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DIVISION S-8—NUTRIENT MANAGEMENT & SOIL & PLANT ANALYSIS

Using the Mehlich-1 Extract to Estimate Soil Phosphorus Saturation for Environmental Risk Assessment

Dep. of Crop, Soil and Environmental Sciences, Virginia Polytechnic Institute & State University, Blacksburg, VA 24061-0404

^* Corresponding author (mikebeck{at}vt.edu)

	ABSTRACT

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION CONCLUSIONS REFERENCES

 
Methods for environmental risk assessment of P loss potential from soils lack uniformity and are generally difficult for routine analysis. Mehlich-1 extractable P (M1-P), an approach that is widely used to assess soil P status for plant growth, was used as a soil test P (STP) estimator of the degree of P saturation (DPS) of a soil. The concept of DPS integrates the dominant properties controlling the P sorption-desorption status of soils. Soil samples from three physiographic regions of Virginia were analyzed for M1-P and a wide range of other extractable P forms and selected chemical and physical soil properties. The DPS determined by ammonium oxalate (NH₄–Ox) extractable P (P_ox), Al (Al_ox), and Fe (Fe_ox), ranged from 2 to 155%. Mehlich-1 P, with a range of 1 to 1100 mg kg^–1 was the most suitable single variable for estimating DPS. However, soil type and properties from the three physiographic regions were sufficiently different that regression models to estimate DPS based on M1-P were significantly (P < 0.001) different between regions. Addition of other chemical or physical soil properties yielded insufficient improvements to the regression models over the strong relationships (r² = 0.93, 0.98, and 0.75 for the Ridge & Valley, Piedmont, and Coastal Plain regions, respectively) between M1-P and DPS. Interpretations/comparisons between studies are often limited by the numerous methods that are used to calculate DPS. We recommend DPS be determined as mmol kg^–1 of NH₄–Ox extractable P, Al and Fe and calculated as 100 (P_ox) (Al_ox + Fe_ox)^–1.

Abbreviations: , fractional P saturation • _m, value at P saturation • Al_ox, ammonium oxalate extractable Al • DH₂O, distilled water • DPS, Degree of P saturation • Fe_ox, Ammonium oxalate extractable Fe • F_r, remaining P sorption capacity • H₂O-P_t, ICP analyzed water extractable P • ICPES, inductive coupled plasma emission spectroscopy • M1-P, Mehlich-1 extractable P • NaOH-P_i, sodium hydroxide extractable P analyzed by molybdate blue method • NaOH-P_o, NaOH-P_t – NaOH-P_i • NaOH-P_t, sodium hydroxide extractable P analyzed by ICPES • NH₄–Ox, ammonium oxalate • P_i, Ortho phosphate analyzed by molybdate blue method • P_o, Soluble organic P • P_ox, Ammonium oxalate extractable P • P_t, total P • PSC, P sorption capacity • STP, soil test P • VTESTL, Virginia Tech Extension Soil Testing Laboratory

	INTRODUCTION

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION CONCLUSIONS REFERENCES

 
THE ROLE OF P in eutrophication of surface waters has long been recognized. The elimination of P from laundry detergents in the 1970's was a relatively simple and effective regulatory approach to eliminate one source of P into surface waters. However, nonpoint source inputs of P from agricultural fields to surface waters, while widely recognized for decades (Ryden et al., 1973), represent a more complex and evolving problem and environmental concern. The focus of research on P movement to surface waters has shifted from particulate P/soil erosion (Ryden et al., 1973; Logan, 1982) to dissolved P in runoff waters (Sharpley et al., 1994; Pote et al., 1996), and to leaching and subsurface tile drainage losses (Breeuwsma et al., 1995; Schoumans and Groenendijk, 2000; Siddique et al., 2000; Sims et al., 2000). Phosphorus has generally been considered an immobile element in acidic soils due to the strong reactions with soil Fe and Al complexes. However, substantial increases in livestock production densities and subsequent manure-P loading rates that far exceed crop uptake (Breeuwsma et al., 1995; Sims et al., 1998), have resulted in a shift of the equilibrium of P loading vs. P sorption potentials. As a consequence of excessive loading rates, the finite P sorption capacity of many soils has become increasingly saturated, and the ability of the soil to "fix" P is reduced. This process results in an increased rate of P release or desorption and potential for transport to surface waters (Sharpley, 1996). In one study of heavily amended soils, Siddique et al. (2000) found a curvilinear relationship between P leaching and soil P saturation with an inflection point, or change point, after which there was significant leaching of soil P with further P loading.

