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Relationship between phosphorus concentration in surface runoff and a novel soil phosphorus test procedure (DGT) under simulated rainfall.

Introduction

Enrichment of surface waters with phosphorus (P) can contribute to increases in algal growth and, consequently, a decline in the environmental health and amenity of waterways. The concentration of P in runoff from grazing, cropping, and horticultural lands is typically higher than environmental guidelines for receiving waters. Soil P status is an important determinant of runoff P concentrations in grazing systems (Pote et al. 1999; McDowell et al. 2003).

Identifying soils likely to generate high concentrations of P in runoff is a precursor to implementation of risk-management strategies. Agronomic soil tests such as Colwell P are attractive for this purpose because of the copious quantity of 'free' data available. However, agronomic P measures are poorly related to runoff P concentrations across differing soils; hence, their utility as predictors of runoff P concentrations or risk is limited (Pote et al. 1999; McDowell et al. 2003; McDowell and Condron 2004). However, a measure of P buffering may complement agronomic soil tests to improve the prediction of runoff P, as suggested by McDowell and Condron (2004) and Moody (2011). This approach is conceptually reasonable given that for Australia's most commonly used agronomic test, Colwell P (Colwell 1963), the prediction of plant response to fertilisers is markedly improved by a measure of P buffering (Gourley et al. 2007; Moody 2007).

Recently, the novel technique of diffusive gradients in thin-films (DGT) has been used to assess P concentrations in water and lake sediment systems (Zhang et al. 1998; Monbet et al. 2008) and soil P available to plants (Menzies et al. 2005; McBeath et al. 2007; Mason et al. 2010). The DGT technique measures the diffusive supply of elements, in this case P, under infinite sink conditions. A DGT device is placed in contact with the soil, and P accumulates on a binding layer as a function of the concentration of P in the soil pore-water and the rate at which P is supplied from the soil solid phase into the pore-water. For moisture contents at or above field capacity, any potential effect of soil tortuosities are eliminated and DGT measurements reflect solution concentrations (Hooda et al. 1999). The presence of a diffusive layer between the sample and the sink limits the maximum flux of P, facilitates flux calculations, and protects the binding layer from contamination with particulate material (Zhang et al. 2001).

Monbet et al. (2008) found that the depth profiles of DRP in pore-water extracted by centrifugation closely matched those measured by DGT in two lake sediments from the Gippsland region of Australia. Mason et al. (2010) found that the DGT method assessed crop responsiveness to P applications across a wide range of different soil types with greater accuracy than other routinely used soil testing methods for P, such as Colwell P, both with and without adjustment for P buffering. Menzies et al. (2005) also highlighted the accuracy of DGT in identifying P-responsive soils. The reported improved performance of DGT over traditional soil testing measures in predicting P deficiency in cropping--soil systems has led to current research to assess its agronomic utility in pasture--soil systems.

Mobilisation of P into runoff in pasture systems is not by erosive processes but rather by desorption, dissolution, and diffusion (Nash et al. 2002)--processes that DGT purports to mimic as noted above. Consequently, we postulated that runoff P concentrations would be strongly related to soil DGT P. However, we are aware of no research that investigates the relationship between DGT P and runoff P concentrations in pasture systems.

Therefore, the objective of our research was to assess the merits of DGT, Colwell P, and a combination of Colwell P and P buffering index (PBI) for predicting the concentrations of P in runoff from established pastures using rainfall simulation.

Materials and methods

Soil collection and runoff tray establishment

Six soils with a diverse range of soil P buffering properties were collected from long-term pasture sites in New South Wales and South Australia (Table 1). Sufficient soil (~500 kg) was collected to allow for packing of 15 runoff trays for each site.

Soil P status in each of the trays within a soil type/site was varied by adding various rates of P in the form of finely ground (<0.5 mm) triple superphosphate. The objective of these additions was to achieve a range of soil test P values that varied 5-fold from the baseline values. The soil and fertiliser for each tray was mixed in a clean cement mixer before addition to the trays.

The runoff trays were constructed from plywood [1.0 m long, 0.2 m wide, 0.125 m high (sides and rear) and 0.1 m high at the downslope end to allow runoff to exit the tray] (Anon. 2005) and had six 0.5 cm holes drilled in the base to assist drainage during the equilibration period. The soils were packed to a depth of 0.1 m into runoff trays to achieve a bulk density of ~1.05 g/[cm.sup.3], which is typical of field pasture topsoils. The soils were subsequently sown with perennial ryegrass (Lolium perenne L.) at a rate of 25 kg/ha and fertilised with basal potassium and sulfur and grown in a shade house in the field. After 9 months, the rainfall simulations were undertaken as outlined below.

