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Agriculture, Ecosystems and Environment 139 (2010) 98–109

Contents lists available at ScienceDirect

Agriculture, Ecosystems and Environment

journa l homepage: www.e lsev ier .com/ locate /agee

s there a link between elevated atmospheric carbon dioxide concentration,oil water repellency and soil carbon mineralization?

arin Müllera,∗, Markus Deurerb, Paul C.D. Newtonc

The New Zealand Institute for Plant & Food Research Limited (PFR), Private Bag 3123, Waikato Mail Centre, Hamilton 3240, New ZealandPFR, Private Bag 11030, Manawatu Mail Centre, Palmerston North 4442, New ZealandAgResearch Ltd., Grasslands Research Centre, Palmerston North 4442, New Zealand

r t i c l e i n f o

rticle history:eceived 13 November 2009eceived in revised form 7 July 2010ccepted 9 July 2010vailable online 4 August 2010

eywords:ree Air Carbon Dioxide Enrichment

a b s t r a c t

We hypothesized that elevated atmospheric carbon dioxide concentration [CO2] as a feature of climatechange promotes the occurrence of soil water repellency (SWR) which reduces soil carbon mineralizationand thus increases carbon sequestration. Soil surface transects under elevated (475 �L L−1) and ambientatmospheric [CO2] in a long-term Free Air Carbon Dioxide Enrichment experiment were sampled at ahigh spatial resolution. All samples were hydrophobic at the time of the sampling. At the micro-scale, thedifferences in degree and persistence of SWR, soil organic matter (SOM) content, microbial respirationrates (MRR) and water content between the treatments were not significant. SWR was not correlated

xperimentarbon mineralizationydrophobicityrganic carbonlevated atmospheric carbon dioxidenfiltration

with SOM or MRR. A strong correlation between water contents and SWR parameters demonstrated theimportance of SWR for water redistribution. At the meso-scale of disc infiltrometry, infiltration rateswere reduced by SWR, and were higher under ambient than under elevated [CO2]. This corroborates thetendency of reduced SWR under elevated [CO2] observed at the micro-scale. SWR showed a spatial struc-ture, exhibiting short ranges. SOM and MRR showed no spatial pattern at the scale analyzed, emphasizingthat SWR did not contribute to an increase of the long-term terrestrial C sink in response to increased

atmospheric [CO2].

. Introduction

Soil water repellency (SWR) is when a soil does not wet uppontaneously when water is applied to its surface. It is a transientoil property and will occur whenever soils dry out below a ‘crit-cal soil water content’ (Dekker and Ritsema, 1994), which mightccur more often given the extent to which climatic extremes androughts have been forecast for most regions in the wake of climatehange (Watson, 2001; Meehl et al., 2007). A true understanding ofhe ecological significance of SWR is still very limited.

We hypothesized that elevated atmospheric [CO2] as a typicaleature of climate change promotes the occurrence of SWR whicheduces soil carbon mineralization and thus decreases CO2 emis-ions from the soil. The mineralization of soil organic carbon (SOC)s governed by the quality of soil organic matter (SOM), viz. theioavailability of SOC to microorganisms, soil temperature and soil

ater content (Kirschbaum, 1995; Leiros et al., 1999; Fang andoncrieff, 2005). All three factors are affected by climate change.

or example, it is known that elevated [CO2] enhances root growthnd root turnover rates (Canadell et al., 1996; Allard et al., 2005).

∗ Corresponding author. Tel.: +64 7 959 4555; fax: +64 7 959 4430.E-mail address: karin.mueller@plantandfood.co.nz (K. Müller).

167-8809/$ – see front matter © 2010 Elsevier B.V. All rights reserved.oi:10.1016/j.agee.2010.07.005

© 2010 Elsevier B.V. All rights reserved.

This may stimulate soil carbon mineralization rates (Sowerby etal., 2000; Paterson et al., 2008). But also, no consistent effect of ele-vated [CO2] on the turnover rates of the soil carbon pools has beenreported (Pendall and King, 2007). Elevated [CO2] could also modifythe quality of SOM, which in turn would change the soil’s wetta-bility. SWR is considered a material property of SOM (Ellerbrocket al., 2005), and has been shown to be correlated with differentfractions of OM (McKissock et al., 2003; Doerr et al., 2005; Morleyet al., 2005), and is thought to be linked to microbial activities(Hallett and Young, 1999) and root or fungal exudates (Wallis andHorne, 1992). The effects of SWR on carbon mineralization are onlyrarely investigated (Borken and Matzner, 2009). CO2 losses froman agricultural soil were reduced by 30% after adding hydrophobicsubstances to the soil (Piccolo and Mbagwu, 1999). Carbon miner-alization of a silty agricultural soil, silt loam grassland and arablesoils, and a sandy forest soil declined with increasing SWR (Goebelet al., 2005, 2007). Goebel et al. (2005) showed that water-repellentsoil surfaces inhibited aggregates from imbibing water. The result-ing lower water content of the aggregates reduced the microbial

decomposition of the SOM located in the aggregates.