Since STP is clearly and positively correlated with dissolved reactive P in runoff water (Pote et al., 1996; Sharpley et al., 1977), STP is used by some regulatory authorities for environmental P risk assessment to impose action threshold levels (Sims, 1992), or it is incorporated as one of multiple variables in P-index approaches to assess the risk of P reaching surface waters (Coale et al., 2002; Sims et al., 2000). However, since STP does not account for soil-specific soil-P reactions (Bache and Williams, 1971), results are soil-specific and the interpretation may be ambiguous (Hooda et al., 2000). For example, Sibbesen and Sharpley (1997) in a study of Danish soils obtained different regression relationships of dissolved P vs. STP for seven soils. To control for such variation in soil properties, Schoumans and Groenendijk (2000) postulated that to use STP as an environmental indicator, it must take into account more relevant soil chemical properties, in particular NH₄–Ox-extractable Al and Fe, which can serve as an estimator of P sorption capacity (PSC).

The concept of DPS has been developed as a tool to compare soils of different contents of P and oxalate-extractable Al and Fe (van der Zee and van Riemsdijk, 1988). Breeuwsma et al. (1989)(1995) posed a general definition of DPS as P_ox divided by the PSC of the soil. As documented by Beauchemin and Simard (1999), the application of this general concept has found wide acceptance, but the methods used to obtain specific DPS values vary greatly. However, to facilitate the widespread use of the concept of P saturation to assess the environmental risk of soil P loss, the methods to determine the necessary variables need to be simple, inexpensive, and suitable for routine analysis. Unfortunately, they are not.

Extraction by Mehlich-1 solution (Mehlich, 1953) is the current STP used in the Virginia Tech Extension Soil Testing Laboratory (VTESTL) and specified by current Virginia nutrient management regulations. Therefore, our objectives were to: (i) investigate the relationship of DPS in soils from three major agriculturally important physiographic regions of Virginia with M1-P and other potentially predictive soil properties, (ii) develop a model to obtain DPS using M1-P and other relevant soil properties, and (iii) evaluate the possibility of standardizing the methods used to determine DPS.

	MATERIALS AND METHODS

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION CONCLUSIONS REFERENCES

 
Field Sampling
Field sites from three counties representing the three major agriculturally important physiographic regions of Virginia were sampled in the fall of 1998: Accomack County on the Coastal Plain of the Eastern Shore; Amelia County in the Piedmont; and Rockingham County in the Ridge and Valley region of Virginia. The dominant soil series sampled were Frederick (Fine, mixed, semiactive, mesic Typic Paleudults) for the Ridge & Valley, Cecil (fine, kaolinitic, thermic, Typic Kanhapludults) for the Piedmont, and Bojac (Coarse-loamy, mixed, semiactive, thermic Typic Hapludults) in the Coastal Plain. Sites were selected to obtain soils with a wide range of M1-P and varied management histories. Sampled fields were under a range of management conditions including heavy use of animal manures and fertilizers to unmanaged woodlots. Each site was <2 ha in size except for one site in Amelia. Composite soil samples were obtained by randomly taking 2.5-cm diam. cores at 20 cores plus two cores per hectare at two depths, 0 to 5 and 0 to 15 cm. Hence, for a 5-ha field, 30 (20 plus 10) soil cores were collected for each depth.