Rainfall simulation and runoff analysis

The trays were placed at a slope of 5% then subjected to rainfall simulation at 45 mm/h (coefficient of uniformity 0.95; data not presented) using a Tee Jet 3/8HH SS 24 WSQ (Spraying Systems Co., Wheaton, IL, USA) (Humphry et al. 2002). Rainfall simulation was continued for 30min after runoff commenced. Time to runoff (min) and total runoff depth (mm) was recorded for each tray. A single, composite runoff sample was collected and a subsample immediately filtered using a <0.45-[micro]m filter. The unfiltered and filtered samples were analysed for total P (TP) and total dissolved P (TDP), respectively, using an acidic persulfate digest (APHA 1995) and subsequent determination of P by colourimetry (Murphy and Riley 1962).

Soil sampling and analysis

For each tray, 20 soil cores (0-10 mm deep and 20 mm diam.) were collected and composited. The shallow sampling depths were used, as it is only the immediate topsoil (<10mm) that interacts with runoff (Ahuja and Lehman 1983; Sharpley 1985). Soil samples were air-dried at 40[degrees]C, ground to pass a 2-mm sieve, and stored at ~4[degrees]C before analysis.

Soil pH in 10mM Ca[Cl.sub.2], electrical conductivity, organic carbon, exchangeable cations (sum = effective cation exchange capacity), oxalate-extractable iron and aluminium, Colwell P, and unadjusted (without the Colwell P term included in the equation) P buffering index (PBI) (Burkitt et al. 2008) were determined as described in Rayment and Lyons (2010) (Table 1).

A binding layer containing ferrihydrite (Zhang et al. 2001) was used for the DGT test. The gel solution used for making diffusive gels, as well as for the mixed binding layer, was composed of 15% v/v acrylamide (Boehringer Ingelheim, Germany) and 0.3% v/v agarose-derived cross-linker (DGT Research Ltd, UK). Both the diffusive and binding gels were cast according to published procedures (Zhang and Davison 1995). Plastic DGT devices designed for soil deployment comprised a backing plate and a front plate with an exposure window (area 2.52 [cm.sup.2]). Diffusive gels with a thickness of 0.6 mm were incorporated on top of the ferrihydrite layer. A 0.13-mm-thick, mixed cellulose ester filter (0.45 [micro]m) was placed on top of the diffusive gel for protection. Soil samples (20 g) were adjusted to ~50% water-holding capacity (WHC) via addition of ultra-pure water (18.2 M[OMEGA]/cm) at 72h before DGT deployment. At 24h before scheduled DGT deployment, soil moisture was adjusted further to maximum WHC as assessed by visual inspection (glistening of water on the soil surface). The DGT devices were deployed the next day at ~22[degrees]C, and after 24 h the DGT assemblies were removed and rinsed with ultra-pure water to remove any adhering soil particles. For some soil samples, their high soil P status resulted in saturation of the ferrihydrite layer within the 24-h deployment, based on the P-binding capacity of the ferrihydrite as previously reported (Zhang et al. 1998; Mason et al. 2005). These samples were re-analysed using a shorter deployment time (6 h). The DGT method controls the flux of P onto the binding layer through use of a diffusive layer, and therefore the accumulation of P on the gel is linear with time, assuming the soil P pool is not significantly depleted (Zhang et al. 1998). Based on the relatively high available P in the relevant soils and resultant saturation of the gels, it is a reasonable assumption that the soil-solution P pool would not have been significantly depleted. Consequently, it can be assumed that the 6-h DGT values would be comparable to the 24-h values. Furthermore, the duration of deployment is accounted for in the DGT calculation. After dismantling the DGT units, the ferrihydrite gel was retrieved and placed in 1 mL of 1 M HCl, left for 24-h, and subsequently diluted to 5 mL for analysis.

Ammonium molybdate reactive P concentrations in the eluted solution were determined by employing a modified version of the Murphy and Riley (1962) method using a continuous flow analyser (ChemLab, England). The reaction time was ~135s, with final concentrations of 0.6g/L of ammonium molybdate, l g/L of ascorbic acid, 0.05g/L of potassium antimony tartrate, and 0.125M [H.sub.2]S[O.sub.4]. The absorbance of the phosphate complex was measured at 880 nm. Calibration standards were prepared in 0.2 M HCl.