The site of this study has been exposed for 10 years to ele-vated atmospheric [CO2] in a Free Air Carbon Dioxide Enrichment(FACE) experiment, at Bulls, New Zealand. Ambient atmospheric[CO2] levels are expected to reach the elevated [CO2] of 475 �L L−1

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K. Müller et al. / Agriculture, Ecosyst

imulated in this experiment around 2030. The soil at the site is aandy soil prone to drying out and SWR (Wallis et al., 1993; Newtont al., 2003). The elevated atmospheric [CO2] and soil water con-itions mimic a future scenario anticipated for many regions asconsequence of climate change. We hypothesized that elevated

tmospheric [CO2] would affect the occurrence of SWR. Newtont al. (2003) showed for the same experimental site that the per-istence of the potential SWR significantly decreased after 5 yearsf exposure to elevated atmospheric [CO2]. They did not analysehe degree of SWR. The mechanism responsible for the observedhange in repellency has not been identified. To the best of ournowledge this is the only published study on the impact of ele-ated [CO2] on SWR.

How climate change affects soil physical properties such as soilater dynamics (Niklaus et al., 1998; Nelson et al., 2004; Nowak

t al., 2004) is even less well understood. We hypothesized thatWR is an important mechanism for the local water redistributionn dry soils and that it is part of a terrestrial feedback mechanismso climate change. SWR considerably slows down the increase inoil water contents following rain, exacerbates the occurrence ofreferential flow pathways, which results in an inhomogeneousattern of soil moisture. The local redistribution of soil water is

mportant for soil microbial activities and carbon mineralizationBirch, 1958a,b).

It is clear that we require a better understanding of the interac-ions between soil biology and the resulting physical soil properties.o date, most studies investigating the occurrence of SWR focusedn subcritically water-repellent soils with contact angles below 90◦

Goebel et al., 2004, 2005, 2007; Woche et al., 2005; Lamparter et al.,009). Hydrophobic soils have been rarely investigated. Therefore,he objectives of our study were to assess in hydrophobic soils (1) iflevated atmospheric [CO2] increased SWR; (2) if SWR influencedarbon mineralization; and (3) if SWR could indirectly influenceoil carbon mineralization by governing soil water distribution inoils.

. Materials and methods

.1. Description of study site

The study site has been exposed for 10 years to elevatedtmospheric CO2 in a Free Air Carbon Dioxide Enrichment (FACE)xperiment at 40◦14′S and 175◦16′E, Bulls, New Zealand. The FACExperiment has been described in detail elsewhere (Newton etl., 2001). In brief, since October 1997, three circular experimen-al areas of 12 m diameter have been enriched to 475 �L L−1 CO2uring the photoperiod. Three control rings were installed at theame time. The rings are in a 2.5-ha field and contained with per-anent fenced areas of 25 m × 25 m (Fig. 1). The site carries an

stablished pasture that contains a range of about 25 plant speciesncluding legumes (Trifolium repens L., Trifolium subterraneum L.),3 and C4 grasses (Agrostis capillaris L., Lolium perenne L., Poa praten-is L., Cynodon dactylon L.) and dicotyledons (Leontodon saxatilis., Hypochaeeris radicata L.). The permanent pasture of all rings iseriodically grazed by sheep that are enclosed within the treat-ent area during the grazing period. The long-term average rainfall

1945–1995) at the experimental site was 874 mm and the meannnual temperature is 12.9 ◦C.

The soil at the site is classified as a Mollic Psammaquent (FAO,006) with 0.2 m black loamy fine-sand topsoil characterized by

ew distinct reddish mottles in the lower part of the horizon, whichas a weakly developed nutty structure and leads with a sharpoundary to 0.2 m grey single grained sand. There was no statisti-al difference in the soil texture of the two treatments, with valuesnder ambient and elevated [CO2] being, respectively: sand, 87 ± 2

d Environment 139 (2010) 98–109 99

and 86 ± 1%; silt, 7 ± 1 and 9 ± 1%; and clay, 6 ± 2 and 5 ± 1% (pers.communication Des Ross). Soil tests showed soil pH(water) to be 5.8,and an Olsen-P of 20 �g mL soil−1 (Newton et al., 2006).

2.2. Pasture sampling

Pasture herbage in eight randomly placed rectangles(1 m × 0.078 m) in each ring was harvested by cutting 20 mmabove-ground level just before grazing events. A sub-sample ofabout 50 g from the bulked herbage in each ring was sorted intospecies before drying for 24 h at 60 ◦C to determine dry weight ofthe total herbage and its botanical components (Edwards et al.,2001).

2.3. Soil sampling

The soil samples were collected at the end of summer whenSWR is at its peak. On 8 March 2008, we took soil cores (0.025 mdiameter × 0.020 m deep) at 0.1-m intervals along a 4-m transectacross each ring (41 samples per ring). All transects started at thecentre of a ring but their orientation was randomly chosen to avoidbias based on the distribution of vegetation and micro-topography(Fig. 1). The soils were placed immediately in plastic bags that weretightly sealed to minimise evaporative losses. Plant material, rootsand litter were removed gently by hand before the soil analyses.