Laboratory Methods
Soil samples were air-dried and ground to pass a 2-mm sieve. A subsample was sent to the VTESTL for determination of M1-P (0.05M HCl + 0.025M H₂SO₄), Ca, Al, and Fe (Mehlich, 1953) by inductively coupled plasma emission spectrometry (ICPES, type FTMOA85D, Spectro Analytical Instruments, Inc, Kleve, Germany). Soil pH was determined on a 1:1 ratio (v/v) of soil/distilled water (dH₂O). Particle-size analysis was determined by the pipette method (Day, 1965).

Water-extractable P was determined by shaking soils at a 25:1 (v/w) dH₂O/soil ratio for 1 h at room temperature (Pote et al., 1996). Samples were centrifuged for 10 min at 1150 x g (2000 rpm) and filtered through a 0.45-µm filter membrane. The extract was analyzed for its total P (H₂O-P_t) by ICPES and for ortho-P (P_i) by the molybdate-blue method (Murphy and Riley, 1962). Water-soluble organic P (P_o) was determined as the difference between H₂O-P_t and P_i. Ammonium oxalate extractable P was determined by adding 40 mL of 0.2 M NH₄–Ox adjusted to pH 3.0 to 1 g soil, shaking for 2 h in the dark, centrifuging for 10 min at 2000 rpm, and filtering through number 40 Whatman filter paper (Pote et al., 1996; Sheldrick, 1984). Total P, Al, and Fe in the extracts were analyzed by ICPES. Total soil P (P_t) was extracted by digesting 0.4 g of soil in 3.5 mL concentrated H₂SO₄ with a CuSO₄/K₂SO₄ catalyst and determining P concentration by flow injection analysis colorimetry (Lachat Instruments, 1996). Phosphorus extractable by 0.1 M NaOH was determined by shaking soils at a 60:1 (v/w) solution/soil ratio for 16 h at room temperature (Hedley et al., 1982), centrifuged for 10 min at 2000 rpm and filtered through number 40 Whatman paper. The extracts were analyzed for NaOH-P_t by ICPES and NaOH-P_i by colorimetry (Murphy and Riley, 1962). Organic P in this fraction (NaOH-P_o) was determined as the difference between NaOH-P_t and NaOH-P_i.

Numerous methods for determining the DPS of a soil are available (Beauchemin and Simard, 1999), but most follow the general format of Eq. [1].

[1]

where PSC can be determined from adsorption studies but is often estimated from the amount of Al_ox and Fe_ox extracted from a soil. The methods by which DPS are calculated differ greatly. In our study we used the formula in Eq. [2].

[2]

where P_ox, Al_ox, and Fe_ox refer to ammonium oxalate extractable ions.

The remaining P sorption capacity of the soils (F_r) was estimated by the single-point adsorption method (Bache and Williams, 1971). We modified the procedure slightly, using a solution/soil ratio of 20:1 (v/w) where the solution contained 20 mg P L^–1 (as KH₂PO₄) in 0.01 M CaCl₂, and the samples were shaken for 18 h at room temperature. After shaking, the soils were centrifuged for 10 min at 1150 x g (2000 rpm) and filtered through #40 Whatman paper. The extracts were analyzed for P_i by the molybdate-blue method (Murphy and Riley, 1962).

Statistical Analysis
For the statistical testing of differences between physiographic regions for the M1-P to DPS relationship, a logarithmic transformation was applied to both variables (M1-P and DPS) because a few data points with high values for both variables were judged to have extreme influence on the analysis and plot (as indicated by leverage index plots). After transformation, no outliers were apparent and the variance was relatively stable along the regression line. The X-Y relationship appeared linear after the transformation, but there was a slight and statistically significant deviation from linearity. Therefore, the final comparisons are based on a quadratic regression of log(M1-P) on log(DPS).

Pairwise statistical comparisons of M1-P at select values of DPS (20, 35, 50, 65) were performed using SAS proc nlMixed with the "contrast" and "estimate" commands. Because the available data are not measurements of M1-P at precisely those DPS values, the comparisons are based on fitted regression lines relating log transformed M1-P (the dependent or Y variable) to log transformed DPS (the independent or X variable). Statistical analyses were performed using the PC software package by Statistical Analysis Systems, Version 8.e (SAS Institute, 1999).

