how fast does soil grow?

14
How fast does soil grow? Uta Stockmann , Budiman Minasny, Alex B. McBratney Faculty of Agriculture and Environment, Department of Environmental Sciences, Biomedical Building C81, The University of Sydney, 2006 New South Wales, Australia abstract article info Article history: Received 5 May 2013 Received in revised form 27 September 2013 Accepted 6 October 2013 Available online 5 November 2013 Keywords: Pedogenesis Soil weathering Soil production rate (SPR) Terrestrial cosmogenic nuclides (TCN) Critical zone Quantifying the rate of soil formation has become important in response to the consideration of soil as a renewable resource. The availability of new sophisticated laboratory techniques has opened up the possibility of addressing the demand of quantifying processes of soil landscape evolution in the critical zone. Here, we investigated the rate of soil formation of world soils based on published results of TCN-derived (terrestrial cosmogenic nuclides, 10 Be) soil production rates (SPR). The compilation of published TCN-derived SPR for different climatic zones and lithologic conditions showed exponentially decreasing SPR with increasing soil thickness for the majority of the discussed studies. This implies that the presence of a soil mantle protects the bedrock from further weathering. We found that rates of soil production in Australia appear to be similar in range when compared with other parts of the world. We concluded that we can formulate an average quantitative estimate of globalsoil production based on TCN: soil production rate (mm kyr -1 ) = 114 ± 11 exp (-2.05 soil thickness in mm). Such a rate is useful for global modelling of soil formation to better understand the role of soils in landscape evolution. © 2013 Elsevier B.V. All rights reserved. 1. Introduction The quantication of soil genesis is still an understudied topic, despite the fundamental importance of soils for the functioning of ecosystems in the critical zone (Brantley et al., 2007). The term critical zone has become known recently as the interface where rocks meet life and soil-forming processes take place that transform bedrock and biomass into soil, the Earth's living skin(Anderson et al., 2007). It has been discussed widely that soil is degrading faster than it can be replenished (Montgomery, 2007; Pimentel et al., 1995; Quinton et al., 2010) and one may argue that we are not far from reaching peak soil, a state where the world soils are not sufcient to sustain their fertility, either through the loss of topsoil or depletion of nutrients. This is particularly of concern for the agricultural sector, where a loss of the soils fertile capacity has major effects on (crop) productivity. Therefore, to better understand the complexity of soil systems we need to explore processes that lead to its formation and to answer questions such as how fast or slow does soil form?or at what rate does soil form over time?(Hoosbeek and Bryant, 1992; Minasny et al., 2008; Stockmann et al., 2011). Over the years, soil scientists have formalised concepts and models of soil formation to improve our knowledge of pedogenesis. Early models were limited to a description of soil evolution in the landscape or were based on simple empirical relationships (McBratney et al., 2003). However, recently there has been a shift of interest towards mechanistic modelling of soil formation (e.g. Brantley et al., 2008; Finke and Hutson, 2008; Minasny et al., 2008; Salvador-Blanes et al., 2007; Sommer et al., 2008; Vanwalleghem et al., 2013). These mechanistic models implement soil-forming processes to describe pedogenesis quantitatively which requires a detailed understanding of these. In recent years sophisticated laboratory techniques have become available that can be used to quantify processes of soil formation in- situ from eld data to ultimately parameterise and verify models of pedogenesis. However, different measurement techniques have different assumptions, and there could therefore be a problem when comparing weathering rates obtained from different techniques. Here, we are dealing specically with in-situ terrestrial cosmogenic nuclides (TCN) that have been applied to determine the rates of soil production from soil parent materials. TCN are produced through the interaction of secondary cosmic rays with Earth materials. Their production in atoms g-mineral -1 year -1 occurs in the uppermost metres of the Earth's surface and decreases exponentially with depth (Bierman and Nichols, 2004). Subsequently, concentrations of TCN reect the near surface residence time of Earth materials and high concentrations of TCN in Earth materials imply longer exposure to cosmic rays. Lal (1991) introduced the prospect of applying in-situ concentrations of TCN for determining the age of landscapes and for quantifying various geomorphic processes. Heimsath et al. (1997) used the concepts discussed in Lal (1991) to establish the use of TCN in pedological studies. They applied TCN to derive in-situ soil production rates (SPR) from eld data and were able to verify the conceptual framework of exponential decrease of soil formation with increasing soil thickness (Ahnert, Geoderma 216 (2014) 4861 Corresponding author at: Faculty of Agriculture and Environment, Biomedical Building C81, Suite 401, 1 Central Avenue, Eveleigh, 2016, NSW, Australia. Tel.: +61 2 86271147. E-mail address: [email protected] (U. Stockmann). 0016-7061/$ see front matter © 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.geoderma.2013.10.007 Contents lists available at ScienceDirect Geoderma journal homepage: www.elsevier.com/locate/geoderma

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Geoderma 216 (2014) 48–61

Contents lists available at ScienceDirect

Geoderma

j ourna l homepage: www.e lsev ie r .com/ locate /geoderma

How fast does soil grow?

Uta Stockmann ⁎, Budiman Minasny, Alex B. McBratneyFaculty of Agriculture and Environment, Department of Environmental Sciences, Biomedical Building C81, The University of Sydney, 2006 New South Wales, Australia

⁎ Corresponding author at: Faculty of AgricultureBuilding C81, Suite 401, 1 Central Avenue, Eveleigh, 20186271147.

