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Published online 3 April 2006
Published in J Environ Qual 35:806-814 (2006)
DOI: 10.2134/jeq2005.0178
© 2006 American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America
677 S. Segoe Rd., Madison, WI 53711 USA
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TECHNICAL REPORTS

Surface Water Quality

Determining Phosphorus Release Rates to Runoff from Selected Alberta Soils Using Laboratory Rainfall Simulation

Charles R. Wrighta,*, Mohamed Amranib, Muhammad A. Akbara, Danial J. Heaneyc and Douwe S. Vanderweld

a Alberta Agriculture, Food and Rural Development, 206 J.G. O'Donoghue 7000-113th Street, Edmonton, AB, Canada T6H 5T6
b Environment Canada, 105 McGill Street, 7th Floor, Montreal, QC, Canada H2Y 2E7
c Norwest Labs, Edmonton, AB, Canada
d City of Edmonton, Edmonton, AB, Canada

* Corresponding author (Ralph.Wright{at}gov.ab.ca)

Received for publication May 9, 2005.

   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 MATERIALS AND METHODS
 RESULTS AND DISCUSSION
 CONCLUSIONS
 REFERENCES
 
Phosphorus losses from agricultural land can cause accelerated eutrophication of surface water bodies. This study evaluated the use of soil test phosphorus (STP) levels to predict dissolved inorganic phosphorus (DIP) concentrations in runoff water from agricultural soils using laboratory rainfall simulation. The objectives of this study were to determine (i) to what extent STP concentrations can be used as a basis to predict P losses from Alberta soils and (ii) how extended rainfall simulation run times affected DIP losses. Soil samples collected from a total of 38 field sites, widely scattered throughout the southern half of Alberta, were subjected to rainfall simulation in the laboratory. The STP concentrations were determined using Miller–Axley, Norwest, Kelowna, Modified Kelowna Mehlich-III, and distilled water extraction methods. Each rainfall simulation event lasted for at least 90 min. Runoff samples were collected in time series for the duration of each simulation, during two distinct runoff intervals: (i) for the first 30 min of continuous runoff (T30) and (ii) for 40 min during runoff equilibrium (Teq). For all the STP extractants and both runoff intervals, the relationship with DIP–flow-weighted mean concentration (FWMC) was linear and highly significant with r2 values ranging from 0.74 to 0.96. However, the slopes of the resulting regression lines were, on average, 1.85 times greater for the T30 runoff interval over those computed for the Teq interval. Thus experimental methodology greatly influenced regression parameters, suggesting that more work was needed to verify these relationships under natural conditions. In addition, with many of the r2 values greater than 0.90 there would be little, if any, benefit derived by including soil properties in regression analysis.

Abbreviations: DIP, dissolved inorganic phosphorus • DW, distilled water • FWMC, flow-weighted mean concentration • STP, soil test phosphorus


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
MATERIALS AND METHODS
 RESULTS AND DISCUSSION
 CONCLUSIONS
 REFERENCES
 
A PRELIMINARY STUDY of agricultural impacts on water quality in Alberta has shown that in areas of low to high agricultural intensity, total phosphorus (TP) concentrations in streams and lakes often exceeded provincial water quality guidelines (0.05 mg TP kg–1) for the protection of aquatic life (Canada-Alberta Environmentally Sustainable Agriculture, 1998). In addition, dissolved inorganic phosphorus (DIP) was found to be the major component of TP in streams draining agricultural lands (Canada-Alberta Environmentally Sustainable Agriculture, 1998). Since DIP is readily available for biological uptake, it poses an immediate threat for accelerated algal growth that may result in deterioration of water quality in lakes and streams (Sharpley and Smith, 1989). Complex models have been used to quantify phosphorus (P) losses in runoff from agricultural lands (Beasley et al., 1985; Young et al., 1989; Sharpley and Williams, 1990) and by regressions between DIP in runoff and various soil P tests (Romkens and Nelson, 1974; Sims et al., 2000; Hansen et al., 2002). These models typically predict dissolved P in runoff with an extraction coefficient relating STP to runoff DIP. Several field and laboratory studies have also shown a strong relationship between some measure of STP and DIP in runoff water from manured and/or fertilized land (Romkens and Nelson, 1974; Sharpley, 1995; Pote et al., 1996; Cox and Hendricks, 2000; Sims et al., 2000; McDowell and Sharpley, 2001; Torbert et al., 2002; Fang et al., 2002; Daverede et al., 2003; Andraski and Bundy, 2003). However, for an effective design, use, and/or adaptation of these models, the relationship between STP and DIP concentration in runoff water needs to be adequately understood and quantified for local soils.