	RESULTS

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION CONCLUSIONS REFERENCES

 
Soil Properties
A summary of pertinent soil characteristics is given in Table 1. The data are typical of the moderately acid surface soils of the Mid-Atlantic region under agricultural use. As intended, we were able to obtain a sample population with a wide range of M1-P as well as other relevant soil chemical properties. Median values are given because of the skewed distribution of the analyzed parameters. For example, for the Piedmont soils, the mean M1-P was 156 mg kg^–1, but the median was 85 mg kg^–1. The wide range and median values of the Al_ox and Fe_ox, soil texture, and pH indicate that while the soils were collected from similar soil map units within counties, their chemical and physical properties varied considerably. A few samples had such a high extractable P content that P desorption was observed; an actual enrichment of the solution (20 µg P mL^–1) used in the P sorption experiment. The desorption approximately coincided with calculated P saturation values >100% (data not shown).

View this table: [in this window] [in a new window]   Table 2. Pearson's correlation coefficients (r) among soil P properties and P parameters for the three major physiographic regions of Virginia.

While M1-P was the single best overall predictor of DPS in the soils studied, statistical analysis of the relationship of DPS vs. M1-P (Fig. 1a,b) shows the differences among the regions are statistically significant (P < 0.001). The statistical analysis was conducted on the log-transformed data plot (Fig. 1b) as described in the methods section. However, we depict the data (Fig. 1a, 2, 3) in the observed scale to facilitate the use and discussion of the data. Using the observed-scale data, we obtained the best model to predict DPS for the Ridge & Valley (r² = 0.93) and Piedmont samples (r² = 0.98) using a second order polynomial function. The best fit for the Coastal Plain samples (r² = 0.75) was obtained with a first-order power function. Table 3 shows the results of pair-wise comparisons of M1-P at select values of DPS. The Ridge & Valley region produced statistically higher values of M1-P than the other regions, except at the lowest DPS value. There were no significant differences between Piedmont and Coastal Plain samples.

View larger version (28K): [in this window] [in a new window]   Fig. 1. (a) Degree of P saturation (DPS, Eq. [2]) as a function of Mehlich-1 extractable P for soils of three major physiographic regions of Virginia. *** denotes significant at 0.001 probability level. (b) Log scale plot of DPS (Eq. [2]) as a function of Mehlich-1 extractable P for soils of three major physiographic regions of Virginia.

View larger version (16K): [in this window] [in a new window]   Fig. 2. Remaining P sorption capacity (Fr) as a function of Mehlich-1 extractable P for soils of three major physiographic regions of Virginia. ***Denotes significant at 0.001 probability level.

View larger version (16K): [in this window] [in a new window]   Fig. 3. Remaining P sorption capacity (Fr) as a function of degree of P saturation (DPS) for soils of three major physiographic regions of Virginia. ***Denotes significant at 0.001 probability level.

The relationship of P sorption to P saturation (Fig. 3) was the most difficult to model, since conceptually P sorption should approach zero as DPS approaches 100%. The scatter of the data was broad enough so that no one model was consistently superior for all three regions at describing this function. Evaluating the models based on r² values for the curves, the power function was as good or better than the other functions tested. The area of inflection for the three curves were at DPS values of 67, 40, and 59% for the Ridge & Valley, Piedmont, and Coastal Plain, respectively, which corresponded to F_r values of 50, 87, and 79 mg kg^–1, respectively.

