Quality, 28, 1479-1489.
- Erisman, J. W., Galloway, J. N., Seitzinger, S., Bleeker, A., Dise, N. B., Petrescu, A. R., ... & de Vries, W.
(2013). Consequences of human modification of the global nitrogen cycle. Philosophical Transactions of the Royal Society B: Biological Sciences, 368, 20130116.
- Esri (2019), World Land Cover ESA 2010, retrieved 28 May 2020, from
https://www.arcgis.com/home/item.html?id=7173340debc240a9b7ee5aec230e099c
-
Esri (2019), World soils harmonized soil database – Texture, retrieved 28 May 2020, fromhttps://www.arcgis.com/home/item.html?id=aa9a3a2dc6924f46adc5a999787f7961
-
Esri (n.d.) Raster Pyramids, Retrieved 27 June 2020, fromhttps://desktop.arcgis.com/en/arcmap/10.3/manage-data/raster-and-images/raster-pyramids.htm - Fraters, B., Hooijboer, A. E. J., Vrijhoef, A., Claessens, J., Kotte, M. C., Rijs, G. B. J., ... & Bosma, J. N. (2016).
Landbouwpraktijk en waterkwaliteit in Nederland; toestand (2012-2014) en trend (1992-2014): Resultaten van de monitoring voor de Nitraatrichtlijn.
- Grimaldi, C., Fossey, M., Thomas, Z., Fauvel, Y., & Merot, P. (2012). Nitrate attenuation in soil and shallow
groundwater under a bottomland hedgerow in a European farming landscape. Hydrological Processes, 26, 3570-3578.
- Hengl, T. (2006). Finding the right pixel size. Computers & geosciences, 32, 1283-1298.
- Holden, J., Grayson, R. P., Berdeni, D., Bird, S., Chapman, P. J., Edmondson, J. L., & Jones, D. T. (2019). The
role of hedgerows in soil functioning within agricultural landscapes. Agriculture, ecosystems & environment, 273, 1-12.
- INTERREG NWE (n.d.) , Fabulous Farmers, Retrieved 24 June 2020, from
https://www.nweurope.eu/projects/project-search/fabulous-farmers/
- Jarecki, M. K., & Lal, R. (2003). Crop management for soil carbon sequestration. Critical Reviews in Plant
Sciences, 22, 471-502.
- Leguédois, S., Ellis, T. W., Hairsine, P. B., & Tongway, D. J. (2008). Sediment trapping by a tree belt: processes and consequences for sediment delivery. Hydrological Processes: An International Journal, 22, 3523-3534.
- Montgomery, D. R. (2007). Soil erosion and agricultural sustainability. Proceedings of the National Academy of Sciences, 104, 13268-13272.
- Nixon, S. W. (1995). Coastal marine eutrophication: a definition, social causes, and future concerns.
Ophelia, 41, 199-219.
- Oelbermann, M., Voroney, R. P., Thevathasan, N. V., Gordon, A. M., Kass, D. C., & Schlönvoigt, A. M. (2006). Soil carbon dynamics and residue stabilization in a Costa Rican and southern Canadian alley cropping system. Agroforestry Systems, 68, 27-36.
-
Paudel, B. R., Udawatta, R. P., Kremer, R. J., & Anderson, S. H. (2012). Soil quality indicator responses to row crop, grazed pasture, and agroforestry buffer management. Agroforestry Systems, 84, 311-323. - Rabalais, N. N., Turner, R. E., & Scavia, D. (2002). Beyond Science into Policy: Gulf of Mexico Hypoxia and
the Mississippi River: Nutrient policy development for the Mississippi River watershed reflects the
accumulated scientific evidence that the increase in nitrogen loading is the primary factor in the worsening of hypoxia in the northern Gulf of Mexico. BioScience, 52, 129-142.
- Rawls, W. J. (1983). Estimating soil bulk density from particle size analysis and organic matter content1. Soil
Science, 135, 123-125.
-
Rijkswaterstaat & Staatsbosbeheer. (2013). Heggen langs de grote rivieren: Aanpak bestaande heggen in
het programma inhaalslag Stroomlijn van Rijkswaterstaat. Retrieved 27 June 2020 from
https://ijsselhoeven.nl/sites/default/files/uploaded-files/rapport_heggen_langs_de_grote_rivieren_-_september_2013.pdf
- Schmitt, T., Dosskey, M. G., & Hoagland, K. D. (1999). Filter strip performance and processes for different vegetation, widths, and contaminants. Journal of Environmental Quality, 28, 1479-1489.
- Schoonover, J. E., Williard, K. W., Zaczek, J. J., Mangun, J. C., & Carver, A. D. (2005). Nutrient attenuation in agricultural surface runoff by riparian buffer zones in southern Illinois, USA. Agroforestry Systems, 64, 169-180.
- Schwarzenbach, R. P., Egli, T., Hofstetter, T. B., Von Gunten, U., & Wehrli, B. (2010). Global water pollution
and human health. Annual Review of Environment and Resources, 35, 109-136.
- Sharrow, S. H., & Ismail, S. (2004). Carbon and nitrogen storage in agroforests, tree plantations, and pastures in western Oregon, USA. Agroforestry systems, 60, 123-130.