2.4. Determination of the persistence and degree of SWR

We quantified the topsoil’s wettability by determining the per-sistence and the degree of SWR: The persistence of SWR can beassessed on field-moist samples for the actual SWR present inthe fresh soil material or on dried samples for the potential SWR(Dekker and Ritsema, 1994). The persistence of the actual SWR wasmeasured in the laboratory using the water drop penetration timetest (WDPT) (King, 1981). In essence, the time it takes for a dropletof water placed onto the soil surface to infiltrate completely into thesoil is recorded. We used the threshold of 5 s proposed by Bisdomet al. (1993) to differentiate between wettable and water-repellentsoils. This threshold is arbitrarily chosen and has no physical mean-ing. We recorded WDPT values up to 6 h and applied the categoriesfor the persistence of SWR defined by Dekker and Jungerius (1990)to the mean of three repetitions per sample. This definition distin-guishes seven classes for the persistence of SWR: class 0, wettable;class 1, slightly persistent SWR (5–60 s); class 2, strongly persis-tent SWR (60–600 s); class 3, severely persistent SWR (600 s to 1 h);and extremely persistent SWR (>1 h), further subdivided into class4 (1–3 h), class 5 (3–6 h), and class 6 (>6 h). Only for 20% of thesamples did triplicate drops have a standard deviation larger than60 s. Then the degree of SWR was determined using the molarity ofethanol-droplet (MED) test and the results were quantified in theform of the contact angles (CA) between the drop of the aqueousethanol solution and the soil surface (Roy and McGill, 2002). Thetest can determine a CA larger than 90◦, which is the threshold forthe occurrence of hydrophobicity. In the MED test the surface ten-sion of the wetting liquid, an aqueous ethanol solution, is variedto the point where the soil spontaneously adsorbs the liquid, andthe contact angle between the soil surface and the liquid is 90◦. Thesoils were oven dried at 65 ◦C for 48 h and then equilibrated for 24 hat room temperature before conducting the MED test (Kawamotoet al., 2007).

2.5. Determination of microbial respiration rates

Soil respiration includes the contribution of root-associated res-piration and mineralization of organic matter. We determined CO2evolution rates of fresh soil samples sieved to 2 mm and thus only

100 K. Müller et al. / Agriculture, Ecosystems and Environment 139 (2010) 98–109

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ig. 1. Photos of the experimental site. (a) A ring of the Free Air Carbon Dioxide Enrhe orientation of the transects was randomly chosen to avoid bias based on the di

onsidered microbial respiration rates (MRR). An aliquot of 5 g freshieved soil material was incubated for 4 h in airtight containers at2 ◦C after a 24-h pre-incubation period at the same temperature.he CO2–C concentration of the headspace of each container waseasured after 1 and 4 h by injecting a sub-sample (0.1–1 ml) of

as into an Infra-Red Gas Analyzer (ADC 225 MK3, The Analyticalevelopment Co. Ltd., Hoddesdon, UK). We assume that the CO2volution rate is linear during the measurement period. Variableolumes of gas had no effect on the accuracy of the analysis.

.6. Determination of total organic matter

The gravimetric soil water content of all soil samples was deter-ined by drying a sub-sample of about 5 g for 24 h in the oven

t 105◦. Afterwards the SOM content was measured by igniting theamples for 5 h at 550 ◦C. Following Täumer et al. (2005), the differ-nce between the dried and ignited samples was taken as the SOMontent, because the samples were non-calcareous with a topsoilH of 5.8.

.7. Quantification of the impact of SWR on water infiltration

Infiltration was measured in the field at the meso-scale of aisc infiltrometer (R = 32.5 mm), while all other analyses were con-ucted with a disturbed soil sample of about 5 g in the laboratory.he impact of the soil’s wettability on water dynamics at thexperimental site was quantified by comparing the intrinsic perme-bilities k of water and ethanol. This approach takes advantage ofhe fact that a fully wetting liquid like ethanol wets both hydrophilicnd hydrophobic soils at a contact angle equal to zero (Letey et al.,000).

We measured the infiltration rate of water with a tensionisc infiltrometer (R = 100 mm) and subsequently the infiltrationate of ethanol at the same location with a glass infiltrometerR = 32.5 mm) on 4 January 2008. The infiltration rate was measuredt two tensions h1, h2 in order to derive the respective hydrauliconductivities K1(h1), K2(h2) (cm s−1) from the steady-state infil-ration rates. For water, the tension was set to h1 = −20 mm and2 = −10 mm. To compare the intrinsic infiltration rates of the twoiquids, these tensions were converted into equivalent tensions forthanol by dividing them by 2.5 (Jarvis et al., 2008). Steady-state

ow into unsaturated soil can be described with the following equa-ion (Wooding, 1968):

(h) = K(h)[

1 + 4�R˛∗

](1)

nt (FACE) experiment, (b) a 4-m soil sampling transect starting at the ring’s centre.tion of vegetation and micro-topography.

where R is the radius (m) of the infiltration surface (m2) and ˛*is the sorptive number of the soil (mm−1). From the steady-stateinfiltration rates q1(h1) and q2(h2), the parameter ˛* can be derived(Ankeny et al., 1991):

a∗ = ln(q1(h1)/q2(h2))h1 − h2

(2)

assuming that ˛* is constant for the chosen range of tensions. Theparameter ˛* is then used to solve Eq. (1) for an average tensionfollowing Reynolds and Elrick (1991). With the derived hydraulicconductivity (K(h1, h2)), the unsaturated hydraulic conductivityfunction proposed by Gardner (1958):

K(h) = Ksat exp(˛∗h) (3)

where Ksat is the hydraulic saturated conductivity (h = 0), wassolved. The conductivities for ethanol Ke(h) and water Kw(h) wereused to estimate the intrinsic permeabilities k (m2). The intrin-sic permeability describes the part of the conductivity K(h) thatdepends only on the properties of the soil through which a liquidis flowing, while the conductivity is a function of both the soil andits properties and of the properties of the liquid itself (Bear, 1972):

K(h) = k�g

�(4)

where � is the dynamic viscosity (N s m−2), which is 0.0010 forwater and 0.0012 for 95% ethanol at 20 ◦C. The ratio of the intrinsicpermeabilities was calculated as

R(k) = ke(h1)kw(h1)

(5)