	DISCUSSION

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Soil Properties
The wide range observed in both physical and chemical soil properties (Table 1) indicates that the samples encompassed a broad range of conditions relative to potential P mobility. This facilitates a broader interpretation of the results and supports our assumption that the soil samples collected from the three counties can be used to represent the soils of these three major physiographic regions of Virginia, and that they were typical of the moderately acid soils of the mid-Atlantic region. The soil chemical properties, particularly Al_ox and Fe_ox are in agreement with those reported by Paulter and Sims (2000) for 122 samples from Delaware's Piedmont and Coastal Plain region. However, our samples from the Piedmont region covered soils with a wider range in total P and M1-P, and with higher mean M1-P content (85 vs. 28 mg kg^–1). A further difference between our samples and those of Paulter and Sims (2000) was that M1-P and P_ox as a percentage of total P were greater, 25 and 65% vs. 5 and 20%, respectively for the Piedmont samples, but very similar to their Sussex County Coastal Plain soils. The Ridge & Valley province does not extend into Delaware and therefore our data sets were not comparable for that region. However, all of these data strongly contrast with those for soils used by van der Zee and van Riemsdijk (1988) where P_ox was nearly equivalent to total P of the soils.

Mehlich 1-Phosphorus vs. Degree of Phosphorus Saturation and Phosphorus Sorption
The results discussed thus far demonstrate significant regional differences in soil properties, P parameters, and their basic internal relationships. Our hopes were that M1-P, which is currently the STP method for crop fertilizer recommendation in Virginia, could also be used to assess the P status of soils from all regions. The strong relationship of M1-P to DPS appeared to make M1-P suitable for such use. However, unlike for crop fertilization applications, regional soil differences necessitated separate models for the three major physiographic regions. At a DPS of 20%, the range of M1-P was 58 to 65 mg kg^–1 for the soils of the three regions (Table 4). This coincided well with the existing M1-P cut-off value of 55, above which soil P is deemed excessive and no P fertilization is recommended. However, at DPS exceeding 20%, M1-P values diverged for the soils of the three regions. At 65% DPS, the corresponding M1-P values are 496, 356, and 283 mg kg^–1 for the Ridge & Valley, Piedmont, and Coastal Plain soils, respectively. The significance of this divergence and the need for separate models is illustrated by the following example. If the regression model were based on the combined data of all regions, the M1-P corresponding to 65% DPS would be 464 mg kg^–1. However, a M1-P of 464 mg kg^–1, actually corresponds to a DPS of 62% for the Ridge & Valley, 80% for the Piedmont, and 94% for the Coastal Plain soils.

The uniform nonregional application of M1-P as the STP extractant has been very useful for crop fertilization recommendation across the physiographic regions of Virginia. This use is primarily focused on M1-P at <55 mg kg^–1, which coincides with a DPS of slightly <20% (Fig. 1, Table 4). However, from an environmental standpoint, M1-P values of >60 mg kg^–1 are of particular interest. At these higher levels, the relationships of soluble P to DPS (albeit derived by various methods) predict increased P_i concentrations in soil solution (Schoumans and Groenendijk, 2000), in leachates (Siddique et al., 2000), and in runoff from fields (Pote et al., 1999), at concentrations that may be harmful to aquatic systems. It is also at these higher values of M1-P and DPS that differences between soil types become evident. Our data (Table 4, Fig. 1) show that differences between soil types (or physiographic regions) become increasingly pronounced at higher DPS values. Another example is provided by Sharpley (1996), who showed that equal Fe-strip P concentrations resulted in different DPS estimates for two soils (6.4 vs. 9.9%) even at relatively low levels of DPS. Thus, for the use of the Mehlich-1 extraction as a predictor of DPS, it is necessary to identify possible groupings of soils and to determine separate M1-P vs. DPS relationships for those groupings.

The strong curvilinear relationship of P sorption to M1-P (Fig. 2) indicates that M1-P increases at a small rate as the remaining PSC increases to a value approximately 165 mg kg^–1 (approximately 40 mg kg^–1 M1-P). However, M1-P increases at a much faster rate once the remaining PSC increases above approximately 165 mg kg^–1. This inflection point around a sorption capacity of 165 mg kg^–1 also coincides with a DPS of slightly <20% (Fig. 3).

