Soil erosion – The impact of grazing and regrowth trees

Geoderma 361 (2020) 114102

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Soil erosion – The impact of grazing and regrowth trees a,⁎

a

b

b

T a

G.R. Hancock , M. Ovenden , Kushagra Sharma , Walter Rowlands , Abraham Gibson , T. Wellsb a b

School of Environmental and Life Sciences, The University of Newcastle, Callaghan, New South Wales 2308, Australia School of Engineering, The University of Newcastle, Callaghan, New South Wales 2308, Australia

A R T I C LE I N FO

A B S T R A C T

Keywords: Soil erosion Environmental tracer 137 Cs Sediment transport Vegetation and erosion

It is well recognised that excessive soil erosion is a significant environmental issue for disturbed landscape systems. This is particularly the case for agriculture, where soil loss is not only a source of pollution but impacts soil fertility and sustainability. In recent years, improved pasture management, rotational grazing and holistic management practices have been employed and it is generally believed that soil erosion has been reduced to acceptable levels. For many landscapes, there has been a lack of ecological diversity and trees have been planted or allowed to regrow to provide ecological services as well as provide shade and windbreaks. However, in many circumstances, observed grass cover is reduced leaving soil exposed. While these practices are well-accepted, there has been little assessment of their impact on soil erosion and general soil properties. Here, we examine a grazing (pasture) landscape typical of many on the east coast of Australia and calculate erosion rates using an environmental tracer. We find that with the current management practices the soil loss is relatively low (< 5 tonnes/ha/yr). However, it is likely to be an order of magnitude greater than soil production rates. Regrowth trees maintained to provide shelter and ecological services did not result in higher soil loss despite observed and measured reduced ground cover both under and surrounding the tree plot.

1. Introduction The most important aspect for agriculture is the economic and environmental sustainability of the soil resource (Zapata et al., 2002; Minasny et al., 2018). Soil provides the foundation for terrestrial based agricultural systems and without a sustainable soil resource, agriculture will ultimately fail (Blanco and Lal, 2008). A significant issue with all agricultural systems is the loss of soil due to erosion (Bui et al., 2011). Soil erosion is a natural process that can be exacerbated by human land management (Hazelton and Murphy, 2007). For many landscape systems, deforestation has occurred with grassland developed to support cattle grazing. For many areas, particularly in south-east Australia, clearing occurred many decades previously (in this study over a century) with the landscape now being managed for pasture. However, for many sites, trees have been planted or encouraged to re-establish due to the increasing recognition of their economic and environmental benefits. In recent years there have been a dearth of studies examining soil erosion in Australia and there is a need to reassess whether current management practices are economically and environmentally sustainable (Krause et al., 2003; Hancock et al., 2014). Replanting of trees in

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areas that have been pasture or allowing natural regrowth of trees to occur (i.e. the focus of this study) is increasingly common. In many areas it has been recognised that there are many benefits of both planting and encouraging the tree regrowth in grazing landscape systems (Sauer et al., 2007; Minasny et al., 2018). Trees provide shelter for cattle, windbreaks reducing wind erosion and evaporative loss as well as supporting habitats for native flora and fauna which can provide ecological services. However, these regrowth areas often have no pasture under their canopy, use more water than the surrounding pasture and can be seen to have negative environmental and economic benefits also (George et al., 2012; Willgoose, 2018). One of the greatest benefits of revegetation can be to the improvements in soil quality and sustainability of soil. Restoring vegetation can provide increased Soil Organic Carbon (SOC) and improved water holding capacity and soil structure (Cunningham et al., 2012; George et al., 2012; Davey and Koen, 2013; Korkanc, 2014; Baah-Acheamfour et al., 2015; Cardinael et al., 2015; Minasny et al., 2018). Studies that directly analysed the belowground benefits of shelterbelts have primarily focused on their influence within cropping systems rather than grazing operations (Walter et al., 2003; Follain et al., 2007; Sauer et al., 2007; Lenka et al., 2012; Korkanc, 2014; Cardinael et al., 2015). These

Corresponding author. E-mail address: [email protected] (G.R. Hancock).

https://doi.org/10.1016/j.geoderma.2019.114102 Received 3 June 2019; Received in revised form 4 November 2019; Accepted 17 November 2019 0016-7061/ Crown Copyright © 2019 Published by Elsevier B.V. All rights reserved.

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at no set weights. Stocking rates for the site are one animal per hectare (or less) which is the general carrying capacity for the area. The cattle are rotationally grazed (moved to different paddocks) according to available feed and pasture management based on landowner experience. Additional land management including slashing (mowing) of grass to improve the species mix and palatability, as well as the occasional use of fire to reduce wildfire risk as well as manage pasture is employed. The site has many areas where trees have been allowed to spontaneously regrow in the last 20 years with the majority being Eucalyptus Canaliculata (Grey Gum) and Laevopinea (Stringybark) (Fig. 2). The site is located within the southern New England Fold Belt with Carboniferous sediments characterised by thickly bedded green sandstone, with varying quantities of brown mudstone, conglomerate, and limestone (Roberts, et al., 1991; Krause, et al., 2003). Soils at the site are well-drained and firm with no visible evidence of erosion and no salting evident. The soils are Haplic Mesotrophic Brown Chromosol (Australian Soil Classification) or Yellow Podzol (Great Soils Group) or Ultisols (USDA). The study area has a moist, warm temperate climate (Cornish, 1993) with mean annual rainfall between 1450 and 1750 mm. There is typically a seasonal maximum during summer months (November to February) and a seasonal minimum in the winter months (June to August). Large rainfall events do occur and are caused by low pressure systems that develop of the coast with the last one being in 2007 (Verdon-Kidd et al., 2014). Maximum summer temperature average is 28 °C and in winter minimum temperatures throughout the area drop to an average of 3 °C (www.bom.gov.au). Frosts are common in winter.