E-mail address: [email protected] (U. Sto

0016-7061/$ – see front matter © 2013 Elsevier B.V. All rihttp://dx.doi.org/10.1016/j.geoderma.2013.10.007

a b s t r a c t

a r t i c l e i n f o

Article history:Received 5 May 2013Received in revised form 27 September 2013Accepted 6 October 2013Available online 5 November 2013

Keywords:PedogenesisSoil weatheringSoil production rate (SPR)Terrestrial cosmogenic nuclides (TCN)Critical zone

Quantifying the rate of soil formation has become important in response to the consideration of soil as arenewable resource. The availability of new sophisticated laboratory techniques has opened up the possibilityof addressing the demand of quantifying processes of soil landscape evolution in the critical zone. Here, weinvestigated the rate of soil formation of world soils based on published results of TCN-derived (terrestrialcosmogenic nuclides, 10Be) soil production rates (SPR). The compilation of published TCN-derived SPR fordifferent climatic zones and lithologic conditions showed exponentially decreasing SPR with increasing soilthickness for the majority of the discussed studies. This implies that the presence of a soil mantle protects thebedrock from further weathering. We found that rates of soil production in Australia appear to be similar inrange when compared with other parts of the world. We concluded that we can formulate an averagequantitative estimate of ‘global’ soil production based on TCN: soil production rate (mm kyr−1) = 114 ± 11exp (−2.05 soil thickness inmm). Such a rate is useful for globalmodelling of soil formation to better understandthe role of soils in landscape evolution.

© 2013 Elsevier B.V. All rights reserved.

1. Introduction

The quantification of soil genesis is still an understudied topic,despite the fundamental importance of soils for the functioning ofecosystems in the critical zone (Brantley et al., 2007). The term criticalzone has become known recently as the interface where rocks meetlife and soil-forming processes take place that transform bedrock andbiomass into soil, the Earth's ‘living skin’ (Anderson et al., 2007). It hasbeen discussed widely that soil is degrading faster than it can bereplenished (Montgomery, 2007; Pimentel et al., 1995; Quinton et al.,2010) and one may argue that we are not far from reaching peak soil,a state where the world soils are not sufficient to sustain their ‘fertility’,either through the loss of topsoil or depletion of nutrients. This isparticularly of concern for the agricultural sector, where a loss of thesoils fertile capacity has major effects on (crop) productivity. Therefore,to better understand the complexity of soil systems we need to exploreprocesses that lead to its formation and to answer questions such as‘how fast or slow does soil form?’ or ‘at what rate does soil form overtime?’ (Hoosbeek and Bryant, 1992; Minasny et al., 2008; Stockmannet al., 2011).

Over the years, soil scientists have formalised concepts and modelsof soil formation to improve our knowledge of pedogenesis. Earlymodels were limited to a description of soil evolution in the landscapeor were based on simple empirical relationships (McBratney et al.,

and Environment, Biomedical6, NSW, Australia. Tel.: +61 2

ckmann).

ghts reserved.

2003). However, recently there has been a shift of interest towardsmechanistic modelling of soil formation (e.g. Brantley et al., 2008;Finke and Hutson, 2008; Minasny et al., 2008; Salvador-Blanes et al.,2007; Sommer et al., 2008; Vanwalleghem et al., 2013). Thesemechanistic models implement soil-forming processes to describepedogenesis quantitatively which requires a detailed understanding ofthese.

In recent years sophisticated laboratory techniques have becomeavailable that can be used to quantify processes of soil formation in-situ from field data to ultimately parameterise and verify models ofpedogenesis. However, differentmeasurement techniques have differentassumptions, and there could therefore be a problem when comparingweathering rates obtained from different techniques. Here, we aredealing specifically with in-situ terrestrial cosmogenic nuclides (TCN)that have been applied to determine the rates of soil production fromsoil parent materials. TCN are produced through the interaction ofsecondary cosmic rays with Earth materials. Their production in atomsg-mineral−1 year−1 occurs in the uppermost metres of the Earth'ssurface and decreases exponentially with depth (Bierman and Nichols,2004). Subsequently, concentrations of TCN reflect the near surfaceresidence time of Earth materials and high concentrations of TCN inEarth materials imply longer exposure to cosmic rays. Lal (1991)introduced the prospect of applying in-situ concentrations of TCNfor determining the age of landscapes and for quantifying variousgeomorphic processes. Heimsath et al. (1997) used the conceptsdiscussed in Lal (1991) to establish the use of TCN in pedological studies.They applied TCN to derive in-situ soil production rates (SPR) from fielddata and were able to verify the conceptual framework of exponentialdecrease of soil formation with increasing soil thickness (Ahnert,

49U. Stockmann et al. / Geoderma 216 (2014) 48–61

1977). Since then a number of authors have investigated the rate ofsoil production from various parent materials using TCN. Althoughconceptually relatively simple, determining soil production rates istechnically extremely complex (refer to Child et al. (2000) for detailedlaboratory procedures).

Here, we investigated the rate of soil formation of world soils basedon published data sets that specifically used TCN to analyse the in-siturate of soil production. Our aim was to explore and compile soilproduction rates in different geographical settings. We then used thisdataset to formalize an average estimate of global soil production andsoil residence times. Subsequently, we compared these TCN-derivedSPR with soil formation or weathering rates estimated using differentapproaches (e.g. mass-balance studies) to further explore commontheories on the variability of pedogenesis (e.g. as discussed in Popeet al., 1995).

In general, the range of soil production is believed to be dependent onlocal site characteristics including climate, parent material, vegetationand topography (Dokuchaev, 1899). It is assumed that soil productionvarieswith the strength and composition of the parentmaterial, the soil'stemperature and thewater penetration through the soil profile. Theorieson the extent ofweathering of parentmaterials in-situ and consequentlyon the rate of soil production aremainly based on observed geographicalvariations in climate and weathering characteristics. Existing climatictheories on the extent of weathering discuss the variability ofpedogenesis as a result of different rock disintegration and decaybecause of different climatic zones (Pope et al., 1995). Accordingly,the mechanical breakdown of rock may be greater in parts of theworld with extreme climatic conditions. Alternatively, chemicalweathering is assumed to be the predominant factor of soil formationin the wet and warm tropics, resulting in deeply weathered profiles.Ultimately, temperature and water availability seem to be the maindrivers for different rates of soil production in-situ.