The P concentration in the eluted solution was converted to a DGT measured concentration (CDGT)--hereafter referred to as DGT P--as described by Zhang and Davison (1995) where CDGT is the time-averaged concentration of P in soil solution at the surface of the DGT device.

Statistical analysis

The relationships between the variables 'soil', DGT P, Colwell P, PBI, and runoff P were examined by multiple linear regression using GENSTAT V. 12 (Lawes Agricultural Institute, Rothamsted, UK). Data were transformed (square root transformation) to normalise variance for examination of the TP-DGT relationships, whereas data were [log.sub.10]-transformed for examination of the TP-Colwell P/PBI relationships.

Results and discussion

The addition of P fertiliser to the soils resulted in a wide range of Colwell P and DGT P concentrations for the soils (Fig. 1). These concentrations encompass a broad range of values, including those commonly found in both extensive and intensive grazing systems (Gourley et al. 2010; Hart and Cornish 2010). There was a highly significant relationship (P < 0.001, [r.sup.2] = 93) between P added and Colwell P, with no significant effect of 'soil' or PBI (P> 0.05). In contrast, there was a highly significant (P< 0.01) effect of PBI on the relationship between P added and DGT P. These findings reflect the propensity of the Colwell P method to reflect the quantity component of soil P (Kuo 1996), whereas the DGT method reflects the solution and diffusion components of P resupply from the solid phase of the soil (Hooda et al. 1999; Degryse et al. 2009).

[FIGURE 1 OMITTED]

The rainfall simulations were undertaken when pasture biomass was large and groundcover was high (>90%). There was no effect (P> 0.05) of soil test P on pasture biomass. This reflects the fact that minimum soil test P concentrations for all soils were either at, or close to, the agronomic optimum. The average time to initiation of runoff was 19 min (range 15-24 min) and average runoff was 15.4 mm (range 11-23 mm). The effects of these hydrological parameters on the following soil P-runoff P relationships were examined and in all cases were not significant (P> 0.05) and, consequently, are not discussed further.

Runoff P (TP) concentrations varied widely from 0.03 to 9.4 mg/L. The P contained in runoff was predominantly (>85%) dissolved (<0.45 [micro]m) and the levels of suspended sediment were low; these characteristics are consistent with P mobilisation via a process of dissolution as distinct from erosion. This dominance of dissolved P is common in intensive pasture systems and in other systems when ground cover is high (Nash et al. 2000; McDowell et al. 2007).

There was a highly significant (P< 0.001, [r.sup.2] = 0.84) linear relationship between soil DGT P and runoff P concentrations (Fig. 2) for all soils combined, with no significant (P>0.05) effect of 'soil' or PBI. This [r.sup.2] value is similar to those reported elsewhere in the literature for the relationship between other measures of soil test P and runoff P (Pote et al. 1999; McDowell and Condron 2004; Vadas et al. 2005; Robertson and Nash 2008). For example, McDowell and Condron (2004) found a strong relationship (P< 0.001, [r.sup.2] = 0.92) between water-soluble soil P and runoff P across a series of soils, while Robertson and Nash (2008) found typical correlation coefficients of ~0.6 on a single soil type. The strength of the relationship between DGT P and runoff P that we found--both in absolute terms and relative to other available soil P measures reported in the literature--highlights the potential utility of DGT as an indicator of potential runoff P concentrations.

[FIGURE 2 OMITTED]

Although we found a significant curvilinear relationship between Colwell P and runoff P concentration for individual soils (P<0.05), there were large and highly significant differences in the relations (P<0.01) between soils (Fig. 3). The Robertson soil (with the highest PBI) had very low runoff P concentrations, whereas the Richmond soil (with the lowest PBI) had very high runoff P concentrations. This variation in relationships between soils when using agronomic measures such as Colwell P is consistent with the observations of other researchers (e.g. McDowell et al. 2003; Zhang et al. 2006). Like others (McDowell and Condron 2004; Moody 2011), we postulate that these differences in soil P runoff P relationships might be explained by P buffering properties. Indeed, we confirmed that the differences in our Colwell P-runoff P relationships were explained by the effect of soil P buffering, as evidenced by the following highly significant model (P< 0.001, [r.sup.2] = 0.82):

[Log.sub.10]TP = -2.5([+ or -]0.2) + [1.6([+ or -]0.1)[log.sub.10]Colwell P] -[0.3([+ or -]0.02)[log.sub.10]Colwell P x [log.sub.10]PBI]

where [+ or -] values in parentheses are the standard errors of the terms.