This ratio R(k) reflects how much the permeability is reducedby SWR. Intrinsic permeabilities account for the specific propertiesof the infiltrating liquid and should, in a hydrophilic soil, be thesame whether determined with ethanol or water. This means thatfor a wettable soil R(k) would have a value of 1. An R(k)-value of10 would indicate that the intrinsic permeability is reduced ten-fold by SWR. Thus, comparing the intrinsic permeabilities of waterand ethanol allows us to quantify by how much SWR reduces waterinfiltration. Like SWR, this ratio would be time dependent. Our ratiowas derived for the infiltration of water after 90 min and for theinfiltration of ethanol at steady state. The importance of relating

the infiltration of water in soils with SWR to a particular time isdiscussed in detail elsewhere (Lamparter et al., 2010). Three rep-etitions of the infiltration experiment within a randomly selectedarea under the elevated and ambient atmospheric CO2 treatmentswere conducted. The values here reported are mean values.

K. Müller et al. / Agriculture, Ecosystems and Environment 139 (2010) 98–109 101

Fig. 2. Spatial distribution of (a) the degree of soil water repellency (SWR), (b) the classes of the persistence of SWR, (c) the soil organic matter (SOM) content, and (d)the microbial respiration rates (MRR) along three transects each under elevated and ambient atmospheric [CO2] on 08 March 2008. Ring 1, 2 and 3 were under elevateda ch tra

2

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tmospheric [CO2], and Ring 4, 5 and 6 under ambient atmospheric [CO2]. Along ea

.8. Statistical analysis

The Shapiro–Wilk test was used to determine if the dataere normally distributed. The WDPT data were log-transformed

o meet the assumptions of the statistical tests. We used theoftware Genstat 9.1.0.150 and a one-way ANOVA to distin-uish if the mean values of properties under the elevatednd ambient CO2-atmospheric levels were significantly differ-nt (P < 0.05). We interpreted the differences between averagesf properties to be significant if they were larger than theirespective least significant differences (˛ ≤ 0.05). Pearson’s Chi-quare test was used to assess whether the observed numbersf soil samples in each WDPT class of elevated and ambientO2-atmospheric levels were significantly different (P < 0.05). Theelationship between parameters (SOM, MRR, soil water content,

DPT and CA) was assessed by linear regression analysis andalculation of Pearson’s correlation coefficient using SigmaPlot0.0. Non-linear dependencies were also tested, but without suc-ess. Data for total herbage production and the proportion of

egumes were analysed for the years 1999–2003 inclusive, for

hich there were 30 individual harvests and for 2004–2008nclusive for which there were 33 harvests. Data were summed

ithin years and then analysed by repeated measures ANOVA inenstat.

nsect, 41 topsoil samples at a distance of 0.1 m were collected.

2.9. Geostatistical analysis

A geostatistical analysis was performed to investigate the spa-tial structure and the distance over which the variables log(WDPT),CA, SOM, and MRR were correlated. The dissimilarity between theobservations as a function of their separation distance h (m) canbe described using experimental semivariograms. The average dis-similarity is measured by the experimental semivariogram, whichis calculated as half the difference between the components ofevery data pair separated by the vector h (Cressie, 1993). Semivari-ograms were created up to half the maximum lag distance (Journeland Huijgbregts, 1978). The structure of spatial dependence ofthe parameters was described by fitting exponential, Gaussian andlinear models with nugget effect to the semivariograms. The expo-nential model is (e.g.) given as

�(h) = Cn + Cs

[1 − exp

(−3h

a

)](6)

where Cn is the nugget variance (variance at zero lag distance), Cs isthe structural variance and a is the range (Cressie, 1993). We chosethe model with the smallest standard error for the effective rangea.

102 K. Müller et al. / Agriculture, Ecosystems and Environment 139 (2010) 98–109

Table 1Mean (±standard error) and coefficient of variation CV for the soil properties contact angle (CA), water drop penetration time (WDPT), microbial respiration rate (MRR), totalorganic matter content (SOM), gravimetric water content (WC), determined under elevated and ambient [CO2] along three transects of 4 m (n = 123). Mean values in thesame row are significantly different if followed by different letters (P < 0.05). The minimum and maximum values of the parameters are presented in italics.

[CO2] Elevated Ambient

Mean CV (%) Mean CV (%)

CA (◦) 100.06 (±0.20)a 2 100.21 (±0.25)a 393.8–103.7 93.8–104.5

WDPT (s) 395 (±83.49)a 234 745 (±124.16)a 1851–28,946 1–21,961

MRR (�g CO2 g−1 24 h−1) 6.08 (±0.20)a 37 6.16 (±0.28)a 512.2–14.4 0.5–18.4a

SOM (%) 10.80 (±0.09)a 10 10.97 (±0.12)a 12

3

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8.2–13.9

WC (%) 19.52 (±0.33)a10.5–29.5

. Results

The first part of the result section is dedicated to SWR: we areresenting the impact of elevated atmospheric [CO2] on SWR, fol-

owed by a general analysis of the phenomenon of SWR at thexperimental site. In the remainder of the result section, we presenthe impact of elevated atmospheric [CO2] on pasture productivitynd the other soil parameters analyzed, MRR, SOM and WC, andheir relation to SWR.

.1. Impact of elevated atmospheric [CO2] on SWR

All topsoil samples collected along transects in the six ringsnder ambient and elevated atmospheric [CO2] were hydrophobic,ith contact angles >90◦ ranging between 93.8 and 104.5◦ (Fig. 2a).

he differences in the degree of SWR between the rings with ele-ated and ambient [CO2] were small and non-significant (Table 1).bout 33% of all samples were wettable within a second (Fig. 3). It isoted that the WDPT reflects only the first wetting step of the outer

oil surface. The persistence of SWR was highly variable (Table 1).