There are many factors that have been reported to affect the strength of the relationship between STP and DIP. In a field rainfall simulation study on three soils, Cox and Hendricks (2000) reported a direct relationship between STP (Mehlich-III) and DIP in runoff with r2 values ranging from 0.62 to greater than 0.90, but this relationship was affected by clay content. Torbert et al. (2002) conducted rainfall simulations on field plots and showed a linear relationship between STP and DIP with r2 values ranging from 0.75 to 0.96. However, these researchers contended that STP–DIP relationships were soil specific and should be used with soils of similar characteristics grouped together. In a field study Pote et al. (1999) found that soils with the most variable runoff yielded the weakest relationships between water-extractable STP and DIP in runoff. In addition, they found highly significant linear relationships (r2 values ranging from 0.22 to 0.85) between STP concentrations derived from six different extraction methods and P concentration in runoff. Of these STP methods, the distilled water, acidified ammonium oxalate, and FeO paper methods were better correlated to runoff P concentration than others. However, Vadas et al. (2005) concluded that soil type might not be a major factor that affects the relationship between DIP and STP, since STP methods were inherently sensitive to soil physical and chemical properties with respect to their effects on P release.

Given the above discussion, it appears that to quantify how STP relates to DIP in runoff, and which STP method provides the best relationship with DIP, the soil medium needs to be isolated and tested free from the hydrological complexities and other potential P sources present in field experiments. These results could then be integrated into a model that incorporates the effects of landscape and management in addition to other potential sources for P (e.g., vegetation, fertilizer, fresh manure, etc.). Currently, a number of routine agronomic soil P tests are being used to make phosphorus fertilizer recommendations in Alberta (McKenzie et al., 1995). These tests include the Miller–Axley (Miller and Axley, 1956), Kelowna (Van Lierop, 1988), Modified Kelowna (Qian et al., 1991), Norwest (Ashworth and Mrazek, 1989), and Mehlich-III (Mehlich, 1984). However, studies investigating the effect of the soil medium alone, on P release to runoff using either agronomic or environmentally oriented soil tests, have not previously been conducted in Alberta. Therefore, the objectives of this study were to determine (i) how well STP concentrations can be used as a basis to predict P losses from Alberta soils and (ii) how extended rainfall simulation run times affected DIP concentrations.


   MATERIALS AND METHODS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
RESULTS AND DISCUSSION
 CONCLUSIONS
 REFERENCES
 
Soil Collection
Soil samples were collected from 38 sites widely scattered throughout the southern half of Alberta. While the majority of soils at these sites were Chernozems, soil samples also included Luvisols, Solonetzs, Vertisols, and Gleysols (Table 1). Samples were collected post-harvest (in the fall) of 1998 and 1999 from farmer's fields and also from research sites that were used to test the effects of manure additions for various other purposes. At the research sites, soils had received beef or hog manure for periods ranging from 3 to 5 yr, each with a control treatment that had received no manure. Manure histories from the farmer's fields were less well known regarding application rates, but the type of manure applied was known and ranged from no manure to beef manure exclusively, or hog manure exclusively. To ensure that soils and manure had sufficient time to interact, thereby reducing the effects of fresh manure applications on the experimental results, none of the soils had received manure applications within 6 mo of sampling. Before sampling, crop stubble and residue were removed from the sampling area. Soil samples of approximately 200 kg were collected from the top 10 cm of the soil surface using a shovel and a 20-L pail. Each soil sample was well mixed in the field at its existing moisture content and placed in 200-L plastic barrels. Each barrel was sealed with waterproof, breathable covers for transport and storage. These barrels were stored outside on the north side of the laboratory through the winter (under sub-zero temperatures) and early spring before conducting experiments.


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  		Table 1. Soil classification for sampling sites.