studies have highlighted that fenced off areas for ecological service areas and the installation of shelterbelt structures do increase SOC with some estimates ranging from 38% (Lenka et al., 2012) to 55% (Sauer et al., 2007; Cardinael et al., 2015) depending on age (Follain et al., 2007; Hancock and Coulthard, 2012; Baah-Acheamfour et al., 2015). Other benefits to the soil profile include a decrease of bulk density and soil compaction associated with increased SOC, macro-aggregation and subsurface microbial activity (Lenka et al., 2012; Korkanc, 2014; Cardinael et al., 2015; Stockmann et al., 2015; Minasny et al., 2018). In this study, measurements of soil erosion, soil depth, and other soil properties across transects through pasture and revegetated sections are taken to assess the impact of these practices on the sustainability of the soil resource and their practicality at the farm-scale. There are several methods to quantify soil erosion (Blanco and Lal, 2008; Morgan and Nearing, 2011). The most reliable and detailed approach is to construct sediment traps and or weirs on a hillslope or in a channel which can quantify all sediment movement (Hancock et al., 2000; 2017a). The difficulty with this approach is that such apparatus is time consuming and expensive to construct and maintain. On active farms (such as this site) they are also subject to damage by cattle. Erosion pins are another method, which are inexpensive and easy to maintain, but are also subject to damage by livestock (Hancock and Lowry, 2015; ; Boardman and Favis-Mortlock, 2016). Surveying methods are appropriate where there is rapid or noticeable change where rills and gullies can be measured by a theodolite (Hancock et al., 2000). Other methods, such as digital photogrammetry, are very useful where there is no vegetation but in grazing landscapes where there is a dense grass cover complex algorithms must be used to remove vegetation from the data set which can lead to accuracy issues. Light Detecting and Ranging (LiDAR) offers great potential as it can ‘see through’ vegetation, however it is still not readily available at sufficiently low cost for many research teams. All surveying methods require repeat measurements at a later date to determine rates of change and the calculation of erosion rates (Morgan and Nearing, 2011). Here we use the environmental tracer 137Cs to determine soil erosion and deposition rates and patterns (Loughran, 1994; Zapata et al., 2002; Walling et al., 2011; Hancock et al., 2014). 137Cs is a by-product of the atmospheric testing of nuclear weapons that terminated in the early 1970s. Quantifying 137Cs concentration and its distribution allows both soil erosion and deposition patterns and rates to be determined on average over an approximate 50-year period. While soil loss is important to quantify, it needs to be placed in the context of soil production and soil depth (Smith and Stamey, 1965; Stockmann et al., 2011; Bui et al., 2011). There are many questions around whether current soil loss rates for both natural (undisturbed) and agriculturally managed landscapes are in equilibrium with soil production rates. This centres on the question of how sustainable our soil resource is. This research is part of a series long-term projects to quantify soil erosion and deposition rates and patterns to better understand our soil resources in natural, post-mining and agricultural landscape systems (Hancock et al., 2000; 2008; 2014; Martinez et al., 2009; Hancock et al., 2017a,b). Here we examine soil erosion rates in a grazing environment typical of that of the east coast of Australia. We also examine the influence of natural regrowth of trees on soil erosion. The results are placed in the context of the sustainable soil resource.

3. Methods The project uses environmental tracers to quantify soil erosion and deposition. The hillslope sampling, 137Cs reference site and data together with soil sampling and topographic data are described in the following section.

3.1. Sampling methodology Here we use a transect based sampling methodology (Pennock and Appleby, 2002). Three transects (Transects 1, 2 and 3, Table 1) ran from the catchment divide and terminated in an ephemeral first order channel (Fig. 2). The site is characterised by a consistent easterly aspect, uniform slope (Table 1) and uniform use of the land (grazing) and uniform soil type. Transects 1 and 2 were exclusively pasture while Transect 3 ran through an area of eucalypt regrowth. All transects ran from the drainage divide (top of hill) to the zero-order drainage line (bottom of hillslope). For each transect soil samples were collected at regular spacings so that the entire toposequence was sampled (Pennock and Appleby, 2002). Approximately 20 samples were collected for each transect for consistency (Table 1). While different months, Transect 1 was sampled in May 2016, Transect 2 in March 2017 and Transect 3 in March 2018 there were no extreme rainfall events during this time (Verdon-Kidd et al., 2014). An alternative stratified sampling method could have been employed, however inspection of the site suggested no reason for this (denser or sparser sampling) and samples at regular spacings were collected. Samples were collected in steel cores of 95 mm internal diameter and length 210 mm. For insertion, a steel cap (‘dolly’) was placed on top of the core and a hammer used to insert the core until flush with the soil surface (Loughran, 1994; Hancock et al., 2014). The sample depth was sufficiently deep to capture the whole 137Cs depth distribution profile (as shown in Fig. 3). The location of each point was recorded by differential Global Positioning System (GPS).

2. Site description The study site is located in the Mulconda Creek catchment, a tributary of the Williams River, 16 km north of Dungog, NSW, Australia (Fig. 1). The property is used for beef cattle grazing with native and improved pasture being the dominant vegetation. This includes breeders (females), calves and steers. The cattle are sold when market conditions are favourable or when feed and rainfall conditions deem selling is the most profitable way forward. Therefore the cattle are sold 2

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Fig. 1. Location of the study site.

Fig. 2. Sampling Transect 1 in 2016 and Transects 2 and 3 as shown by the orange line. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.) 3

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Diffusion and migration coefficients are required for the calculation of erosion rates using 137Cs (see Section 4). These were determined from soil samples obtained using a 20 cm × 50 cm, (1000 cm2) scraperplate and frame (Campbell et al., 1988) at the hillslope crest from within the site where the 9 cores were collected. The scraper plate samples were collected in 20 mm depth increments from the surface to a final depth of 200 mm.

Table 1 Hillslope transect and sampling data for the Mulconda Park site.