2. Quantifying SPR using in-situ TCN (10Be) concentrations of soilparent materials— an overview

Beryllium-10 (10Be) has predominantly been used for quantifyinglandscape evolution in TCN research. This is due to the relatively wellknown production rate of 10Be in quartz minerals at sea level and highlatitude (N60°) (Gosse and Phillips, 2001) and its non-existence in rocksprior to its emergence into the cosmic ray field (von Blanckenburg, 2006).

To determine SPR from soil parent materials the following needs tobe known:

(1) the rate of 10Be production in Earth materials in atoms g−1 yr−1

(5.1±0.3 atoms g−1 yr−1 at sea level and high latitude (Stone,2000)),

(2) the half-life of 10Be (1.51 ± 0.06 × 106 years (Hofmann et al.,1987); 1.39±0.01×106years (Chmeleff et al., 2009)), and

(3) the concentration of 10Be in atoms g-quartz−1 from the sampledsoil parent materials.

Calculations of SPR inmmkyr−1 are based on amodel of Nishiizumiet al. (1991), assuming that the concentration of 10Be in the sample ofsoil parent material is controlled by concentration increase withexposure time and also by the erosion rate (ε) of the parent materialitself. Assuming constant erosion rates and cosmic ray intensities, thesteady-state 10Be concentration (N) in the sample of parent materialin atoms g-quartz−1 can be calculated as:

N ¼ P h; θð Þλþ ρparentε

Λ

� �� � ð1Þ

where P is the production rate of 10Be in the target mineral quartz atdepth h and slope θ in atoms gram-quartz−1 year−1, ρparent is themean density of the parent material in g cm−3, λ the decay constant

of 10Be with λ equalizing ln2/10Be half-life, and Λ is the mean length ofattenuation of cosmic rays with Λ equalizing 150 g cm−2. SolvingEq. (1) for ε, gives the equation for calculating soil production rates(SPR) in mm kyr−1 (Heimsath et al., 1997; Lal, 1991):

SPR ¼ Λρparent

P h; θð ÞN

−λ� �

ð2Þ

Thereby, the annual production rate of 10Be (P) needs to benormalized to production rates at the site sampled based on elevation,latitude and longitude (Stone, 2000), as well as corrected for the soiloverburden and shielding by slope (Dunne et al., 1999; Granger andMuzikar, 2001). Soil production rates are calculated with theassumption that soil erosion and soil production are balanced and soilthickness is in steady state (i.e. assuming equal rates of soil formationand soil erosion throughout the production of TCN). Here, they aredefined as the rate of conversion of bedrock to soil, predominantlyaffected by the mechanical disruption or physical weathering ofbedrock.

In the geomorphology literature, erosion rate (Lal, 1991), denudationrate (von Blanckenburg, 2006) and soil production rate (Heimsath et al.,1997) are all used to describe TCN-derived denudation rates (physicaland chemical processes combined). Heimsath et al. (1997) applied theconcept of deriving TCN-based erosion rates of rock (Lal, 1991) toinvestigate soil production. They derived erosion rates from soil parentmaterials, and subsequently called this calculation ‘the rate of soilproduction’. Theoretically, as discussed in von Blanckenburg (2006)only the term denudation rate is of correct use in this context, becauseunlike the term ‘erosion’ it defines the removal of mass through bothphysical and chemical processes. This definition is rectified with theexplanation that the calculation of TCN-derived denudation rates alsoinvolves the combined effort of physical and chemical weatheringprocesses. Therefore, the term erosion rate is used inaccurately, becauseit generally describes only the mechanical processes of denudation. Itcan also be confused with erosion of soil.

In pedology, both, soil weathering as well as the soil formationrate define the same process of the conversion of parent material (e. g.bedrock, saprolite) to soil, combining physical and chemical processes.However, in the soil production concept the physical breakdown ormechanical disruption of the parent material (consolidated bedrock) tosoil is assumed to be the dominant process responsible for the thickeningof the soil profile. Therefore, TCN-derived SPR primarily reflects thephysical conversion of the parent material to soil. In this paper, the termsoil production rate (Heimsath et al., 1997) is used exclusively for TCN-derived rates of soil formation.

3. Materials and methods

3.1. Data analysis

Published results of TCN-derived (10Be) soil production rates (n=226) from different environmental settings globally (Australia, Northand South America) were reviewed to first discuss conceptual modelsof soil production and their verification with field data (Fig. 1). Twoconcepts of soil production have been discussed in relation to TCN-derived SPR. One explains soil production using a depth-dependentexponential functionwhere the formation of soil declines exponentiallywith increasing soil thickness (Dietrich et al., 1995; Heimsath et al.,1997), described formally as:

SPR ¼ P0 exp −bhð Þ ð3Þ

where P0 ([L T−1], mm kyr−1) is the rate of weathering of bedrock ath=0, h ([L], cm) is the soil thickness and b ([L−1], cm−1) is a rateconstant, a length scale that characterizes the decline in soil productionwith increasing soil thickness. The second concept explains soil

Fig. 1. Location of field sites of published TCN-derived soil production rates.

50 U. Stockmann et al. / Geoderma 216 (2014) 48–61

production using a humped function, where the production of soilfrom parent materials is abundant underneath an incipient soilcover and less prominent on exposed parent materials or an alreadythick soil mantle (Carson and Kirkby, 1972; Gilbert, 1877). IndividualSPR were thus related to the overlying thickness of the soil (Fig. 2).Therefore, only studies that investigated how soil formed over timewere compiled, i.e. only SPR (mm kyr−1) derived from parentmaterials of soil mantled landscapes and surface bedrock samplesfor reference values at zero soil depth were considered, excludingoutcrop samples at different height above the soil surface (N0 cm).

Fig. 2. Compilation of published TCN-derived SPR (mm kyr−1). Data from Australia ar

This is different from a previous study of Portenga and Bierman(2011) who compiled 10Be data from rock outcrop studies todetermine the Earth's geological erosion rates. These data werecollected for rock outcrop with no soil cover and differing heightabove the surface.