 
Rainfall Simulator
Laboratory rainfall simulations were chosen to achieve a greater degree of control over hydrological and soil surface conditions and to minimize the effects of other variables on P release to runoff. The laboratory rainfall simulator consisted of duplicate paired chambers, each utilizing a Fulljet 1/2-50WSQ nozzle (Spraying Systems, Wheaton, IL) operating at 2.9 m above the target area to generate simulated rainfall at a target intensity of 75 mm h–1 (National Phosphorus Research Project, 2005). However, calibrations of the nozzles revealed average rainfall intestines of about 65 mm h–1. Within each chamber, two rain-frames were situated below the nozzle on adjustable slope tables permitting up to four prepared soils to be rained on simultaneously. Stainless steel rain-frames (0.95 m long by 0.50 m wide) were custom-built to hold a 10-cm layer of soil. Each rain-frame was used as a single replication and soils were rained on in triplicate. The frames were constructed with two separate compartments; an upper compartment containing the soil and a lower compartment to allow pre-wetting of the soil samples from below through capillary rise and to permit drainage during rainfall simulation. The lower compartment was separated from the upper compartment with a plastic bottom with 10% of its area perforated with regular spaced 25-mm holes and overlain by an inert geo-textile.

Soil Handling and Runoff Sampling
Before conducting rainfall simulations, each soil sample was passed through a screen with a 10-mm mesh sieve at field moisture content. Before placing soil samples in rain-frames, representative subsamples were taken to determine STP concentrations. Subsamples were also analyzed for soil texture (Gee and Bauder, 1986), organic matter content (Schnitzer, 1982), pH (McLean, 1982), and electrical conductivity (Rhoades, 1982). After placing the soils in the rain-frames, the soil samples were pre-wet for approximately 18 h from below through capillary rise, by establishing a 1-cm water table at the bottom of the 10-cm-deep soil sample. Pre-wetting of the soils was intended to reduce runoff variability due to inherent differences in antecedent moisture conditions, and also to establish a consistent soil water content for comparing soils and standardizing the experimental protocol (Pote et al., 1996). Before simulation, excess pre-wet water was gravity-drained from the rain-frames for 60 to 90 min. Both the pre-wet and the rainfall simulation source water originated from City of Edmonton tap water that was passed through a commercial water softener employing an ion exchange resin, with KCl salts used as the exchange salt. During and after each simulation, samples of tap water were analyzed for DIP to ensure that background levels of DIP in the source water were far less than those measured from the soils. All soils were rained on in triplicate at a slope of 7%, with final values of runoff and DIP reported as averages.

Each rainfall simulation event was conducted for a minimum duration of 90 min. This duration was selected to investigate the effects of extended simulation run times on runoff DIP concentrations. The extended run time yielded two distinct runoff intervals, the first 30 min of runoff (T30) (National Phosphorus Research Project, 2005) and the second interval at apparent runoff equilibrium starting approximately 10 min after the end of the T30 interval during which time runoff rates were near equilibrium (Teq). In Teq three consecutive samples, each 20 min apart, were drawn. Most of the rainfall events lasted for about 90 min. All runoff samples were collected in acid washed and triple rinsed glass containers. During the course of the study, three sampling schemes were employed:

  1. Runoff samples for 13 of the soils were taken starting at 0, 10, 20, and 30 min from the initiation of runoff, and thereafter, 20 min apart at 50, 70, and 90 min from the beginning of simulation (not from the start of runoff).
  2. Runoff samples for 10 of the soils were taken starting at 0, 6, 12, 20, and 30 min from the initiation of runoff, and thereafter, 20 min apart at 50, 70, and 90 min from the beginning of simulation (not from the start of runoff).
  3. Runoff samples for 15 of the soils were collected in total, for each 30-min interval (i.e., 0–30, 30–60, and 60–90 min from start of runoff) in separate 23-L glass bulk containers. A 1-L aliquot drawn from each bulk container was used to determine DIP–flow-weighted mean concentration (FWMC) for each 30-min interval. The aliquot was drawn using a vacuum pump while the bulk sample was being mixed on a large spring shaker.