Transect 1 Transect 2 Transect 3

Length (m)

Relief (m)

Average slope (tan)

No. of samples

Sample spacing (m)

120 74 114

24.9 14.9 24.8

0.21 0.20 0.22

21 23 20

6 3.5 6

3.3. Soil processing 0-20

Upon extraction, the soil samples were placed in a labelled bag and transported to The University of Newcastle. The soil samples were placed in a 40 °C oven to dry for one week, checked for moisture content, and then placed in the oven again for a further 24 h and reweighed to ensure they were fully dry. The samples were then disaggregated using a mortar and pestle and the less than 2 mm and > 2 mm size fraction separated by sieving. The fine soil fraction was further disaggregated by mortar and pestle, and both fractions weighed. The hydrometer method, (Smith and Atkinson, 1975), was employed to determine the percent sand, silt and clay content of the fine soil fraction. Electrical Conductivity (EC) and pH of a 1:5 (soil: distilled water) solution from each sample was measured using a Eutech EC and pH instrument. SOC and soil Nitrogen (soil N) analysis was conducted using the LECO method by EAL Lismore. Soil pH was always less than 7 so no correction for carbonates was necessary.

20-40 40-60

depth (mm)

60-80 80-100 100-120 120-140 140-160 160-180 180-200 0

20

40

60 137

Fig. 3.

3.2.

137

137

80

100

120

2

Cs (Bq/m )

3.4. Soil depth measurement

Cs depth distribution profile obtained from the scraper plate.

Soil depth has a strong influence on hillslope hydrology and sediment transport (Willgoose, 2018). Soil depth was determined at each transect sample point by driving a petrol powered 35 mm auger down into the soil profile until refusal. Three depth measurements were taken at each point with the maximum value used as soil depth. Using this procedure provided a maximum soil depth and avoided the issue of floating core stones producing an artificially low soil depth (Hancock et al., 2014).

Cs method and reference site

137 Cs (half-life 30.1 years) was generated during the atmospheric testing of nuclear weapons and is strongly adsorbed to the fine fraction of soil (clay) after falling to earth. The concentration and distribution of 137 Cs can be used to determine medium-term (~50-year-old) soil erosion/deposition behaviour (Loughran, 1994; Campbell et al., 1988; Loughran et al., 2002; 2004; Zapata et al., 2002; Martinez et al., 2009; Hancock et al., 2014). Soil erosion and deposition can be calculated from the measurement of the accumulation and depletion of 137Cs at a point relative to concentrations at an erosionally stable reference site. Here a reference inventory site was located at the hillslope crest of the three transects. There were no trees, rain shadow or evidence of disturbance to influence the fallout of 137Cs. Nine soil cores extending from the surface to 210 mm depth taken on an approximate 1 m grid pattern. These provided an inventory level and spatial variability of 137 Cs at the reference site. To further quantify the spatial variability of 137 Cs, a further series of 210 mm deep cores were collected along the hillslope crest. There were located 100 m and 50 m to the north, and 50 m to the south of the reference cores. At these locations four cores were collected. In summary, 25 cores were collected to determine the spatial variability of 137Cs (Table 2).

3.5. Digital elevation model New technology, such as high-resolution GPS, allows digital mapping of soil properties and landscape features (Minasny et al., 2013). A Trimble 4700 base station and rover (Differential GPS) was usedto create a digital elevation model of the site. The hillslope was systematically traversed by foot where elevations were recorded automatically every 2 m with additional points collected along and around the hillslope transect. Ordinary kriging was used to grid the data using Surfer 11 software. The accuracy of the system was approximately 20 mm in the X and Y (horizontal) and 25 mm in the Z (vertical) using the CORSnetNSW system (www. http://spatialservices.finance.nsw.gov. au/corsnet-nsw). The Tarboton et al. (1992) method was used to remove the few pits in the DEM. 3.6. Statistical analysis

Table 2 137 Cs control site data (Bq m2).

Basic assessments were conducted using linear regression and student t-tests in Excel and KaleidaGraph software. Additional statistical analysis (Anova) was conducted using Excel software. All graphs and figures outlining relevant results were developed using this program with plots developed and additional analysis displayed using KaleidaGraph.

parameter Year of sampling Number of samples Mean concentration Standard Deviation Standard error Coefficient of variation (%) 95% confidence interval Upper 95% confidence interval Lower 95% confidence interval Upper 20% confidence interval Lower 20% confidence interval

2016 25 612 93.4 6.0 15.3 60.2 672.4 552.0 734.7 489.8

4.

137

Cs analysis

Dried soil samples of approximately 1000 g were placed in Marinelli containers and gamma emissions were counted on a hyper-pure germanium spectrometer over a period of 24 h where counting errors of 4

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approximately +/− 10% or less were achieved. The Loughran (1994) method was utilised to determine the concentration of 137Cs in the soil samples which were then converted to areal activity (Loughran et al., 2002). Here we use the Diffusion and Migration Model (DMM) as it is the most appropriate approach for converting 137Cs levels observed into soil redistribution rates at uncultivated sites (Walling and He, 2001; Martinez et al., 2009; Walling et al., 2011). The DMM assumes 137Cs distribution throughout the soil column is the result of the combination of lateral soil movement of surface soil layers and vertical movement of 137 Cs down through the soil profile (Pegoyev and Fridman, 1978; He and Walling, 1997; Walling and He, 1999; Walling et al., 2011). In the DMM, the processes that drive vertical transport of 137Cs within the soil profile are characterised by a diffusion coefficient D, (kg2 m−4 yr−1), and migration rate, V, (kg m−2 yr−1) (Walling and He, 1999, 2001; Walling et al., 2011). To determine D and V values, a vertical 137Cs profile was constructed from the analysis of 20 mm deep incremental scraper plate soil samples taken down to a depth of 200 mm at the reference site (Fig. 3). The method is well described in Hancock et al. (2019). The process assumes that the 137Cs diffusion and migration behaviour at the reference site is representative of that occurring at each transect sample point. Values for D and V were found to be 68.4 kg2 m−4 yr−1 and 1.2 kg m−2 yr−1 respectively (optimised for the time of sample collection in 2017). A relaxation depth, H, that describes the soil depth to which fallout penetrates the soil is also required and we use the value of 4.5 kg m−2 for undisturbed soils as suggested by others (He and Walling; 1997; Walling et al., 2011). A sensitivity test of this value found that the DMM was relatively insensitive to the magnitude of H (i.e. varying H from 3 to 6 kg m−2 changed the overall slope erosion rate by 0.01 mm yr−1 in this study). The DMM was used to calculate the erosion/deposition profile along the transect. The DMM calculates deposition (a positive value) and erosion (a negative value) or for each point on the hillslope based on the reference inventory concentration (Walling and He, 2001). This is then distance weighted according to the hillslope length and distance between points to produce a cumulative erosion or deposition value for each point on the hillslope, based on the individual point concentration.