As discussed, for each soil-mantled study site, soil production ratesderived from parent materials were related to the overlying thickness ofthe soil. We fit an exponential function for each data set of SPR using anonlinear least squares method, to obtain a potential average weatheringrate, P0, at zero soil depth, as presented in Eq. (3) (Table 1).We also fitted

e shown in black whereas data from other parts of the world are shown in grey.

51U. Stockmann et al. / Geoderma 216 (2014) 48–61

the function to the whole dataset of measured TCN-derived SPR tocalculate an estimate of the potential average global weathering rate.

The data set of TCN-derived SPR was also used to investigate theimpact of geographical variations such as climate, parent materialsand tectonic settings on rates of pedogenesis. Furthermore, TCN-derived SPR were compared with worldwide soil formation ratesestimated from different methods (e.g. denudation rates, massbalance, solutes). The compiled list is a revised and updated versionof Montgomery (2007) who assessed soil formation and soil erosionrates for agricultural sustainability (see Appendix, Table 1).

4. Results

4.1. Compilation of published TCN-derived SPR data

As shown in Table 1, field studies were situated in soil mantledlandscapes of Australia, North America and South America. Measuredrates of soil production calculated from field data ranged between 0.1and 594mmkyr−1 (0.0001 and 0.6mmyr−1, n=226) (Fig. 3A and Bshow histograms of the normal and base 10 logarithmic transformeddistribution of measured SPR), and were calculated for soils of upto 108 cm of depth. Mean rates of soil production are around67mm kyr−1. However, the median of 27mmkyr−1 appears to moreadequately represent the highly skewed distribution of the data.

Table 1TCN-derived average SPR for study sites in Australia, North America and South America.

Reference Study site Elevation Climate

AustraliaHeimsath et al.(2000)

Bega Valley, Nunnock River, NSW 200m Warm-temperateRainfall 910mmy

Heimsath et al.(2001a)c

Southeastern highlands,Frogs Hollow, NSW

900m Semi-aridRainfall 550–750

Heimsath et al.(2006)

Snug (lowland), Brown Mountain(escarpment crest), NSW

200m Warm-temperateRainfall 870mmyRainfall 690mmy

Heimsath et al.(2009)

Arnehm Land, Tin Camp Creek, NT 150m TropicalSeasonal rainfall (1400mmyr−1

Heimsath et al.(2010)

Tyler Pass, Central Australia 815m Semiarid

Stockmann(2010)

Werrikimbe National Park, NSW 1100m Warm-temperatetropicalRainfall 2000mm

Wilkinson et al.(2005a,2005b)

Southeastern highlands,Blue Mountains, NSW

1100m Warm-temperateRainfall 900–1000

North AmericaHeimsath et al.(1997, 1999)

Tennessee valley, California, USA 200m Semi-aridRainfall 760mmy

Heimsath et al.(2001b)

Oregon coast range, USA 300m Humid-temperateRainfall 2000mm

Heimsath et al.(2005)

Point Reyes, California, USA 150m Mediterranean

Heimsath et al.(2012)

San Gabriel Mountains,California, USA

855 to 2494m

South AmericaOwen et al.(2011)

Atacama desert, Northern Chile 1170 and1450m

Hyperarid

687 and 670m Arid377 and 400m Semiarid

a P0 is the potential physical and chemical weathering rate of parent materials at depth h=b b is the rate constant (mm−1).c Here, we included the reference values of field-measured SPR at zero soil depth in the fit of

explains the relatively low P0 value found here when compared to the Heimsath et al. (2001a)apparent soil production rates at zero soil depth (143mm kyr−1), for Frogs Hollow.

4.2. Relationship of SPR and soil depth

The data set of SPR was first grouped by soil depth intervals (i.e.‘Surface’, ‘0–30 cm’, ‘30–60 cm’, ‘N60 cm’) to investigate the overallbehaviour of the rate of soil production with depth. The box plots(Fig. 4) revealed that median rates of soil production decreased withincreasing distance of the soil surface to the unweathered parentmaterial, for the whole data set. The rate of soil formation is thereforereduced with an increasing layer of soil and we can therefore arguethat a thick layer of soil is protecting the bedrock from intenseweathering.

Subsequently, for each individual TCN-derived SPRdataset,measuredsoil production rates were related to the thickness of the soil profile, andthe depth-dependent exponential function of soil production (Eq. (3))was fitted for each geographical location (Figs. 5 and 6A, B and C).These weathering rates (P0) at zero soil depth for each field site,calculated in this study, will be discussed further with reference to thesite location.

4.2.1. AustraliaStudies conducted in soil-mantled Australian landscapes are situated

in relatively stable tectonic settings across three climatic zones (Table 1);the warm-temperate and subtropical climate of south-eastern Australia(Heimsath, 2006; Heimsath et al., 2000, 2001a, 2006; Stockmann,

Geology P0 (mmkyr−1)a bb

r−1Granite and granodiorite of theBega Batholith

62± 6 2.180±0.354

mmyr−1Ordovician metasediments andDevonian granites

31± 11 0.419±1.034

r−1

r−1

Ordovician metasediments andDevonian granites

21± 1229± 6

0.511±1.0800.938±0.429

Oct. to Apr.)Sandstone 75± 24 3.194±0.724

Late Palaeozoic lithified conglomerate 7±0.2 0.233±0.077

to sub-

yr−1

Devonian, Carboniferous, Permiansedimentary andmetamorphic rocks

10± 4 0.125±0.617

mmyr−1Triassic sandstone 13± 1 0.215±0.268

r−1Greenstone, greywacke, sandstoneand chert (Jurassic–CretaceousFranciscan assemblage)

56± 11 1.305±0.825

yr−1Eocene turbidite sandstone andsiltstone (Tyee/Flourney formation)

205±26 1.444±0.424

Granitic rocks (quartz diorite,granodiorite)

91± 6 1.817±0.266

Granitic and metamorphic rocks 224±26 2.789±0.869

Mixture of volcanic, plutonic andmetasedimentary rock

1±0.1 1.824±0.394

3±1 17.14±11.7641± 11 3.77±1.58

0 (mm kyr−1) derived from TCN-derived SPR.

the depth-dependent exponential decay function and therefore the calculation of P0 whichpaper. Heimsath et al. (2001a) excluded these values when calculating the maximum in

Fig. 3. A. Data set of soil production rates, plotted in histogram form (normal distribution). B. Data set of soil production rates, plotted in histogram form (log-transformed distribution).