For Schemes 1 and 2, a minimum sampling duration was set such that at least 250 mL of runoff was collected. However, after several (13) simulations under Scheme 1, it became evident that flow rates were highly variable during the first 30 min of runoff, but generally tended toward an apparent equilibrium after about 40 min. Therefore, to better characterize the initial 30-min period of surface runoff, Sampling Scheme 2 was introduced that included an additional sample within the first 30 min. For both Schemes 1 and 2, the sample timing during the equilibrium phase was adjusted so that samples were taken since the start of simulation, rather than the start of runoff. This simplified sample timing and permitted samples to be taken simultaneously across all four soil frames for 1 min. Samples from each frame were then placed side by side and the water level in each sample was visually compared to see if similar runoff volumes had been collected. If three consecutive samples had similar water levels, runoff equilibrium was deemed to have occurred and the simulation ended. Under Scheme 3, runoff equilibrium was determined by placing a balance under the large (23 L) glass container and on the fly measurements of total weight vs. time were plotted until subsequent observations yielded little or no change in runoff rates.

For each runoff interval, under Schemes 1 and 2, runoff volumes and DIP flux (mg h–1 m–2) rates were calculated based on the individual sample points. Total runoff volume for each sample was computed by subtracting the sediment load from the total sample mass. The DIP–FWMC for each runoff interval (T30 and Teq) was computed as the total DIP flux divided by the runoff volume corresponding to that runoff interval. The DIP concentrations and runoff flow rates determined from each sample were assumed to represent the sample interval midpoints. A Visual Basic program was written to calculate DIP mass, runoff volumes, and DIP–FWMC for each runoff interval by summing the areas under the curves (sample intervals) between successive sample midpoints. Mass of DIP and runoff volumes for each of the runoff intervals, T30 and Teq, were calculated as follows:

Formula 1[1]
where Fx is the DIP (mg L–1) or runoff volume, L; c0 is the concentration of the first sample, mg L–1; v0 is the volume of the first sample, L; i is the sequential sample number; ci is the analyte concentration, mg L–1; Qi is the flow rate, L min–1; ti is the sample time, min; and n is the number of samples. Note that when calculating runoff volume, c0 and ci evaluate to 0.

While calculating DIP mass and runoff volumes for the first 30 min of continuous runoff (runoff interval T30) using Eq. [1], special consideration was given to the first and second sample intervals. The mass of DIP collected during the first sample interval (c0 x v0) was equal to the DIP flux for the first sample. Using this value as a midpoint within the summation term in Eq. [1] for calculating the second sample interval would constitute an error. Therefore, flux values for the second sample interval, c1 and Q1, were estimated for the end point of the first sample. This was done by assuming that the shape of the curve between time at the start of rainfall simulation (T0), and the end point of the first sample, was a right angle triangle where:

Formula 2[2]

In this case, Mx was the DIP mass and t was the sample duration. The mass at the endpoint for the first sample was then estimated by solving for c1. The estimated endpoint, c1, was then used to calculate the area under the curve between the end of the first sample and the second sample midpoint. The runoff rate Q1 for the end point of the first sample was estimated in a similar fashion.

After 23 simulations had been performed, it was evident that runoff volume and runoff DIP concentration curves were similar in form for most soils. Therefore, to reduce the number of runoff samples and to save on sample analysis, Sampling Scheme 3 was devised. Before adopting this strategy additional, concurrent tests on the same soils using both the second and third sampling schemes confirmed that both methods produced similar results for DIP–FWMC over each interval. Before subsampling, the contents of the bulk container were weighed to determine the mass of sediment + runoff water. Total sediment load in the bulk container was then computed based on sediment concentrations in the subsamples with back calculations used to estimate runoff water volume in the bulk container.

Determination of Phosphorus and Sediment in Runoff
Samples were transported to the laboratory in coolers packed with ice and filtered within 24 h (in most cases, within 6 h). A 50-mL aliquot of runoff sample was filtered using Whatman (Brentford, UK) no. 42. Then, the filtrate was put on a vacuum filtration system using a 0.45-µm filter paper and then transferred into a clean dry test tube or flask. The P concentrations were then determined using an automated ascorbic acid method (Method 365.1; USEPA, 1993). Sediment mass was determined by filtering the remaining runoff sample through a Whatman no. 4 filter paper, drying the paper with sediment at 105°C for 24 h, and subtracting the filter paper tare weight from the total dry weight. Sediment concentration was determined as the sediment mass divided by the water volume.