DPT values often varied between smaller than 5 s and larger thanh within a single ring over short distances of a few centimetres

Fig. 2b). The WDPT data were not normally distributed. To normal-

ig. 3. Frequency distribution of the persistence of soil water repellency (SWR) inopsoil samples collected from three rings under ambient (n = 123) and three ringsnder elevated (n = 123) atmospheric [CO2] (475 �L L−1), respectively, on 08 March008. The persistence of SWR was classified in seven classes. The bars denote onetandard deviation.

7.9–14.6

19 18.58 (±0.44)a 262.5–27.8

ize the data they were log-transformed. The differences in actualpersistence of SWR between the rings with elevated and ambient[CO2] were non-significant. The soils under elevated [CO2], how-ever, tended to a lower persistence of SWR (strongly persistent:395 s) than the soils under ambient conditions (severely persistent:745 s). This tendency was corroborated by the significant differentdistribution of samples in the five water repellency classes (Pear-son’s Chi-square test). However, the differences were not largeenough to distinguish ambient from elevated [CO2] within any ofthe five repellency classes (Fig. 3).

We found a linear correlation between the persistence oflog(WDPT) and the degree of SWR (CA):

log(WDPT) = −38.21 + 0.4CA (7)

The correlation was weak (R2 = 0.48) but statistically significant,indicating that a higher contact angle is not necessarily accompa-nied by a higher actual persistence of SWR. This means that the CA isnot a reliable predictor of the persistence of SWR. Soils with a highdegree but short persistence of SWR can, for example, be causedby amphiphilic organic substances that change their configurationrapidly upon contact with water. At the meso-scale, SWR led to asignificant reduction of water infiltration in both treatments: alltension disc infiltrometer measurements indicated higher intrinsicinfiltration rates for ethanol than for water after 90 min. The esti-mated intrinsic permeabilities of ethanol were significantly higherthan those of water under both elevated (P = 0.015) and ambient(P < 0.001) atmospheric [CO2] (Fig. 4a). At the higher tension, theratio of intrinsic permeabilities was 6.2 under elevated and 10.6under ambient atmospheric [CO2], and at the lower tension, theratio was 1.8 and 2.8 for the elevated and ambient atmospheric[CO2], respectively. The differences between the ratio of intrinsicpermeabilities under elevated and ambient [CO2] were not signifi-cant (Fig. 4b).

3.1.1. Spatial distribution of the persistence and degree of SWRThe semivariograms for the CA and log(WDPT) were calculated

up to half the maximum length of each transect, viz. 2 m, to anal-yse the spatial pattern of SWR. The spatial correlation models wereadjusted to the empirical semivariances for both parameters of eachtreatment. They indicate short ranges (Fig. 5). Beyond a lag of 0.5 m,the semivariances fluctuated around the sill. Exponential modelsfitted the data best and explained about 80% of the variances found.

There was a clear spatial pattern for both repellency characteristics.The distance between the samples of 0.1 m, the minimal lag dis-tance, was short enough to derive the spatial dependencies of theSWR parameters CA and log(WDPT). The range a for the degree ofSWR under elevated [CO2] (0.29 ± 0.03 m) was about half the range

K. Müller et al. / Agriculture, Ecosystems an

Fig. 4. (a) Intrinsic permeabilities of ethanol and water measured at two tensions(water: −10 and −20 mm water; ethanol: −4 and −8 mm) under ambient andelevated atmospheric [CO2] (475 �L L−1), respectively, on 04 January 2008. (b) Cal-culated ratio of intrinsic permeabilities. Tension 1 was −10 and −8 mm, and tension2 was −20 and −8 mm for water and ethanol, respectively.

Fig. 5. (a) Exponential model fitted to the experimental semivariograms of the degree of son = 123) and ambient (n = 123) atmospheric [CO2]. (b) Exponential model fitted to the expfor the topsoil samples collected under elevated (n = 123) and ambient (n = 123) atmosph

d Environment 139 (2010) 98–109 103

found under ambient atmospheric conditions (0.57 ± 0.05 m). Thesame was true for the persistence of SWR (log(WDPT)), with a rangea of 0.27 ± 0.07 m for the elevated [CO2] compared with a range of0.50 ± 0.06 m for the ambient atmospheric conditions.

3.2. Impact of elevated atmospheric [CO2] on pasture growth

For both periods, 1999–2003 and 2003–2008, the differencesbetween the annual average total pastoral dry matter productionunder ambient and elevated [CO2] were not significant (Table 2).Of the four functional groups analyzed, C3 grasses, C4 grasses,dicots and legumes, only the average annual dry matter yield ofthe legumes was increased significantly under the elevated [CO2]during both time periods. For the dicots, this was only true duringthe early stages of the FACE experiment (Table 2).

3.3. Impact of elevated atmospheric [CO2] on MRR and SOM

The topsoil samples under ambient and elevated [CO2]had similar microbial respiration rates (6.08 ± 2.24 and6.12 ± 2.74 �g CO2 g−1 dry matter 24 h−1, respectively). TheSOM contents of the topsoil samples ranged between 7.9 and14.6%. The average SOM content was 10.8 ± 1.0 and 11 ± 1.2% forthe topsoil samples exposed to elevated and ambient atmospheric[CO2], respectively. The difference was not significant (Table 1).