52 U. Stockmann et al. / Geoderma 216 (2014) 48–61

2010; Wilkinson et al., 2005b) and the monsoonal tropical climate ofnorthern Australia (Heimsath et al., 2009).

Studies by Heimsath et al. (2000, 2001a, 2006) are situated alongtransects of the Great Escarpment of south-eastern Australia incor-porating sites over a variety of landscapes, in the coastal lowlands, atthe base of the escarpment and in the highland areas. Soil productionrates decreased exponentially with increasing soil thickness at allstudy sites and were influenced by burrowing animals and soil creep(Heimsath et al., 2002). Calculated potential weathering rates of thegranitic parentmaterials at the lowland site of Snug and the escarpmentbase site of Nunnock River are characterized by differing P0 values of21± 12mmkyr−1 and 62± 6mmkyr−1, whereas the highland sitesof Frogs Hollow and Brown Mountain are defined by very similar P0values of 31±11mmkyr−1 and 29±6mmkyr−1 (Table 1, Fig. 6A, 1–4).

Estimated, potential average weathering rates of sandstone parentmaterials in the Blue Mountains, a study site also located in the south-eastern Australian highlands are much lower with a P0 value of 13 ±1 mm kyr−1 (Fig. 6A, 5) compared to sites near the Bega Valley(Wilkinson et al., 2005a). The authors (Wilkinson et al., 2005a) arguedthat soil formation depended on a finite depth of soil with a peak insoil production of 18 mm kyr−1 at 10 cm of soil cover and postulated

that soil production potentially followed a humped function (Gilbert,1877) in this soil-mantled, bedrock dominated environment.

At the study site of Stockmann (2010), Werrikimbe National Park,which is also situated along the Great Escarpment in south-easternAustralia, rates of soil production varied between 3 and 18mmkyr−1

for the parentmaterial of sedimentary rocks. Therewas no clear patternof SPR with soil depth at this site. Judging from field observations thatshowed the presence of a soil mantle with varying thickness along thehillslopes, the landscape that formed appeared to be most likely theresult of exponentially influenced soil production as conceptuallydiscussed in Dietrich et al. (1995). The average potential weatheringrate of the parent material (P0) is estimated to be approximately10 ± 4 mm kyr−1 (Fig. 6A, 6) which is the lowest P0 value whencompared to the other studies situated along the Great Escarpment ofsouth-eastern Australia. However, overall, no apparent variations ofSPR in orders of magnitude were found for these field studies situatedin different geographical settings along the Great Escarpment (Fig. 7).

In contrast, at the study site of Heimsath et al. (2009) in tropicalnorthern Australia, Arnhem Land, soil production was suggested tofollow a humped soil production function resulting from observedmaximum SPR under an intermediate soil thickness. Furthermore, the

Fig. 4. Statistical analysis of soil production rates using box plots. Soil production rates are grouped by soil depth intervals. Themedian of SPR decreaseswith increasing distance of the soilsurface to the unweathered parentmaterial, for the whole data set of published TCN. (Themiddle line refers to themedian and the box refers to the interquartile range of the distribution.The distance between the lines extending from the box plots equals the data points that are still found within a 1.5 interquartile range from the quartiles of the distribution.).

53U. Stockmann et al. / Geoderma 216 (2014) 48–61

soil landscape was characterized by outcrops and the absence of soilthicknesses shallower than the maximum in soil production. Thesecharacteristics were suggested as indicators for a landscape potentiallyderived from humped soil production (Dietrich et al., 1995). In ArnhemLand, soil productionwas greatest underneath 35cmof soil with a valueof 20 mm kyr−1. When fitting the depth-dependent exponential soilproduction function to this dataset, a P0 value of 75 ± 23 mm kyr−1

was determined (Fig. 6A, 7). Soil production in Arnhem Land wasinfluenced by tree throw resulting from cyclonic activities as well asperiodic bushfires. The tropical climate of Arnhem Land is considerablydifferent to the climates of field sites located in the south-easternAustralian highlands, but nevertheless soil production rates are ofsimilar intensity.

Heimsath et al. (2010) also investigated soil production rates acrosssoil-mantled convex-up spurs at Tyler Pass in semiarid central Australia.The site is characterized by uniform soil thicknesses of about 30cm and

Fig. 5.Best-fit to field data adjusting the exponential decay function using a nonlinear least squazero soil depth is plotted individually for each data set. Data from Australia are shown in blacconventionally as in pedology, demonstrating the behaviour of a parameter down the soil profi

is strongly influenced by bioturbation. Potential weathering rates arecomparatively low with a P0 value of 7.2 ± 0.2 when fitting theexponential soil production model (Fig. 6A, 8).

4.2.2. North AmericaIn North America, TCN-derived SPR were estimated for tectonically

stable as well as active landscapes situated in three different climaticzones (Table 1). Heimsath et al. (1997, 1999, 2001b, 2005) conductedfield studies in the semi-arid and the Mediterranean climate ofCalifornia, USA, and the humid-temperate climate of Oregon, USA. Forall field sites exponentially decreasing soil production rates withincreasing soil thickness were found. At the field site in the TennesseeValley, California, the potential weathering rate of the sedimentaryparent material was characterized by a P0 value of 56±11mmkyr−1,whereas at Point Reyes, California, the potential weathering rate of thegranitic parent material (P0) had a value of 91 ± 5 mm kyr−1 when

resmethod. Here, the best fit of SPR including the potential averageweathering rate (P0) atk whereas data from other parts of the world are shown in grey. Average SPR are plottedle. Calculations were made with SPR as the dependent variable.