Soil Test Phosphorus
Soil test P was determined using six methods: distilled water (DW) (Pote et al., 1996); Miller–Axley (0.03 M NH4F + 0.015 M H2SO4) (Miller and Axley, 1956); Norwest (0.015 M NH4F, 1.0 M HOAc, 0.5 M NH4OAc) (Ashworth and Mrazek, 1989); Kelowna (0.015 M NH4F, 0.25 M HOAc) (Van Lierop, 1988); Modified Kelowna (0.015 M NH4F, 0.25 M NH4OAc, 0.25 M HOAc) (Qian et al., 1991); and Mehlich-III (0.2 M CH3COOH, 0.25 M NH4NO3, 0.015 M NH4F, 0.013 M HNO3, 0.001 M EDTA) (Mehlich, 1984).

All extracts were analyzed for P colorimetrically by the molybdate blue method of Murphy and Riley (1962).

Statistical Analysis
Statistical analysis was performed using SYSTAT 10 (SPSS, 2000) and Excel (Microsoft Corporation, 2005). For the two runoff intervals (i.e., T30, and Teq), DIP–FWMC in runoff was compared with STP concentrations determined from the six soil P test methods. Linear regression was performed to characterize DIP–FWMC in runoff vs. STP values. The strength of the DIP–FWMC vs. STP relationships was evaluated on the basis of the adjusted coefficient of determination (r2) (Freund and Littell, 1986). In addition, slopes of the linear regressions were compared by constructing 95% confidence intervals (CI) for the slope parameters (Neter et al., 1990). When CI values for slope parameters for any two or more regressions overlapped completely, the slopes were considered to be not significantly different. In all statistical analyses, a probability level of 0.05 (P < 0.05) was used.


   RESULTS AND DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS AND DISCUSSION
CONCLUSIONS
 REFERENCES
 
Soils
This study used soils representing a wide range of soil types with varied physical and chemical characteristics and manure application histories. In total, 66% of the samples were Chernozems, 11% Luvisols, 19% Solonetzs, 3% Vertisols, and 3% Gleysols (Table 1). Across these soils, sand content ranged from 12 to 63%, silt from 14 to 52%, clay from 14 to 74%, organic matter from 3.6 to 16.7%, pH from 5.2 to 8.0, and electrical conductivity from 0.2 to 2.9 dS m–1, and manure histories included no manure, beef manure, and hog manure (Table 2). Across this group, STP ranged widely with DW ranging from 1 to 121 mg kg–1, Miller–Axley from 8 to 386 mg kg–1, Norwest from 8 to 600 mg kg–1, Modified Kelowna from 10 to 495 mg kg–1, Kelowna from 12 to 697 mg kg–1, and Mehlich-III from 13 to 854 mg kg–1 (Table 3).


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  		Table 2. Physical and chemical properties of soils used for laboratory rainfall simulations.

 

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  		Table 3. Soil phosphorus as determined by six soil test phosphorus (STP) extraction methods.

 
Runoff Hydrographs and Dissolved Inorganic Phosphorus Concentrations
For illustrating the general characteristics of runoff and DIP concentration time series, four soils were selected for developing the plots shown in Fig. 1. These soils (4, 9, 19, and 22) were chosen because they were both representative of the population of soils tested and were also relatively well contrasted, both physically and chemically. For these soils, sand content ranged from 18 to 46%, organic matter from 7.5 to 13.5%, pH from 5.3 to 7.3, and Miller–Axley STP from 40 to 226 mg kg–1.


Figure 1
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  		Fig. 1. Runoff flow rates with time for Soils 4, 9, 19, and 22.

 
On average, runoff started between 2 and 3 min after the simulation commenced, showed a rapid increase in flow rates, and in most cases, achieved near equilibrium after approximately 40 min (Fig. 1). Before the simulation, the soil surface was well structured with many loose, discrete, readily visible aggregates. By the time runoff equilibrium had been reached (40 minutes), the soil surface had radically changed and become uniformly covered with sediments, with all discrete aggregates either washed away or reduced by raindrop impact. Thus it was believed that the largest factor that reduced infiltration rates was filling of soil macropores between loose aggregates. Pote et al. (1996) also noted similar runoff responses.