3.4. Impact of SWR on carbon mineralization

The spatial structure of the log-transformed parameters CA,WDPT, SOM and MRR were analyzed for both treatments together.This procedure is justified, as the means were not significantly dif-ferent (Table 1). Increasing the number of observations for the twoSWR characteristics decreased the uncertainty of the range param-eter a expressed by its lower standard error (Fig. 6). The nuggeteffects Cn were 8 and 40% of the total variance for the degree andpersistence of SWR, respectively. This indicates either high short-scale variability and/or can be attributed to the measurement error.

The range of 0.46 and 0.36 m for the degree and persistence of SWR,respectively suggests a short spatial dependence of SWR. No spatialpattern was detected for the parameters log(SOM) and log(MRR)(Fig. 6). The transect length might have been too short for revealinga spatial pattern for these parameters.

il water repellency (CA) for the topsoil samples collected under elevated (475 �L L−1;erimental semivariograms of the persistence of soil water repellency (log(WDPT))

eric [CO2].

104 K. Müller et al. / Agriculture, Ecosystems and Environment 139 (2010) 98–109

Table 2Mean (±standard error) annual average dry matter production (total and by functional groups) from pasture at ambient or elevated atmospheric [CO2]. Data are for twoperiods, 1999–2003 and 2003–2008 inclusive. P values are from a repeated measures analysis of variance for the 5 years of data.

Period 1999–2003 2003–2008

[CO2] Ambient(g dry weight m−2)

Elevated(g dry weight m−2)

P Ambient(g dry weight m−2)

Elevated(g dry weight m−2)

P

Total 648 (±67) 712 (±125) 0.394 593 (±85) 674 (±49) 0.249C3 grasses 473 (±68) 473 (±105) 0.998 580 (±88) 684 (±44) 0.214Legumes 71 (±7) 126 (±7) 0.027 51 (±6) 92 (±5) 0.008Dicots 21 (±2) 43 (±2) 0.007 73 (±20) 74 (±4) 0.938C4 grasses 11 (±3) 11 (±1) 0.928 8 (±3) 4 (±1) 0.223

Fig. 6. Best model fitted to the experimental semivariograms of (a) the degree of soil water repellency (CA), (b) the persistence of soil water repellency (log(WDPT)), (c) themicrobial respiration rate (log(MRR)), and (d) the soil organic matter content (log(SOM)) for all topsoil samples (n = 246). In all cases, the exponential model fitted the databest, with the exception of the soil respiration rate, where the linear model gave the best fit.

Table 3Parameter values, their standard errors and the coefficient of determination (R2) of the multiple linear regressions between soil water content (WC) and values of water droppenetration time (log(WDPT)) and contact angles (CA) for all topsoil samples (n = 246).

Function for estimating WC Standard error for the values R2

Constant CA log(WDPT)

1.2842 − 0.0109 × CA 0.0813 0.0008 – 0.420.2239 − 0.0239 × log(WDPT) 0.0022 – 0.0011 0.61

0.5034 − 0.0028 × CA − 0.0204 × log(WDPT) 0.0840.3323 − 0.0011 × CAe − 0.023 × log(WDPTe) 0.09850.619 − 0.0041 × CAa − 0.0181 × log(WDPTa) 0.0161

0.0015 0.0009 0.670.0009 0.0017 0.730.0016 0.0029 0.56

K. Müller et al. / Agriculture, Ecosystems and Environment 139 (2010) 98–109 105

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ig. 7. Relationship between (a) persistence of soil water repellency (log(WDPT))nd SOM content (n = 246), (c) persistence of soil water repellency (log(WDPT)) andnd MRR (n = 246).

Correlation analysis revealed weak but highly significant rela-ionships between the SOM content and the degree or persistencef SWR (P < 0.001), as well as weak significant relationshipsetween the MRR and the degree (P = 0.043) or persistenceP < 0.001) of SWR. The linear regressions explained between 1 and4% of the observed variability of the SOM content and the MRRFig. 7).

.5. Impact of elevated atmospheric [CO2] on soil water content

The average gravimetric soil water contents were indistin-uishable between treatments (19.5 and 19% for elevated andmbient atmospheric [CO2]) and ranged between 10.5 and 29.5%.e found that under the given conditions the actual local top-

oil water content was directly dependent on the degree andersistence of SWR. Highly significant multiple linear regres-ions with log(WDPT) and the CA explained between 73 and6% of the variability of the measured soil moisture data for thelevated and the ambient atmospheric CO2 treatments, respec-ively (Fig. 8a). Taking all data together, 67% of the variability

f the soil water contents was explained by degree and persis-ence of SWR (Fig. 8b). All regressions were significant (P < 0.001)Table 3). The best fit for all measured water contents waschieved with degree and persistence of SWR as independentarameters.

il organic matter (SOM) content (n = 246), (b) degree of soil water repellency (CA)obial respiration rates (MRR) (n = 246), and (d) degree of soil water repellency (CA)

The concept of critical soil water content has been introducedby Dekker and Ritsema (1994) as a soil water content below whichthe soil is water repellent and above which a soil is wettable. Theconcept has been extended to the ‘critical soil moisture zone’ byDekker et al. (2001): Above a certain water content the soil is alwayswettable. The zone between the two threshold water contents, inwhich the soil can be wettable or water repellent, is the criticalsoil moisture zone. Following Täumer et al. (2005), we present thetwo thresholds for SWR in Fig. 9. All soil samples having water con-tents larger than 0.24 g g−1 were determined as wettable. Sampleswith water contents smaller than 0.15 g g−1 were water repellent(Fig. 9). The maximum moisture content at which extreme per-sistence of SWR (WDPT > 3600 s) was recorded was 0.24 g g−1. Thehighest water content recorded was 0.29 g g−1. Most of the water-repellent topsoil samples had water contents between 0.1 and0.2 g g−1. The threshold water contents were very similar, with 0.19and 0.24 g g−1 for samples under elevated and 0.15 and 0.23 g g−1

for samples under ambient atmospheric [CO2], respectively.