1

transect 1 200 0.8

0.6

190 185

0.4

soil depth (m)

elevation (m)

195

180 elevation 175

0.2

soil depth

170

0 0

20

40

60

80

100

120

distance (m) 205

1

transect 2 200 0.8

0.6

190 185

0.4

soil depth (m)

elevation (m)

195

180 0.2 175 0

170 0

10

20

30

40

50

60

70

80

distance (m) 1

205

transect 3

200

0.8 195 elevation (m)

The three hillslopes had linear profiles with average slope between 20 and 22% (Fig. 4, Table 1). At the time of each sampling the site had a good and dense grass cover of 100% using a quadrat and visual assessment (Singh, 1996) (Fig. 2) with the exception being within the tree stand (Fig. 5) where ground cover was less than 10% within and 20–40% downslope. Cattle were always present (but were lightly stocked) (less than 1 animal per hectare) and for some visits the pasture had been mowed (slashed) to manage weeds. There was no visible evidence of erosion from rilling or gullying. Transect 3 had a good grass cover both upslope and downslope of the trees (Fig. 2). The transect ran linearly in the steepest downslope direction from pasture at the ridge line above the trees, through the trees and exiting to pasture downslope of the trees. The tree plot (or distance through the trees) was approximately 20 m long. However, within the trees there was a noticeable loss of grass cover, with the majority of the ground surface being bare (Fig. 5). There was no visible evidence of erosion within the tree plot or areas of deposition downslope of the trees. No transect had a soil depth that complied with the soil catena concept (i.e. shallow at the divide and increasing downslope). Soil depth varied down the hillslope and was unique for all three transects with no pattern evident (Table 3, Fig. 4). Transect 1 had the maximum average soil depth (0.70 m) with Transect 2 having the lowest average (0.38 m). Transects 1 and 3 had the shallowest soil depth mid slope with no strong trends. Transect 2 had the deepest soil midslope with no strong hillslope trends.

0.6

190 185

0.4

soil depth (m)

5. Results – hillslope and soil properties

180 0.2 175 170 0

20

40

60

80

100

0 120

distance (m)

Fig. 4. Hillslope profile and soil depth for Transects 1 (top), 2 (middle) and 3 (bottom).

pH range was 4.5 to 6.1 for Transect 1, 5.35 to 5.65 for Transect 2 and 5.15 to 5.65 for Transect 3 (‘very strongly acid’ to ‘slightly acid’ according to Hazelton and Murphy (2007)). EC was less than 170 uS for all samples. There were no trends or relationships with pH and EC and other soil properties for any of the transects. Bulk density, determined from the soil cores (Hazelton and Murphy, 2007) was similar for all transects (1.33 t/m3). Soil organic carbon has a well-recognised influence on soil erosion and soil hydrology as well as being an indicator of soil health (Murphy, 2015). No tests for structural stability were conducted however, here SOC was on average approximately 2% which is classed as high and the soil having good structural condition and stability (Table 4) (Hazleton 5

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Fig. 5. Low grass cover and bare earth inside the eucalypt regrowth area (top). The bottom photograph displays the edge of the tree regrowth area looking downslope and the reduced grass cover both with and surrounds. Table 3 Soil depth characteristics (m) for the Mulconda Park site.

Transect 1 Transect 2 Transect 3

average

min

max

Std. Dev.

0.70 0.38 0.63

0.27 0.25 0.38

0.9 0.75 0.7

0.15 0.14 0.09

Table 4 Soil organic carbon (%) for the Mulconda Park site.

Transect 1 Transect 2 Transect 3

average

min

max

Std. Dev.

2.16 2.25 1.85

1.08 1.37 1.33

3.85 4.08 3.04

0.74 0.69 0.38

Fig. 6. Mulconda Park soil organic carbon and hillslope profile. The vertical dotted line for Transect 3 represents the position of the tree plot. Table 5 Soil Nitrogen (%) for the Mulconda Park site.

and Murphy, 2007). However, there was some variability in SOC with some samples being in the ‘moderate’ category (1.0–1.8%) as well as ‘very high’ SOC (> 3%) (Fig. 6, Table 4). Interestingly there was no relationship with SOC and 137Cs concentration, suggesting at this site that erosion (as quantified by 137Cs) was independent of SOC (Kuhn et al., 2009; Martinez et al., 2009). Nitrogen is a vital soil component. Here total soil N was classed as low (0.05–0.15% by weight) to medium (0.15–0.25% by weight) (Table 5) (Hazelton and Murphy, 2007). There was no significant

Transect 1 Transect 2 Transect 3

6

average

min

max

Std. Dev.

0.13 0.12 0.13

0.07 0.09 0.07

0.18 0.19 0.25

0.033 0.024 0.045

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Table 6 Soil C:N for the Mulconda Park site.

Transect 1 Transect 2 Transect 3

average

min

max

Std. Dev.