[FIGURE 3 OMITTED]

This model is somewhat more complex than a simple linear relationship with DGT, or indeed the models proposed by McDowell and Condron (2004) and Moody (2011). Nonetheless, Australian laboratories have existing investments in performing PBI and Colwell P tests on soils to predict fertiliser P requirements, and this relationship could be used to provide a measure of potential relative runoff P concentration that could be incorporated into interpretations of soil tests for pasture systems.

Two factors contribute to the predictions of runoff P from either DGT or the combination of Colwell P and PBI being 'relative' only, and not absolute. First, the concentration of soil P at 0-10 mm (the depth we analysed) in pasture soils is greater (typically up to 2x) (Hart and Cornish 2010) than that at 0-100mm (the agronomic sampling depth) (Dougherty et al. 2006; Mathers and N ash 2009; Hart and Cornish 2010) although the values are highly correlated (Hart and Cornish 2010). Second, runoff P concentrations depend on factors such as rainfall intensity, path lengths, and site conditions such that rainfall simulation data and subsequently derived relationships do not necessarily predict absolute runoff P concentration at the field scale (Nash et al. 2002; Kleinman et al. 2006; Dougherty et al. 2008). Nevertheless, we propose that a variation of the above model for Colwell P and PB1 or the DGT relationships we present in which 0-100mm soil P values replace those of 0-10mm will still provide informative estimates of relative runoff P concentrations.

Conclusions

We found highly significant relationships between DGT and runoff P and a combination of Colwell P-PBI and runoff P. Because runoff P concentrations depend on factors such as rainfall intensity, path lengths, and site conditions and because our relationships are based on analysis of 0-10 mm soil samples (which is not routinely undertaken), the relationships we present should not be used to predict absolute concentrations of P in runoff at field scales. Nevertheless, we conclude that either DGT, or Colwell P and PBI in combination, can be used to provide a relative measure of runoff P concentration and that this could be incorporated into interpretations of soil tests for pasture systems.

Acknowledgments

The authors acknowledge Dairy Australia who funded this study under project number DAN 12752 along with the support of Grains Research and Development Corporation under project number UA00103.

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Manuscript received 13 March 2011, accepted 27 July 2011

W. J. Dougherty (A,E), S. D. Mason (B), L. L. Burkitt (C), and P. J. Milham (A,D)

(A) Science and Research, NSW Department of Primary Industries, Locked Bag 4, Richmond, NSW 2753, Australia.

(B) School of Agriculture, Food and Wine, University of Adelaide, SA 5005, Australia.

(C) Tasmanian Institute of Agricultural Research, University of Tasmania, PO Box 3523, Burnie, Tas. 7320, Australia.

(D) Centre for Plant and Food Science, University of Western Sydney, LB 1797, Penrith South DC, NSW 1797, Australia.

(E) Corresponding author. Email: warwick.dougherty@industry.nsw.gov.au

10.1071/SR11151 1838-675X/11/060523
Table 1. Selected soil chemical properties (0-100 mm)

[pH.sub.Ca], Soil pH in 10 mM Ca[Cl.sub.2]; EC, electrical
conductivity; PBI, phosphorus buffering index; [Fe.sub.ox],
[Al.sub.ox], oxalate-extractable iron and aluminium; ECEC effective
cation exchange capacity. Analyses were performed following the
methods described by Rayment and Lyons (2010)

Soil ID Soil type [pH.sub.Ca] EC PBI
 (Isbell 1997) (dS/m)

Camden Brown Chromosol 5.4 0.14 87
Flaxley Brown Chromosol 5.5 0.22 110
Glenmore Red Chromosol 4.7 0.18 180
Moss Vale Brown Kurosol 4.3 0.06 540
Richmond Red Kandosol 4.5 0.02 22
Robertson Red Ferrosol 4.2 0.11 1200

Soil ID Colwell P [Fe.sub.ox] [Al.sub.ox] ECEC
 (mg/kg) (cmol/kg)

Camden 41 3261 1305 12
Flaxley 49 3834 2518 12
Glenmore 55 5143 2891 11
Moss Vale 19 4391 4263 6.1
Richmond 52 974 1089 1.7
Robertson 34 11430 5956 6.7

Soil ID OC Particle size analysis (%)
 (%) Silt Clay Sand

Camden 3.8 17 31 53
Flaxley 5.5 19 29 52
Glenmore 2.8 34 43 23
Moss Vale 2.5 30 43 27
Richmond 3.7 5 10 85
Robertson 7.0 19 54 27
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Date:Sep 1, 2011
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