4. Discussion

The impact of the elevated [CO2] on SWR, carbon mineraliza-tion and total carbon was thought to be connected to a changedplant productivity, root growth and plant composition (Morgan et

106 K. Müller et al. / Agriculture, Ecosystems and Environment 139 (2010) 98–109

F undeo he tw(

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ig. 8. Measured and estimated soil water contents for all topsoil samples (collectedf the degree and persistence of soil water repellency. (a) Separate regressions for tn = 246). The solid line is the line of equal values.

l., 2004). No significant differences in the persistence and degreef SWR were found. Our results on the persistence of SWR con-rasted the measurements of a previous sampling in March 2003 athe same site, which had shown that SWR was significantly lessersistent in the soil under elevated than under ambient [CO2]Newton et al., 2003). Like Newton et al. (2003), we found a signif-cant difference in the distribution of soil samples in the soil water

epellency classes between ambient and elevated [CO2]. There wereome obvious differences between the two samplings conducted athe same experimental site: the duration of CO2 enrichment wasonger in our study than the previous study, being 10 years of CO2nrichment compared with 6 years. Furthermore, at our sampling

ig. 9. Relationship between the gravimetric soil water content and the persistencef soil water repellency of all topsoil samples (n = 246) collected on 8 March 2008.he transition zone for the critical water content ranges between 0.15 and 0.24 g g−1.

r the elevated and ambient [CO2] (n = 246). The soil water contents were a functiono treatments (n = 123); (b) all topsoil samples were pooled into a single regression

the soil water content was much higher because of a rainfall eventprior to our sampling, leading to a lower actual SWR compared withthe previously determined actual SWR. The above-ground plantyields were comparable at harvest on 18 March 2008, with 35.9and 32.8 g dm m−2 under ambient and elevated [CO2]. Similarly, thelong-term average annual dry matter yields were not significantlyinfluenced by elevated [CO2]. The proportion of legumes in theherbage at this particular sampling date was increased markedlybut non-significantly under elevated atmospheric [CO2] (Newton,unpublished data), while the long-term average annual legumeyield was significantly higher under elevated [CO2] than underambient conditions. The summer’s drought might have maskedabove-ground responses of legumes to elevated [CO2]. Newton etal. (2003) only determined the persistence of SWR. Therefore, thecomparison of previous measurements of SWR with our results issomewhat restricted. To the best of our knowledge, in no otherstudy the occurrence of SWR under elevated [CO2] has been ana-lyzed.

In accordance with the effect of elevated [CO2] on SWR, ele-vated [CO2] also had no significant impact on carbon mineralizationrates. Zak et al. (2000) reviewed the impact of elevated atmospheric[CO2] on soil microorganisms. While they reported that most stud-ies found trends of more rapid soil and microbial respiration, onlytwo of the 21 rates measured were significantly higher. Significantchanges in soil respiration rates have been observed by Patersonet al. (2008), Ross et al. (2004) and Sowerby et al. (2000). In aglasshouse study of 64 days, Paterson et al. (2008) found bothincreased SOM mineralization and increased plant root respirationrates. It is important, however, to note that other environmentalfactors potentially limiting plant and microbial activities, such astemperature and water availability, were optimized throughout thestudy. Similarly, in the long-term New Zealand FACE experiment,the previously reported significant differences in soil respiration

rates were confined to wet summer and autumns (Ross et al., 2004).Our mean microbial respiration rates were in the range of thosereported by Zak et al. (2000) for soils under different grass species.The relatively high sample variability, and non-significance of treat-ment differences found in the here reported carbon mineralization

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K. Müller et al. / Agriculture, Ecosyst

ates may be partly attributable to the uneven return of sheep dungnd urine to the grazed pasture (Aslam et al., 2009).

Similarly to the MRR, SOM contents were not significantlyffected by the elevated [CO2]. This is in accord with previous obser-ations for total carbon from this study site after 5 years of elevatedCO2] (Ross et al., 2004), and with most results from elevated [CO2]xperiments (Hungate et al., 1997; Gill et al., 2002). Jastrow et al.2005), however, challenged these former results by showing thathe general lack of significant changes in SOM contents observedan be explained by the low statistical power of most experimentspplying a meta-analysis technique. In our study, the increases initter/root production under elevated [CO2] were small compared

ith the total SOM pool of 10% already present in the topsoil. Wehus hypothesized that this also might have contributed to the lackf response of carbon mineralization rates under elevated [CO2].