17.2 15.9 17.7

14.0 13.0 13.4

23.1 19.6 24.1

2.5 1.82 2.67

Fig. 7. Mulconda Park soil Nitrogen (%) and Carbon:Nitrogen ration (C:N) for Transects 1,2 and 3. The vertical dotted line for Transect 3 represents the position of the tree plot.

difference in soil N for the three transects (3-way Anova). In terms of spatial patterns, all transects displayed a general decrease in soil N from the top of the slope with a minimum N concentration occurring at approximately 40 m for Transects 1 and 3 while a minimum occurred at approximately 20 m for Transect 2 (Fig. 7). From the minimum value, Transect 1 displayed a general increase downslope, Transect 2 remained approximately constant while soil N for Transect 3 increased and then declined at the foot of the slope. Overall there we no significant or consistent trends. Further, there did not appear to be any obvious influence of the tree plot in Transect 3.

Fig. 8. Soil textural properties for Transects 1, 2 and 3 at Mulconda Park. The vertical dotted line for Transect 3 represents the position of the tree plot.

7

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The soil carbon to nitrogen ratio (C:N) indicates the relative organic matter breakdown status of a soil. The C:N ratios here were all less than 25 which indicate that decomposition can proceed at the maximum rate possible (Table 6) (Hazelton and Murphy, 2007). There was no significant difference in C:N ratio (3-way Anova) for any of transects nor was there any consistent trend or patterns. Again, the tree plot in Transect 3 appeared to have no influence. Based on soil texture the transects were loamy sand to silty loam. While the relative range of values is not strong, all three transects show an increase in silt and clay moving downslope (and corresponding decrease in sand) (Fig. 8). There was no significant relationship with silt, clay or sand with SOC, 137Cs or any hillslope variables such as elevation, slope, upslope contributing area (t-test and Anova) derived from the digital elevation model described in Section 3.5. There was no relationship with the Topographic Wetness Index (TWI) (Beven and Kirkby, 1979). The TWI) is:

Ai ⎞ λi = ln ⎛ ⎝ tan Si ⎠ ⎜

⎟

(1)

where Ai is upslope catchment area draining through a point and S is slope at that point (derived from the DEM) (Wilson and Gallant, 2000). 6. Results –

137

Cs analysis

Analysis of the 25 soil cores taken at the control site produced an average reference 137Cs inventory of 612 Bq m−2 with a standard deviation of 93.4 Bq m−2 (Table 2). It is well recognised that there is spatial variability and uncertainty present in soil 137Cs and using a reference range is better practice than a single reference value (Owens and Walling, 1996). Here we use the mean 137Cs concentration with 95% confidence limits about the mean (Owens and Walling, 1996). Sutherland (1996) suggested that sufficient samples should be taken to produce a 90% confidence interval of less than 10% of the mean. Here we found a 95% confidence interval about the mean of +/− 60.2 Bq m−2 (which is 9.8% of the mean value). Therefore, the 137Cs reference inventory range was 552.0 to 672.4 Bq m−2 (Table 2). A greater level of uncertainty was also employed ( ± 20% of the mean) (Hancock et al., 2019). This produced an inventory range of 489.8 to 734.7 Bq m−2. It is assumed that each transect core 137Cs inventories were subject to the same level of uncertainty (i.e. ± 9.8% or ± 20%). As a result, sites with 137Cs levels lower than 489.8 Bq m−2 were classified as undergoing erosion, while those with levels > 734.7 Bq m−2 showed evidence of deposition. At locations where 137Cs levels fell within the confidence interval it was deemed that there was insufficient evidence of either erosion or deposition taking place. Consequently, at these locations soil redistribution rates were assigned a value of zero (Sutherland, 1996; Pennock and Appleby, 2002; Martinez et al. 2009; Hancock et al., 2019). The pattern of 137Cs deficiency and excess along the transect is illustrated in Fig. 9; with all three transects having unique patterns. Transect 1 has a general decreasing 137Cs concentration moving downslope. However, it is largely transitional for the top half, erosional for the bottom half, and becomes transitional again at the base of the slope. A high concentration is found near the top of this hillslope; indicating deposition. Field observation found that there was considerable deposition as a result of a decayed fallen tree and a build-up of sediment upslope of this structure (i.e. a sediment dam). Transect 2 has no strong trend and is largely transitional for its length except for several erosional points. Transect 3 is largely erosional for its length with a slight increasing trend in 137Cs moving down the hillslope. For all transects there is little visual evidence of sediment storage and any material that reaches the foot of the slope is able to move into the zeroorder channel (Fig. 2) at the base of the hillslope and exported. To account for possible spatial variations in 137Cs inventory values DMM runs were performed with reference and transect core inventory

Fig. 9. 137Cs concentration for Transect 1 (top), 2 (middle) and 3 (bottom) with the 137Cs reference range of +/- 20% (dotted line). The vertical lines for Transect 3 (bottom figure) indicate the position of the tree plot.

values spanning the expected variation observed as a result of smallscale spatial variation, (i.e. the average value ± 2σ and ± 20%). Using a 137Cs inventory of ± 2σ, all transects produced erosion with Transects 1 (0.8–2.6 t/ha/yr) and 2 (0.7–2.5 t/ha/yr) having very similar erosion rates while Transect 3 had the highest erosion (3.3–4.8 t/ha/yr) (Table 7). Using a 137Cs inventory of ± 20%, both Transect 1 and 2 were similar with deposition (0.5 t/ha/yr -Transect 1 and 0.6 t/ha/yr – Transect 2) and erosion (3.3 t/ha/yr -Transect 1 and 3.0 t/ha/yr – 8

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the hillslope could not be considered excessive. This is further discussed in Section 7.4. To independently verify erosion rates at this site requires field plots, erosion pins and or stream sediment collection (Blanco and Lal, 2008; Morgan and Nearing, 2011). This is beyond the scope of this project and is not practical at this site due to it being a commercial property. There is a real need for a renewed effort for field measurement of erosion rates and patterns and to independently validate findings such as that here as well as develop new methods with new environmental tracers (i.e. 210 Pb, 7Be) (Walling and He, 2001; Zapata et al. 2002). However, the results (and sensitivity analysis conducted here) show the erosion rate for Transect 3 is approximately twice that of Transects 1 and 2 and that this higher erosion rate is independent of the presence of the trees.