During the first 30 min (T30) of continuous runoff, flow rates ranged from 7.7 to 59.8 mm h–1 with an average of 39.0 mm h–1. At equilibrium (Teq), runoff flow rates ranged from 32.2 to 66.8 mm h–1, with an average flow rate of 58.6 mm h–1. It is worth mentioning that the runoff rate of 66.8 mm h–1 was greater than the average rainfall rate of 65.0 mm h–1; it did not exceed the design rainfall intensity of 75 mm h–1. The similarity of hydrographs in Fig. 1 and analysis of the data showed that soil properties did not have a large influence on total measured runoff volumes during each interval. However, a marginal effect of soil organic matter was evident, showing that the total runoff volume decreased with increasing organic matter, but only with the Teq runoff interval (Fig. 2). Note that the low r2 presented here was highly influenced by an outlier that, when removed, yielded an r2 of 0.39.


Figure 2
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  		Fig. 2. Effect of soil organic matter content (OM) on runoff volumes for the T30 and Teq runoff intervals.

 
For Soils 4, 9, 19, and 22, Fig. 3 shows typical time series curves for instantaneous DIP concentrations in runoff. The DIP concentrations were greatest during the first 30 min of runoff and decreased asymptotically to near equilibrium, thereafter showing an apparent inverse relationship with runoff flow rates (Fig. 1). However, the effects of dilution as a function of runoff rates could not fully account for the decrease in DIP concentrations over time. Figure 4 shows instantaneous DIP flux rates as a function of time. It was evident that, except for the first sample, DIP flux decreased with time and, similar to the DIP concentrations, appeared asymptotic. The decrease in both DIP concentrations and flux with time may have coincided with a decrease in the quantity of more soluble forms of P and/or a decrease in P desorption rates as the simulation progressed. In addition, a dilution phenomenon, not singularly tied to instantaneous runoff rates, may have been operating. Perhaps pre-wet soil water with elevated P concentrations was gradually diluted by fresh simulation water and the rate of this process lagged behind the visible runoff rates that were likely controlled by the rapid development of surface seals. Pote et al. (1996) attributed similar declines in DIP concentrations to dilution. However, in this study, dilution as a function of observed runoff rates was insufficient to describe the asymptotic decreases in DIP concentrations over time.


Figure 3
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  		Fig. 3. Instantaneous dissolved inorganic phosphorus (DIP) concentrations in runoff versus time for Soils 4, 9, 19, and 22.

 

Figure 4
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  		Fig. 4. Instantaneous dissolved inorganic phosphorus (DIP) flux in runoff versus time for Soils 4, 9, 19, and 22.

 
Pre-wetting the soils before simulation minimized inherent variations in antecedent soil moisture conditions, thus standardizing soils to similar initial soil water contents. However, pre-wetting also had several effects on the experiment. Physically it gave rise to a quick start to runoff (a few minutes) and a rapid increase in runoff flow rates. Chemically, pre-wetting provided sufficient time for a larger portion of the readily soluble and more slowly soluble forms of P to reach solution, thus elevating P concentrations in the soil water. Later, during rainfall simulation, with the dilution of older pre-wet water with clean simulation water and the decrease of soil P release rates, P concentrations in the water surrounding soil particles were also expected to decrease. This was evident in steadily decreasing instantaneous DIP concentrations observed during the T30 interval as compared to lower, more stable DIP concentration time series observed during the Teq interval.

Estimating Dissolved Inorganic Phosphorus–Flow-Weighted Mean Concentration in Runoff
Scatter plots for both T30 and Teq runoff intervals showing relationships between each of the six STP extraction procedures and DIP–FWMC in runoff are shown in Fig. 5. The relationship between STP and DIP–FWMC was linear and highly significant (P < 0.001), regardless of the STP extraction method (Table 4). Values for r2 ranged from 0.74 to 0.96 for T30 and from 0.76 to 0.94 for Teq and varied only 5% between both runoff intervals. Thus, either of the runoff intervals could be used to develop strong DIP–FWMC vs. STP relationships (Table 4). Although the DW method, the only nonroutine STP method used in this study, yielded the lowest r2 values of 0.74 and 0.76 for T30 and Teq, respectively, it still adequately described DIP–FWMC measured in runoff in this study. However, routine agronomic STP methods performed much better yielding r2 values greater than 0.90 for both runoff intervals (T30 and Teq). These included the Norwest, Modified Kelowna, Kelowna, and Mehlich-III. Like the DW extraction, the Miller–Axley also yielded a low r2 value for both runoff intervals (0.78 for T30 and 0.82 for Teq).