54 U. Stockmann et al. / Geoderma 216 (2014) 48–61

fitting the exponential soil production model (Fig. 6B, 1–2). At both ofthese sites, biogenic activities contributed to the production of soil; anobservation that is similar to the field sites of Heimsath et al. (2000,2001b, 2006) in the Bega Valley, Australia. In fact, P0 estimatescalculated for these study sites in California fit very well within therange of SPR determined for Australian environments. There is noorder of magnitude difference between these rates (Fig. 5).

The data sets from the Oregon coast range (Heimsath et al., 2001b)and the San Gabriel Mountains (Heimsath et al., 2012), however, donot show the same range of SPR. With estimated potential weatheringrates of P0 of 205± 26 mm kyr−1 (sedimentary parent material) and224 ± 26 mm kyr−1 (granitic and metamorphic rocks), respectively,TCN-derived soil production rates are significantly higher (Fig. 6B,3–4). The Oregon coast range and the San Gabriel Mountains are bothsituated in tectonically very active environments where landslidesaccount for periodically high erosion rates which in turn most likelyexplain one order of magnitude higher SPR when compared to theother publically available data sets. In a previous study, DiBiase et al.

Fig. 6.A. Best-fit to field data adjusting the exponential decay function for study sites in Australidata set. SPR are plotted as in soil science against depth. Note: All calculations were made wifunction for study sites in North America. Each graph shows the estimate of the average fit ofdepth. Note: All calculations were made with SPR as the dependent variable. C. Best-fit to fiegraph shows the estimate of the average fit of soil production in mm kyr−1, for each data seSPR as the dependent variable.

(2010) estimated erosion rates for the San Gabriel Mountains andfound that this mountain range indeed experienced relatively higherosion rates over the years in the orders of 35 to 1100mmkyr−1.

4.2.3. South AmericaIn South America, TCN-derived SPR were determined for hyperarid,

arid and semiarid landscapes of Northern Chile in stable and activetectonic settings (Fig. 6C, 1–3). The study of Owen et al. (2011) showedthat soil formation was relatively fast in semiarid environments(estimated potential P0 of 40.54± 11.16) when compared to arid andhyperarid environments where soil formation rates were very slow(estimated potential P0 of and 3.40 ± 1.09 and 1.03 ± 0.009,respectively). Soil formation was driven by biota in the semi-aridstudy areas in the southwhereas abiotic factors (salt-driven) influencedsoil formation in the hyperarid study areas in the north. This studyfound evidence for precipitation-dependent erosion rates of bedrock,i.e. erosion rates decreased with increasing aridity which in turn

a. Each graph shows the estimate of the averagefit of soil production inmmkyr−1, for eachth SPR as the dependent variable. B. Best-fit to field data adjusting the exponential decaysoil production in mm kyr−1, for each data set. SPR are plotted as in soil science againstld data adjusting the exponential decay function for study sites in South America. Eacht. SPR are plotted as in soil science against depth. Note: All calculations were made with

Fig. 6 (continued).

55U. Stockmann et al. / Geoderma 216 (2014) 48–61

might be related to the decreasing availability of biota for weatheringprocesses.

4.3. Relationship of SPR grouped by parent materials and climate

The SPR dataset was grouped by the climatic zones in which thestudies took place as well as the parent materials from which thesoils originated. Here, only main climatic zones and broad groups ofparent rocks were considered, i.e. ‘igneous rocks’, ‘metamorphic rocks’,‘sedimentary rocks’ and ‘mixed rocks’. Statistical analysis of the datausing box plots showed that SPR are relatively similar for the differenttypes of consolidated parent rocks (Fig. 8) but differ in velocity byclimatic zone (Fig. 9). However, more field data from differentenvironments are needed to support these trends. For instance, Popeet al. (1995) reviewed quantitative weathering data based on chemicalweathering studies (solutes, mass-balance approach) for a wide rangeof environments from polar to tropical humid climates. Their data, forinstance, did not reveal a climatic signal, sparking the question ofwhether the top-down assumption of weathering being based on

climatic zones may be overrated. Pope et al. (1995) then proposed abottom-up perception of weathering processes where geographicvariability in weathering is controlled by the variability in weatheringprocesses that operate at microscale. Similarly, a study based onuranium-series isotopes (Dosseto et al., 2008) suggested that the rateof bedrockweathering at a study site in temperate Australiawas definedby the same order of magnitude as the rate inferred for laterites intropical climates.

5. Discussion

5.1. Field observations of how soils form over time-based on TCN-derivedSPR

The majority of published datasets on SPR showed an exponentialdecline of soil production with increasing soil thickness. However, forsome environments exponential decline of soil production with depthwas postulated only (Heimsath et al., 2010; Owen et al., 2011;Stockmann, 2010). For two field studies situated in Australia the highest

Fig. 7. Rates of soil production for field sites along the Great Escarpment in south-eastern Australia (Heimsath et al., 2000, 2001b, 2006; Stockmann, 2010;Wilkinson et al., 2005a,2005b).SPR for lowland and highland sites are very similar, they do not vary much in orders of magnitude.

56 U. Stockmann et al. / Geoderma 216 (2014) 48–61

rate of soil production seemed to be dependent on some intermediatesoil depth which led to the assumption that soil production most likelyfollowed the conceptual model of a humped function. However, at theBlue Mountains site only a local occurrence of the humped functionwas assumed, because of lithologic variations in the area and thepotential existence of a soil cover that was eroded 10 kyr ago(Heimsath et al., 2009; Wilkinson et al., 2005b). A local occurrence ofhumped soil production in this sandstone environment appears to bereasonable as an incipient soil cover would allow the retention ofwater which is one of the most important agents for chemicalweathering near the soil-bedrock interface. At the Arnhem Land site,humped soil productionwas assumed, based on observed soil landscape

Fig. 8. Statistical analysis of soil production rates using box plots. Soil production r

characteristics that were consistent with an environment resultingfrom humped soil production. Evidence for the existence of a humpedfunction of soil production comes from field observations whereweathering rates were highest under an incipient soil cover (Andersonet al., 2007). The unequivocal field evidence for soil production followinga humpedmodel is still to be verifiedwith TCN data although it has beendiscussed widely in the literature either as a conceptual model (Gilbert,1877; Humphreys and Wilkinson, 2007; Wilkinson et al., 2005b) orincorporated in soil-landscape evolution models (Carson and Kirkby,1972; Heimsath et al., 2009; Pelletier and Rasmussen, 2009; Smallet al., 1999). In addition, a successful empirical parameterisation of thehumped model from field data is still to be achieved.

ates are grouped by soil depth intervals and main parent material categories.