Table 7 Erosion rates for Transects 1, 2 and 3. Negative values represent erosion while positive value represent deposition. Units are t/ha/yr. erosion/ deposition Transect 1

Transect 2

Transect 3

Low (-2 S.D) Upper (+2 S.D) Low (-2 S.D) Upper (+2 S.D) Low (-2 S.D) Upper (+2 S.D)

−0.8 −2.6 −0.7 −2.5 −3.3 −4.8

erosion/ deposition Low (-20%) Upper (20%) Low (-20%) Upper (20%) Low (-20%) Upper (20%)

+0.5 −3.3 +0.6 −3.0 −2.2 −5.5

Transect 2). Transect 3 was erosional with a range of 2.2 to 5.5 t/ha/yr. In summary, using both ± 2σ and ± 20% as the reference inventory produced deposition (0.6 t/ha/yr) to a maximum erosion value of 5.5 t/ ha/yr. The DMM model, using an average of all data, produced an overall rate of erosion for all three transects of 2.3 t/ha/yr (0.173 mm loss per year using a bulk density of 1.33 t/m3) using the two inventories.

7.2. Effect of trees and hillslope variability Many others have examined the role of tree plots and shelterbelts (Walter et al., 2003; Follain et al., 2007; Sauer et al., 2007; Cunningham et al., 2012; Hoogmoed et al., 2012; Davey and Koen, 2013; Lenka et al., 2012; Baah-Acheamfour et al., 2014; 2015; Korkanc, 2014; Cardinael et al., 2015). Here we find that there is little physical difference between the pasture and tree transects. It could be that more time is needed for any strong differences to occur and that ~ 20 years is insufficient. However, there was no evidence to suggest that erosion was higher or less within the tree area both from the 137Cs data or visually. This is an interesting finding as the trees, given the lack of groundcover (Fig. 5) could be construed to have the potential to increase erosion. The likely reason for the erosional stability is that the eucalypt roots reinforce the soil, increasing soil shear strength. Soil moisture is also likely to be lower, therefore the soil has higher soil water storage capacity at any given time compared to pasture. The tree canopy may also intercept rainfall reducing drop impact and increasing protection from rainsplash (Morgan and Nearing, 2011; Blanco and Lal, 2008; Willgoose, 2018). Due to this, other reasons need to be explored for the higher erosion rates found in Transect 3. The transect is adjacent to Transects 1 and 2 and is visually similar and has the same soils and subject to the same management. There is no visible evidence to suggest disturbance. It could be that this section of hillslope has been managed differently by the previous owners, however there is no record or field evidence to suggest this. Increased cattle traffic under past management practices may have enhanced erosion. However, this is speculation only and the evidence points to natural variability in erosion and deposition. It is well recognised that soil erosion is highly variable (Walling and He, 2001; Loughran 1994, Loughran et al., 2002; Zapata et al., 2002) and the relatively higher erosion rate for Transect 3 could be representative of the soil erosion range for the area and landscape management. However, Martinez et al. (2009) and Hancock et al. (2014) did not find this variability at a grazing site managed similarly (albeit with lower rainfall and different soil). Given the spontaneous regrowth of the plot, there must have been a viable seed stock or tubers present. This suggests that trees must have been present at the site previously. Therefore, the transect may have been cleared several times and any other regrowth areas controlled by slashing (mowing). This clearing activity may have resulted in an increase in soil loss. Further analysis is needed to fully assess this variability. An obvious issue with the tree plot is the loss of pasture both within and around it (Fig. 5). This will reduce the amount of fodder available for cattle and requires further assessment of the economic impacts of pasture loss versus the advantages of shade for cattle, enhanced biological diversity and the potential increase is pasture productivity due to the plot acting as a wind break.

7. Discussion An advantage of this site is its uniform climate, land management and soils. The site is also broadly representative of the general area and of the east coast of Australia where sedimentary geology dominates. The property, like many others in the area has been well-managed and grazed conservatively and we believe it to be at a stable or equilibrium state. The tree plot is approximately 20 years old and has regenerated spontaneously, similar to many on the property and surrounding landscape. During this time the site and area has been subjected to a record drought and a major storm event (Verdon-Kidd et al., 2014). Therefore, we believe the findings here are transferable to other sites. 7.1. Erosion rates An interesting finding here is that the three hillslope transects, while being located on the same soil with the same aspect, climate and land management have different 137Cs hillslope profiles. While the erosion rates for the transects are not exceptionally different, the results demonstrate that to understand erosion and deposition multiple hillslope transects are very useful. There did not appear to be any patterns with soil depth for any of the transects. However, there appeared to be less variability in soil depth for Transect 3 (Fig. 4, Table 3). There was also no relationship with topography. Here we have only measured soil depth along the transects and it is possible that relationships may be revealed if soil depth was measured at greater detail (i.e. on a regular grid) so that subsurface topographic patterns can be determined. Such measurement is currently beyond the scope of this project. It has been speculated that there is a relationship between SOC and soil erosion and deposition (Kuhn et al., 2009; Martinez et al., 2009) while many have used 137Cs as a surrogate for erosion and deposition (Mabit and Bernard, 1998; Mabit et al., 2008). At this site we find no relationship between SOC and 137Cs, suggesting that erosion and soil carbon were independent at this site. Here we employed two different reference ranges for 137Cs. Erosion rates using ± 2σ as the reference range for the three transects were between 0.8 and 4.8 t/ha/yr (Table 7). Using a reference range of ± 20% Transects 1 and 2 were depositional with values of 0.5 and 0.6 t/ha/yr respectively. The erosion rates for Transects 1 and 2 are not too dissimilar while Transect 3 is slightly higher. Using an average of both ± 2σ and ± 20% reference ranges produces erosion rates of 2.45 and 2.15 t/ha/yr for the entire hillslope. Therefore, the erosion rates for 9

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collecting cores on the ridge (top of hillslope profile) there was standing water visible with the core holes immediately filling upon extraction. Therefore at this site there is sufficient water and its movement to assist in weathering. Soil pH is also quite low for all three transects. Low pH is a recognised issue in the local area and lime is usually added to ameliorate. How the combination of high rainfall, strong soil water movements as well as low pH interact in the process of soil formation is an area of further study (Willgoose, 2018). While at decadal time scales (and from an agricultural management perspective) the soil loss is not immediately critical, it should be noted that it is the near surface soil fraction which contains the highest concentrations of organic carbon and nutrients that are likely currently being lost from the hillslope system. In terms of its geomorphology, geology, soils, land use and vegetation, this study hillslope is broadly representative of others in the area and thus the rates of erosion reported here are likely to be representative of other hillslopes in the area.