Figure 5
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  		Fig. 5. Relationships of dissolved inorganic phosphorus–flow-weighted mean concentration (DIP–FWMC) and soil test phosphorus (STP) from the six extraction methods for the first 30 min (T30) and the equilibrium (Teq) runoff intervals (n = 38).

 

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  		Table 4. Equation coefficients used for predicting dissolved inorganic phosphorus–flow-weighted mean concentration (DIP–FWMC) in runoff as a function of soil test phosphorus (STP) from six extraction methods for the first 30 min (T30) and the equilibrium (Teq) runoff intervals (n = 38).

 
It has been suggested that the correlation between STP and DIP using distilled water as the extraction agent would be better than routine STP methods (Pote et al., 1996; Hooda et al., 2000) since it more closely represented the primary extractant (water) operating during natural rainfall events. However, the results from this study showed that the stronger, agronomic-based extractants performed better. Notwithstanding this study's results, it is also worth pointing out that the weaker r2 for DW method was likely a function of just one data point (Fig. 5). With this data point eliminated, the relationship would have been just as strong as all the others.

Similar studies have reported a linear relationship between some measure of STP and the dissolved forms of P in runoff (Romkens and Nelson, 1974; Sharpley, 1995; Pote et al., 1996; Cox and Hendricks, 2000; Sims et al., 2000; McDowell and Sharpley, 2001; Torbert et al., 2002; Fang et al., 2002; Daverede et al., 2003; Andraski and Bundy, 2003), which, along with the results from this study, provide support for using results from commercial STP extractants as potential predictors for estimating DIP concentrations in runoff water flowing over agricultural soils.

Vadas et al. (2005) investigated published data from 17 different studies that used simulated rainfall to determine the relationship between some measure of STP and dissolved P release from soil. Pooled, these studies represented 31 different soils across a range of simulation run times (15–160 min) and different rainfall intensities (50–100 mm h–1). Despite the wide range of methodologies employed across these various studies, Vadas et al. (2005) found that extraction coefficients (the slope of the regression lines) were often not significantly different among various studies. Two of the STP methodologies examined by Vadas et al. (2005) were the same as the ones presented in this study (Mehlich-III and DW). Across those studies that used the Mehlich-III test, extraction coefficients ranged from 1.2 to 3.0, similar to the range of those presented here (3.7 for T30 and 2.0 for Teq). For those studies that used a DW test, extraction coefficients ranged from 6.0 to 18.3, and were also similar to those found in this study (23.0 for T30, 12.6 for Teq).

It is not surprising that the value of the extraction coefficient varied with STP methodology (Table 5) as each STP procedure extracted a different amount of P per unit soil. However, in this study, extraction coefficients for the T30 interval, derived from the same soil test, ranged from 1.83 to 1.87 times higher than those derived from the Teq interval (Table 5). An evaluation of 95% confidence intervals (CI) constructed around regression coefficients (Neter et al., 1990) showed that for each STP, extraction coefficients were significantly different between the T30 and Teq runoff intervals, since CI values for any two models did not overlap completely for both runoff intervals (Table 5).


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  		Table 5. Comparisons between standard errors of slopes and 95% confidence intervals around the extraction coefficients for soil test phosphorus (STP)–dissolved inorganic phosphorus (DIP) models for the first 30 min (T30) and equilibrium runoff intervals (Teq).

 
Vadas et al. (2005) concluded that the agronomic soil tests are equally, if not more, effective for evaluating P release to soils as was the environmentally oriented DW soil test. Furthermore, they concluded that soil tests were inherently chemically sensitive to soil properties and, as such, soil properties needed not be considered as additional factors when modeling P release from soils to runoff waters. The data presented in this study strongly supports these conclusions. In addition, the results from this study also show that the extraction coefficients, although apparently soil independent, are not methodology independent. This suggests that extraction coefficients derived solely from rainfall simulations may not be directly useable to evaluate P export from natural catchments. More study is needed to understand the scaling issues between small-scale simulator plots and natural catchments. In addition, other factors such as management, vegetation, and hydrology need to be evaluated independently relative to their effects on P release to runoff waters.