- Smith, P. (2008). Land use change and soil organic carbon dynamics. Nutrient Cycling in Agroecosystems,
81, 169-178.
-
StructX (2020), Density ranges for different soil types, Retrieved on 19 June 2020 from https://structx.com/Soil_Properties_002.html
- Sutter, L., Albrecht, M., & Jeanneret, P. (2018). Landscape greening and local creation of wildflower strips
and hedgerows promote multiple ecosystem services. Journal of applied ecology, 55, 612-620.
- Soane, B. D., & Van Ouwerkerk, C. (1995). Implications of soil compaction in crop production for the quality
of the environment. Soil and Tillage Research, 35, 5-22.
- Solheim, A. L., Austnes, K., Eriksen, T. E., Seifert, I., & Holen, S. (2010). Climate change impacts on water
- Tilman, D., Fargione, J., Wolff, B., D'Antonio, C., Dobson, A., Howarth, R. et al. (2001) Forecasting
agriculturally driven global environmental change. Science, 292, 281– 284.
- Udawatta, R. P., Garrett, H. E., & Kallenbach, R. (2011). Agroforestry buffers for nonpoint source pollution reductions from agricultural watersheds. Journal of environmental quality, 40, 800-806.
- UNFCCC. (2015). Join the 4/1000 Initiative. Soils for Food Security and Climate.
- Uusi-Kämppä, J., & Jauhiainen, L. (2010). Long-term monitoring of buffer zone efficiency under different cultivation techniques in boreal conditions. Agriculture, ecosystems & environment, 137, 75-85.
- van Vooren, L., Bert, R., Steven, B., Victoria, N., Paul, P., & Kris, V. (2017). Ecosystem service delivery of
agri-environment measures: a synthesis for hedgerows and grass strips on arable land. Agriculture, Ecosystems & Environment, 244, 32-51.
-
Walter, C., Merot, P., Layer, B., & Dutin, G. (2003). The effect of hedgerows on soil organic carbon storage in hillslopes. Soil Use and Management, 19, 201-207.
- Wang, L., Duggin, J. A., & Nie, D. (2012). Nitrate–nitrogen reduction by established tree and pasture buffer strips associated with a cattle feedlot effluent disposal area near Armidale, NSW Australia. Journal of environmental management, 99, 1-9.
-
Wotherspoon, A., Thevathasan, N. V., Gordon, A. M., & Voroney, R. P. (2014). Carbon sequestration potential of five tree species in a 25-year-old temperate tree-based intercropping system in southern Ontario, Canada. Agroforestry systems, 88, 631-643.- Yang, F., Yang, Y., Li, H., & Cao, M. (2015). Removal efficiencies of vegetation-specific filter strips on nonpoint source pollutants. Ecological Engineering, 82, 145-158.
Appendix
Appendix A: Quantifying effect on Organic Carbon Stocks in weight.
In order to convert the change in organic carbon stocks from percentage to a weight per a given area, additional information was needed. Firstly, a bulk density of the study areas had to be estimated. According to Rawls (1983), bulk density can be estimated based on the soil particle distribution, and the fraction of organic matter present in soils, using the following equation shown in figure 5.
Figure 5: Equation given by Rawls (1983) to estimate soil bulk density based on organic matter content and
To estimate the average soil particle distribution of the three study areas, the data given by the previously used soil texture map was used. To do so, the soil texture map was clipped to fit the three areas. However, when using the polygons of each study area as clipping input, and using this input feature for clipping geometry led to a loss of all attributes of the layer, the “use input features for clipping geometry” box had to be unchecked in the clip tool in ArcGIS Pro. This resulted in a coarser output raster, and therefore a less than optimal accuracy of the average soil texture estimations. To calculate the mean particle size distribution of each study area, the mean values of the “T_Sand”, “T_Silt” and “T_Clay” attribute fields were calculated, which resulted in the values given in table 5.
Table 5: Average soil particle distributions for the study areas
Mineral bulk densities of loam was assumed to be 1430 Kg/m3 for loam, and 1460 Kg/m3 for sandy loam (StructX, 2020).
When using a bulk density of organic matter of 224 Kg/m3 as proposed by Rawls (1983), filling in the equation given in figure 6 results in the following estimated bulk densities: 1391.055 Kg/m3 for the Hull area, with both Brittany and the Flevo polder having an estimated bulk density of 1404.958 Kg/m3. The next step to estimate the increase in the carbon stocks was to estimate the OC content in the soils of the areas without hedges, so that these values can be used to quantify the effect of the hedgerows. Rawls (1983) reported a mean organic matter content of loam soils (Hull) of 0.52% with a standard deviation of 0.99, with sandy loam soils having a mean organic matter content of 0.71% with a standard deviation of 1.29. From these percentages, the percentage of organic carbon content could be estimated using the commonly used conversion factor of 1.72 for the conversion of organic matter to organic carbon (DPIRD, 2019). Then, using the calculated effect in percentages of hedgerows on carbon stock, and the estimated bulk densities, the carbon stock increase in Kg/hectare was calculated (table 3).