7.3. Sustainable land use Soil erosion rates should be judged by comparing the rate of soil loss against the rate at which soil is being formed by the weathering of the underlying bedrock (i.e. soil production) (Verheijen et al., 2009; Bui et al., 2011; Stockmann et al., 2014). This is the concept of tolerable soil loss (i.e. the RUSLE T-value) (Bui et al., 2011; www.iwr.msu.edu/ rusle). Determining whether an erosion rate for a particular site is tolerable is difficult as there is rarely site specific soil production, soil erosion or soil depth data. At best, one can only compare the site data with available surrounding data. At Mulconda Park, erosion rates for the study site are higher than those found for nearby sites and those in the general area. An assessment of erosion rates in forested catchments to the north of the study site found erosion rates of between 0.47 and 1.40 t/ha/yr (0.033 to 0.098 mm/yr). However these were for sites with steep slopes and dense forest of which several are largely undisturbed by Europeans (Hancock et al., 2017b). Nevertheless they provide what we believe to be the only baseline data for undisturbed sites on the east coast of Australia. A catchment assessment in a lower rainfall (~550 mm/yr) area approximately 120 km west of the study site using an analysis of 137Cs distribution and a variety of modelling approaches found erosion rates in the range 0.1 to 0.06 t ha−1yr−1 (Martinez et al., 2009; Hancock and Coulthard., 2012; 2014; 2019). Krause et al (2003) found erosion rates of 0.04 to 0.1 t ha−1 yr−1 for grazing hillslopes close to the study site (with similar rainfall, management and soils) while Elliott et al. (1997) determined erosion rates of 0.19–1.51 t ha−1 yr−1 for grazing sites in the lower Hunter Valley. After using a range of sites and measurement techniques Saynor (1993) reported a value of 0.2 t ha−1 yr−1 as the critical level of net soil loss for South East Australian grazed hillslopes. Smith and Stamey (1965) conclude that rates of erosion for vegetated sites similar to the current study site in the United States lie in the range 0.1 – 0.7 t ha−1 yr−1. Soil production data is scarce for the east coast of Australia (Fifield et al., 2010; Williams, 1978; Pillans, 1997; Heimsath et al., 2000; 2001; 2006; Wilkinson et al., 2005; Stockmann et al., 2011). Wells et al. (2008) reported weathering rates of 0.005 to 0.025 mm/yr equating to soil production rates of 0.007 to 0.035 mm yr−1 for sandstones in the Hunter Valley. Stockmann et al. (2011) found soil production rates of 0.003–0.018 mm yr−1 for a site with similar climate and soils to Mulconda Park. However, the site was in a national park (Werrikimbe) and had not been cleared of timber. Other soil production data for Australian east coast sites (Heimsath et al., 2000, 2001, 2006; Wilkinson et al., 2005) suggest soil production rates in the range of 0.012 to 0.062 mm yr−1. Considerably lower soil production rates (0.001 to 0.007 mm yr−1) have been reported in a southern New South Wales catchment (albeit with a slightly colder climate, i.e. Fifield et al., 2010). At Mulconda Park, denudation rates determined from the 137Cs distribution suggests an average rate of 0.173 mm yr−1. This is at least an order of magnitude greater than any of the soil production rates listed above and exceeds the natural ‘geological’ rate of erosion (0.05 t ha−1 yr−1) reported by Edwards (1987). Given the soil depth results (Fig. 4, Table 3), the loss of soil is agriculturally tolerable at decadal time scales for grassland productivity. However, it is clear that soil is being lost at a faster rate than it is being produced. Stockmann et al. (2014) found that when examining all global soil production data that deeper soils have a reduced soil production rate. That is, deeper soils protect bedrock from weathering. The soils at Mulconda Park are not exceptionally deep soils (Fig. 4, Table 3) so there is the potential that bedrock is weathering at a fast rate and soil is being produced at a faster rate. We are currently seeking resources to examine the issue of soil production rates at this site and surrounds. It should be noted that on two sampling occasions when collecting core samples at the site that the soil was saturated. Upon extraction of the cores (when collecting samples both during and after heavy rainfall), the sample hole began to infill with water. On one occasion when

7.4. Study limitations Here we use the 137Cs method to determine erosion rates. The method relies on a series of assumptions and models that while well understood and recognised, are impossible to verify. We have undertaken a sensitivity analysis of the control concentrations as well as other parameters such as D, V and ho. We find our results to be robust, however, we recognise that there is no independent validation. An alternative is to use numerical models such as the USLE or RUSLE (Wischmeier and Smith, 1978), or a landscape evolution model (Hancock et al., 2019). However, using a model brings its own set of uncertainties (Tucker and Hancock, 2010; Willgoose, 2018). The 137Cs method may be complex and questions asked as to its appropriateness and reliability. However, we ask the question, how would hillslope soil erosion, particularly at this site which is actively grazed and there are differences in vegetation be assessed? While it may be argued that the complexity of the 137Cs conversion model (the DMM employed here) has many uncertainties (and here we have done our best to take these into account) what cannot be argued is the finding that the two pasture transects have approximately the same amounts of 137 Cs while the tree regrowth transect has considerably less. The relative concentrations at the very minimum point to a greater loss of soil for the tree plot transect. While broadly representative of the local area, it should be noted that the erosion rates here and analysis of the tree plot erosion is for one site only. The transferability of these findings to other sites is likely, but verification for other sites is needed. Of particular importance is the assessment of the impact of trees on not just soil erosion but soil properties as well at other sites. The tree plot is representative of common species at the site (and surrounds) and regrowth patterns. While large enough to notice differences in groundcover, it may be that the tree plot is not large enough to effect any significant changes. This may require a larger area of trees or a longer time period (Walter et al., 2003; Follain et al., 2007; Sauer et al., 2007; Lenka et al., 2012; Korkanc, 2014; Cardinael et al., 2015). It is planned to resample the site in five years’ time when there is potential to assess longer term changes. 8. Conclusion The results demonstrate that the site has a relatively low erosion rate that is likely to be higher than the soil production rate. While the erosion rate for the transect containing a plot of tree regrowth was higher than the adjacent pasture, at this site there is no reason why the trees would have increased the erosion rate. Therefore, at this site, it does not appear that a plot of trees increases erosion. While only a small data set (and work is ongoing), the findings here are of global interest as there is a desire for biological diversity in many 10