The greater DIP–FWMC observed during the T30 phase may represent a flushing phenomenon that is not representative of the bulk of runoff that occurs during the majority of natural events, since many of them last for more than 30 min. Since T30 appears to represent a first-flush phenomenon, extraction coefficients developed from this runoff interval may not be suitable for predicting DIP losses in runoff under Alberta conditions.

Since DIP–FWMC appeared to be stable during the Teq runoff interval, extraction coefficients based on this period are likely more representative of the long duration runoff events that arise from the synoptic storms and spring snowmelt events that typically dominate runoff events in Alberta. This may be true, despite the fact that the rainfall intensity used in this study was 65 mm h–1. Indeed, rainfall amounts in excess of 97.5 mm in 90 min are relatively rare and often result in significant erosion, accompanied by excessive sheet wash, rilling, and gully formation. None of the soils rained on in this study developed rill or gully structures, nor did they appear excessively eroded, suggesting that the 65 mm h–1 rainfall intensity used in this study was not as erosive as a similar event occurring in situ. This is because the soil surface in a rain frame represents a flat, bounded element that excludes runoff processes from surrounding areas. Under natural conditions, critical source areas in fields, which are often convergent landscape elements and the site of significant runoff, receive run-on and emergent interflow from upslope areas. The result is that even low intensity rainfall events can cause localized areas where runoff rates are greatly in excess of the incident rainfall intensity. In the laboratory, high rainfall intensities are usually necessary to achieve measurable runoff in a reasonable time frame. In reality, the rainfall simulator may be thought of as a runoff generator, and resulting runoff rates may not be simply related to a rainfall return period based on rainfall intensity and duration.


   CONCLUSIONS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS AND DISCUSSION
 CONCLUSIONS
REFERENCES
 
This study was conducted to investigate the relationship between STP and DIP–FWMC in runoff, and to develop regression relationships that are representative of P release to runoff from Alberta soils. Several (38) different soils exhibiting diverse physical and chemical characteristics with a wide range of STP concentrations were subjected to rainfall simulation in the laboratory. Results from these experiments showed that DIP–FWMC in runoff generated during rainfall simulation was strongly, linearly related to STP. Data showed that DIP–FWMC could be accurately predicted by any of the six STP extraction methodologies for any one of the two runoff intervals (T30 and Teq) used to characterize a simulated runoff event. However, the magnitude of the extraction coefficient (slope of the regression line) was strongly dependent on the runoff interval with DIP–FWMC; on average, 1.85 times greater during the T30 runoff when compared to the Teq runoff interval. Hence, the extraction coefficients developed in this study were greatly influenced by methodology. In addition, DIP time series concentrations measured during the Teq runoff interval were less variable over the entire interval and appeared to be at equilibrium, suggesting that the Teq runoff interval may provide more meaningful extraction coefficients for developing P release rates for long duration runoff events. However, more work is needed to validate these findings under field conditions.

This study also found that routine agronomic STP methods were better predictors of DIP–FWMC in runoff than the distilled water extraction method. Thus the distilled water method may not be the best method for environmental assessment of DIP–FWMC in runoff. This study also showed that a single extraction coefficient based on STP alone was sufficient for determining soil dependent P concentrations in runoff and that this relationship was independent of soil type.


   ACKNOWLEDGMENTS
 
Authors express their appreciation to the Canada-Alberta Beef Industry Development Fund (CABIDF) and the Canada-Alberta Hog Industry Development Fund (CAHIDF) for funding, in part, for this project. Also, thanks go to Agri-Food Laboratories Branch, Food Safety Division, Alberta Agriculture, Food and Rural Development (AAFRD) for helping in sample analysis. Acknowledgments are also due to Dr. Ann L. Kenimer (Department of Biological and Agricultural Engineering, Texas A&M University, College Station, Texas), Tom Goddard (AAFRD), Barry Olson (AAFRD), Rod Bennet (AAFRD), and Joanne Little (AAFRD) for critical reviews of an early draft of this manuscript.


   REFERENCES
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS AND DISCUSSION
 CONCLUSIONS
 REFERENCES
 




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