SWR is a spatially very variable property (Shakesby et al., 2000).he within-site distribution of water repellent and wettable areass important for the occurrence of runoff, erosion and preferentialow in hydrophobic soils at the catchment scale. Water ponding onydrophobic soil surfaces may be laterally redistributed dependingn micro-topography. As a consequence of the water repellencytatus of the neighbouring surface, the water will infiltrate intohe soil matrix or run off the surface. Macropores can also pro-ide preferential pathways for water in hydrophobic soils (Dekkernd Ritsema, 1994). Measurements for SWR are usually made athe micro-scale with soil samples in the laboratory. These testso not provide insight into the distribution of hydrophobicity inhe field unless the soil samples are collected with a high spa-ial resolution (Ritsema and Dekker, 1998; Täumer et al., 2005;egalado and Ritter, 2008). We analyzed the distribution of thearameters of SWR at ambient and elevated [CO2] applying geo-tatistics. This revealed that persistence and degree of SWR wereot random but followed short-ranging patterns, which were simi-

ar under ambient and elevated [CO2], suggesting that the causes ofWR are also not randomly distributed. The range parameter a forxponential models is identical to the correlation length � (Mulland McBratney, 2002). The correlation lengths for both SWR char-cteristics were similar, indicating that they were interrelated. Thisas supported by the weak but significant linear correlation found

etween the two SWR parameters. Others have analysed the rela-ionship between WDPT and CA, with inconsistent results (King,981; Dekker and Ritsema, 1994; Lamparter et al., 2006; Regaladond Ritter, 2008). The non-linear behaviour of SWR with waterontent (King, 1981) might partly explain the different outcomes.he correlation lengths found for the two parameters of SWR wereuch shorter than those reported for other basic soil parameters

uch as organic matter content, pH and hydraulic properties (Mulland McBratney, 2002). In our study at the scale of the transects, nopatial pattern was detected for SOM contents and carbon mineral-zation rates. Furthermore, while the relationships between SOM,

RR and the parameters of SWR were significant, they were weaknd explained at most 14% of the observed variability of SOM con-ents and MRR. In contrast, a strong significant positive correlationetween SOM and SWR was found by Lamparter et al. (2009) andäumer et al. (2005). In both studies, the soil analysed was derivedrom a single site comparable to the study presented here. Ouresults challenge the hypothesis that a strong relationship betweenOM and SWR is typical for a small-scale investigation where sam-les are taken in close proximity (Doerr et al., 2006).

We deliberately determined the actual persistence of SWRhortly after a rainfall event because one objective of the study was

o examine the impact of SWR on the redistribution of soil water.n contrast to all other relationships that we analyzed, we found atrong impact of SWR on the local distribution of soil water con-ents. SWR was the driving factor of the soil water contents in theopsoil. The persistence and degree of SWR led to a mosaic of the

d Environment 139 (2010) 98–109 107

soil water distribution in the topsoil, with patches of low and highersoil water contents. Our results emphasize that SWR is a transientsoil property. If SWR were a long-term strategy for the conservationof SOM (Goebel et al., 2005), then the lower availability of water inthe patches with a higher SWR would have reduced soil respirationrates and would have had a positive feedback on carbon sequestra-tion. However, we did not find such correlations, thus we assumethat the spatial pattern of SWR is an ephemeral phenomenon. Thesubstances causing SWR seemed to be readily washed out dur-ing rain and then rebuilt in different patterns as a response to thenew local conditions. A true understanding of the biological andchemical factors of SWR is still missing. Future research shouldintegrate biodiversity aspects with a chemical characterization ofwater-repellent soil materials, vegetation patterns, and investigatefurther the temporal nature of the short-ranged spatial pattern ofSWR.

The only other studies on SWR employing a geostatistical anal-ysis are catchment studies (Regalado and Ritter, 2006, 2008) andthus, the soil samplings were conducted at a different scale fromthat in the present study. However, the authors also detected thatdegree and persistence of SWR followed a spatial structure. Therange was larger than 200 m (Regalado and Ritter, 2006). Flowand transport models valid for water-repellent soils require thiskind of spatial information on the occurrence of SWR (Deurer andBachmann, 2007).

Little is known about the thresholds of soil moisture, length ofdry periods necessary for the development of hydrophobicity andthe speed by which hydrophilic conditions develop in periods ofwet weather (Shakesby et al., 2000). Our experiments shed somelight on the persistence of SWR. Under both treatments, the per-sistence of SWR of the hydrophobic soil was rather low after thenatural rainfall event. In our study, the upper thresholds for theoccurrence of SWR were between 0.23 and 0.24 g g−1 and identi-cal to the threshold determined for a Typic Psammaquent with anorganic matter content of 18% in the Netherlands (Dekker et al.,2005). Täumer et al. (2005) found an upper threshold of 0.18 g g−1

for a Hortic Andosol on a former wastewater infiltration site nearBerlin, Germany. The transition zone of 0.09 g g−1 found in ourstudy is narrow compared with the transition zones reported inthe literature (Doerr and Thomas, 2003; Täumer et al., 2005). Inthe disc infiltrometer experiments, the persistence of SWR dimin-ished quickly, as reflected in the difference between the two ratiosof intrinsic permeabilities under high and low tension. It has beenshown that the actual wetting process of hydrophobic soils maytake between 2 and 3 weeks (Hurrass and Schaumann, 2007). Theseresults further emphasize that more knowledge is required aboutthe temporal and spatial occurrence of SWR, which might lead to abetter understanding of the mechanisms driving this important soildegradation process. We conclude that SWR does not contribute toincreasing the long-term terrestrial C sink in response to elevatedatmospheric [CO2], a feature of climate change.

Acknowledgements

We thank the Foundation for Research and Technology NewZealand (FRST) for the financial support of this project (SLURI, Cli-mate Change). The authors thank Michael Trolove for conductingthe respiration rate measurements. Tehseen Aslam and Eva Klin-gelmann helped with the soil sampling.

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