Fig. 9. Statistical analysis of soil production rates using box plots. Soil production rates are grouped by soil depth intervals and main climate categories.

57U. Stockmann et al. / Geoderma 216 (2014) 48–61

5.2. What is the average rate of soil production?

It is generally postulated that the soil in Australian landscapes isancient and highly weathered (McKenzie et al., 2004). The conversionrates of soil parent materials to soil at zero soil depth (P0) appear tobe very similar in the Australian landscapes studied, despite theirdiverse environments with differing climatic conditions and parentmaterials. In addition, rates of soil formation in Australia appear to bein similar ranges when compared to other parts of the world (Fig. 10Aand B). However, with about ten-times higher soil production rates,the data sets from the Oregon coast range and the San GabrielMountains in North America do not fit the overall trend of similarbehaviour. As discussed, non-steady erosion rates account for highvalues of SPR in these tectonically active landscapes. Generally, forfield studies set in a tectonically active environment with fluctuatingerosion rates, the assumption of steady-state erosion for calculatingSPR does not apply (Heimsath, 2006). With regard to the field sites inthe Bega Valley of south-eastern Australia there is also the possibilitythat erosion rates were non-steady during periods of dramatic climatechange in the Pleistocene (Heimsath, 2006). However, this is notseen in the compiled SPR data. With the exception of two datasets(Heimsath et al., 2001b, 2012) all TCN-derived SPR fall within a similarrange and do not vary as much as expected; i.e. SPR vary between 10and 100mmkyr−1. These differences fall within one order ofmagnitude(Fig. 10A and B).

Nevertheless, because of the overall behaviour of decline in SPRwithincreasing distance of the soil surface to the parent material (refer toFig. 4), we attempted to calculate an average best fit for the wholedataset of TCN-derived SPR to generate an average potentialweatheringrate of soil parent materials based on in-situ measurements (Fig. 10Aand B which displays unmodified values as well as the log-scale).

The calculated overall value for P0 of 114.27±10.93mmkyr−1 withb=−2.05±0.47mm−1 therefore represents a quantitative estimate ofsoil production and is useful to understand how soils grow in-situ and tomake assumptions of the magnitude of (global) soil production. Thisrate will be beneficial for example to inform and to be included indynamic global modelling of soil formation as it provides basic datawhich can be used to model the stability of the soil landscape. Ourestimated average rate of soil production is somewhat faster whencompared with global rates of soil formation reported in the literature,e.g. 58 mm kyr−1 and 83 mm kyr−1 (Montgomery, 2007; Wakatsukiand Rasyidin, 1992).

By formulating this average TCN-derived SPR, it needs to be clarifiedhere that the soil production rate mostly reflects the physical inter-actions that develop a soil profile by converting parent material to soil(Burke et al., 2007). Therefore, the variation in chemical weathering ofdifferent soil parent materials does not have a major input (White andBrantley, 2003) as SPR are mainly related to the thickness of the soilprofile. Consequently, processes that lead to alterations of the soilprofile, eventually forming a particular soil horizon or soil type, are notconsidered. Hence, formulating an average soil production rate doesnot imply uniform soil profiles or soil types that form through variouspedogenetic processes.

Recent studies have investigated the relationship between physicalweathering and chemical weathering in the conversion process of soilparent materials to soil. These studies applied TCN-derived SPR inconjunction with various weathering indices. For example, Burke et al.(2007) and Yoo et al. (2007) found that chemical weathering accountsfor 13 to 51% of total weathering in soils, respectively. Green et al.(2006) published similar percentages for the study site at NunnockRiver in the Bega Valley, south-eastern Australia (Heimsath et al., 2000),implying that mass loss in solution accounted for 35 to 55% of the totalmass loss from the investigated hillslope. Another study examined theextent of chemical weathering, comparing field sites in the lowlandsand highlands in the region of the Bega Valley (Burke et al., 2009). Thisstudy indicated that the extent of chemical weathering is lower in thehighland sites (47%) than in the lowland sites (57%). Similarly, resultscomparing chemical and physical weathering rates along a transectfrom low to high elevation by Dixon et al. (2009) for the Sierra NevadaMountains, California imply that chemical weathering peaks at midelevations compared to high and low elevation sites and that physicalerosion rates increase with both saprolite weathering rates and intensity.These studies indeed confirm that physical weathering is the dominantprocess in converting parent materials to soil.

5.3. Comparison of SPR with soil formation rates derived from othermethods

The TCN-derived soil production rates were compared with soilweathering rates derived from different methods (e.g. denudation rates,mass balance, solutes, see Appendix, Table 1). Here, only soil formationrates on rock-like consolidated parentmaterials were included, i.e. parentmaterials such as volcanic ash and loess were excluded. Characteristics ofsoil formation and soil production rate distributions of the compiled data

Fig. 10. A. Average soil production rate (P0), derived with TCN data, for study sites in Australia, North America and South America. SPR are plotted conventionally as in pedology. Originalvalues. Note: All calculations were made with SPR as the dependent variable. B. Average soil production rate (P0), derived with TCN data, for study sites in Australia, North America andSouth America. SPR are plotted conventionally as in pedology. Log-scale. Note: All calculations were made with SPR as the dependent variable.