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agricultural systems and one way to increase this diversity is to either plant new vegetation (such as trees) or allow endemic species to regrow. Therefore at this site, other than a reduction in grass and groundcover, trees appear to have little influence on soil erosion or soil properties. Further analysis is needed to assess longer term trends.

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Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgments This research was supported by Australian Research Council Discovery Grants (DP 0556941: “Carbon, nutrient and sediment dynamics in a semi-arid catchment” and DP110101216: “A next generation spatially distributed model for soil profile dynamics and pedogenesis incorporating soil geochemistry and organic matter”). The owner of Mulconda Park, Peter Broderick is thanked for his support. The discussions in the field are fondly remembered. We hope your new adventures go well! Field work support from David Bretreger, Elise Powning, Sean Brennan, James Hugo and Matt O’Connor is appreciated. The authors declare no conflict of interest. The reviewers are thanked for their helpful and supportive comments. References Baah-Acheamfour, M., Carlyle, C.N., Bork, E.W., Chang, S.X., 2014. Trees increase soil carbon and its stability in three agroforestry systems in central Alberta, Canada. For. Ecol. Manage. 328, 131–139. Baah-Acheamfour, M., Chang, S.X., Carlyle, C.N., Bork, E.W., 2015. Carbon pool size and stability are affected by trees and grassland cover types within agroforestry systems of Western Canada. Agric. Ecosyst. Environ. 213, 105–113. Beven, K.J., Kirkby, M.J., 1979. A physically based contributing area model of basin hydrology. Hydrol. Sci. Bull. 24, 43–69. Blanco, H., Lal, R., 2008. 2008. Principles of soil conservation and management, Springer, Heidelberg, Germany. Boardman, J., Favis-Mortlock, D. 2016. The use of erosion pins in geomorphology. Chapter 3 in, Cook, S.J., Clarke, L.E. and Nield, J.M. (eds.) Geomorphological Techniques. British Society for Geomorphology, London, UK. Bui, E.N., Hancock, G.J., Wilkinson, S.N., 2011. Tolerable hillslope soil erosion rates in Australia: Linking science and policy. Agric. Ecosyst. Environ. 144, 136–149. Campbell, B.L., Loughran, R.J., Elliott, G.L., 1988. A method for determining sediment budgets using caesium-137, Sediment Budgets, Porto Alegre Symposium (December 1988). Int. Assoc. Hydrol. Sci. (IAHS) Publ. 174, 171–179. Cardinael, R., Chevallier, T., Barthes, B.G., Saby, N.P.A., Parent, T., Dupraz, C., Bernoux, M., Chenu, C., 2015. Impact of alley cropping agroforestry on stocks, forms and spatial distribution of soil organic carbon – A case study in a Mediterranean context. Geoderma 259–260, 288–299. Cornish, P.M., 1993. Effects of logging and forest regeneration on water yields in a moist eucalypt forest in New South Wales, Australia. J. Hydrol. 150 (2–4), 301–322. Cunningham, S.C., Metzeling, K.J., Mac Nally, R., Thomson, J.R., Cavagnaro, T.R., 2012. Changes in soil carbon of pastures after afforestation with mixed species: Sampling, heterogeneity and surrogates. Agric. Ecosyst. Environ. 158, 58–65. Davey, M.C., Koen, T.B., 2013. Variations in soil organic carbon for two soil types and six land uses in the Murray Catchment, New South Wales, Australia. Soil Res. 51, 631–644. Edwards, K., 1987. Runoff and Soil Loss Studies In New South Wales: A National Soil Conservation Program Project. Technical Handbook No. 10, 38 pp. Elliott, G.L., Loughran, R.J., Packer, I., Maliszewski, L.T., Curtis, S.J., Saynor, M.J., Morris, C.D., Epis, R.B. 1997. A National Reconnaissance Survey of Soil Erosion in Australia: New South Wales, A report prepared for the Australian National Landcare Program, Department of Primary Industries and Energy. Project Number 1989-90: No. 8, The University of Newcastle, N.S.W., Australia. Fifield, L.K., Wasson, R.J., Pillans, B., Stone, J.O.H., 2010. The longevity of hillslope soil in SE and NW Australia. Catena 81, 32–42. https://doi.org/10.1016/j.catena.2010. 01.003. Follain, S., Walter, C., Legout, A., Lemercier, B., Dutin, G., 2007. Induced effects of hedgerow networks on soil organic carbon storage within an agricultural landscape. Geoderma 142, 80–95. George, S.J., Harper, R.J., Hobbs, R.J., Tibbett, M., 2012. A sustainable agricultural landscape for Australia: A review of interlacing carbon sequestration, biodiversity and salinity management in agroforestry systems. Agric. Ecosyst. Environ. 149, 154–163. Hancock, G.R., Coulthard, T.J., 2012. Channel movement and erosion response to rainfall variability in southeast Australia. Hydrol. Process. 26, 6633–6673. https://doi.org/

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