58 U. Stockmann et al. / Geoderma 216 (2014) 48–61

sets and probability distribution plots show that there is a relativeconsistency in the overall data (Table 2, Fig. 11). Although the TCN-derived SPR are slower when compared to other methods, the range ofsoil weathering is quite similar, regardless of which methods wereapplied to quantify soil formation. The smaller values of TCN-derivedSPR aremost likely due to themuch lower values of production at depths.

5.4. Modelling ‘global’ soil production based on the TCN-derived averageSPR

The calculated average soil production rate of 114.27±10.93mmkyr−1

was used to estimate soil production over tens of thousands of years.For this purpose the simple model of Heimsath et al. (1999) and

Table 2Characteristics of soil formation and soil production rate distributions for the compiled data sets.

Method Sample size (n) Median (mm kyr−1) Mean (mm kyr−1) Standard error (mm kyr−1)

Soil production rates TCN (10Be) 226 27.50 67.25 6.04Soil weathering rates Other (denudation, mass-balance, solutes) 125 14.80 41.83 6.34

59U. Stockmann et al. / Geoderma 216 (2014) 48–61

Minasny and McBratney (1999) was implemented to determine theresidence time of the soil:

dt ¼ dhρparent

ρsoilexp P0 −btð Þ

ð4Þ

where ρparent is the density of the parentmaterial and ρsoil is the densityof the soil. In the application of this model, steady-state conditions ofsoil production with no gain or loss of soil through erosion and

Fig. 11.A. Probability distribution plots of rates of soil production derivedwith in-situ TCNdata and weathering rates of soil derived from other methods. B. Density plot distributionof soil production rates derived with in-situ TCN data and weathering rates of soil derivedfromothermethods. Probability distribution anddensity plots show that there is a relativeconsistency in the datasets of soil production and soil weathering rates.

deposition, and a constant bulk density throughout the soil profilewere assumed. A density ratio of soil parent material (bedrock) to soilof 1.4 was employed. The data obtained by using this simple soilproduction model indicate that the soil residence time for a soil profileof 1mdepth is approximately 47,000years, applicable if soil is producedat a rate of approximately 0.1mmyr−1 (Fig. 12).

Under this average rate of soil production, only 8 mm of soil areproduced in approximately 50years. The production of soil from parentmaterials is therefore relatively slow when employing the proposedTCN-derived average rate of soil production. This rate does suggestthat soil is not renewable on societal time scales. This supposition isonly applicable, however, where soil ‘renewal’ results from weatheringof unaltered bedrock without ‘human interventions’. Soil conservationprogrammes have been initiated worldwide to improve the fertility of(top)-soil (Pimentel et al., 1995). Furthermore, soil reclamation orrehabilitation is practised after major environmental disturbances (e.g.mining, see Grigg et al., 2006). It remains to be seen whether or notthese conservation programmes will be successful in the long-run.

It is also important to consider soil production rates from un-consolidated materials such as volcanic ash and loess, which are muchhigher than from unaltered bedrock. Weathering rates as high as 5 to15 mm yr−1 have been recognized in the literature for Indonesiansoils formed on volcanic ash after the 1883 eruption of Krakatoa(Hardjowigeno, 1992; Jenny, 1941).

6. Conclusions

The rate of soil formation over time has been discussed extensivelyin the literature on pedogenesis. In recent years, with the applicationof TCN techniques in soil science it has become possible to quantifythe formation of a soil profile in-situ over millennial time scales.

1. The compilation of publically available data of TCN-derived SPRshowed that the rate of soil production of world soils is reducedwith an increasing thickness of soil. In addition, the compilation ofpublished TCN-derived SPR for different climatic zones and lithologicconditions showed exponentially decreasing soil production rateswith increasing soil thickness for the majority of the studies. Wecan therefore argue that a thick layer of soil is protecting the bedrockfrom intense, further weathering.

0

0.5

1

1.5

2

0 0.1 0.2 0.3 0.4 0.5

So

il th

ickn

ess

(m)

Time 106

dtdh

P0exp(_bt )ρparentρsoil

Fig. 12. Soil profile development over tens of thousands of years assuming steady-stateconditions with no soil gain or loss and constant bulk density of the soil profile, applyingthe average SPR. The displayed equation was revised after Minasny and McBratney(1999), where ρparent is the density of the parent material and ρsoil is the density of thesoil, P0 is the potentialweathering rate of the parentmaterial and b is an empirical constant.

60 U. Stockmann et al. / Geoderma 216 (2014) 48–61

2. The calculation of a best-fit of the exponential decay soil productionfunction for each dataset showed that estimated potential weatheringrates for zero soil depth (P0) appear to be quite similar for theinvestigated environments. Overall, the range of rates of soil formationin Australia was similar to rates in North and South America. Theobserved similar trend of decreasing SPR with increasing distance ofthe soil surface to the parent material apparent in the data set on SPRresulted in the assumption that we can formulate a quantitativeestimate of the rate of ‘global soil production’ based on TCN with a P0value of 114.27±10.93mmkyr−1.

3. When grouping measured SPR by differing consolidated parentmaterials and climatic condition, no significant differences wereobserved, although we found a tentative trend for SPR to potentiallydiffer by climatic zones which would support assumptions that thevariability in weathering is depended on climatic zones. However,the use of TCN is still relatively new in pedological studies andadditional field studies are still required to better understand thecontrols of soil production and to confidently discuss thedependencyof weathering on the macroscale (top-down approach) or meso-/microscale (bottom-up approach).

4. Overall, the distribution of TCN-derived SPR is quite similar whencompared to soil weathering rates derived from other methods.

5. Implementing the potential global average weathering rate (P0) of0.1 mm yr−1 in a simple soil profile evolution model showed thatsoil residence times are of approximately 47,000 years understeady-state conditions of soil production.

Acknowledgements

The authors would like to thank two anonymous reviewers for theiruseful comments on an earlier version of this manuscript.

Appendix A. Supplementary data

Supplementary data to this article can be found online at http://dx.doi.org/10.1016/j.geoderma.2013.10.007.

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