The risk assessment of P-export from a field or watershed to surface waters has evolved to the development of various forms of P indices (Lemunyon and Gilbert, 1993). The P index approach includes STP as the criterion for determining the soil P sorption/desorption status, while other parameters assess landforms and management practices. We showed that the concept of DPS is a suitable criterion to assess the soil P sorption/desorption status of acid to moderately acid soils of the Mid-Atlantic region for environmental risk assessment of P losses to surface and subsurface waters. However, NH₄–Ox extraction is not suitable for routine, large-scale laboratory analysis. Yet, as our data demonstrate, if a P index for Virginia were based solely on M1-P levels applied uniformly across the state, a specific M1-P level (and possibly associated management practices) would not reflect the potential soil P sorption/desorption status across the soils of the three physiographic regions sampled. A solution to this problem is to determine if there are soil/regional differences in soil P reaction properties and subsequently develops regression models to differentially calculate DPS based on the commonly used STP. This allows for the inclusion of a DPS based assessment in the P index, yet would not necessitate any changes in established soil testing procedures. The differentiation of our data into separate models for the three physiographic regions and the high r² values for these models support such an approach.

Ammonium Oxalate-Extraction for the Determination of DPS: The Alpha Variable
The concept of DPS has found widespread acceptance as an environmental assessment tool of the soil P sorption/desorption status. Yet results from studies conducted over the past 15 yr are limited by the multitude of ways that DPS estimators were calculated. Deviations particularly include the variable and associated calculations. The variable and its use were initially defined by van der Zee and van Riemsdijk (1988) as a "saturation factor" to "compare soils with different contents of P and oxalate-extractable Fe and Al." They defined as in Eq. [3].

[3]

Thus, as defined in Eq. [3] and multiplied by 100 is equivalent to DPS as defined in Eq. [2]. These equations do not normalize the DPS or based on the respective soil's P sorption maximum. Van der Zee and van Riemsdijk (1988) did this by defining _m as at total P saturation as in Eq. [4].

[4]

where F_r = remaining sorption capacity of the soil determined by a one-point 18-h lab experiment. To account for the fact that short-term P sorption experiments underestimate the sorption capacity as determined by long-term experiments (Fox and Kamprath, 1970), van der Zee and van Riemsdijk (1988) determined that a 1.8 scaling factor adjusts the F_r variable to reflect the remaining long-term P sorption capacity. The effective DPS is thus /_m, which can be rewritten as in Eq. [5].

[5]

There are several shortcomings that limit the widespread application of Eq. [5].

1. The use of Eq. [5] requires the empirical determination of the remaining P sorption capacity for each soil sample. While this is feasible for research purposes, that analysis is not routine in soil testing laboratories and thus deters the calculation of DPS based on Eq. [5].
   
2. To circumvent the determination of F_r, some researchers have used mean _m values from studies that were conducted with soils of similar properties. To average _m = 0.40 and _m = 0.61 into an _m = 0.50 (Lookman et al., 1995) is convenient, but ignores the wide range in _m (e.g., see this study, Table 1) and the large standard deviations associated with _m (van der Zee and van Riemsdijk, 1988; Paulter and Sims, 2000). The broad range of _m indicates important differences in the soil P sorption–desorption properties. This may significantly affect field-scale P management if such management is dictated by a P index that includes DPS based on _m. This point is illustrated in Fig. 4 . We plotted the effective DPS (Eq. [5]) where 1.8F_r was determined for each soil to obtain _m versus the DPS with use of _m = 0.5 for all soils. The use of _m = 0.5 grossly overestimated the effective DPS, and produced a DPS > 100% for 24 out of 121 soil samples. However, 21 out of the 24 samples actually have remaining P sorption capacities (F_r) and effective DPS in the range of 75 to 95%. The use of _m = 0.61, for example (Maguire et al., 2000) to account for the remaining long-term sorption–desorption capacity, would further skew, or overestimate the results. If _m = 0.5 is used only as a constant (as reported by Leinweber et al., 1997; DeSmet et al., 1996a, 1996b; Lookman et al., 1995), its effect is only that of a scaling factor. The results of studies on DPS could then still be compared with other studies as long as one makes the adjustment for the inclusion or exclusion of _m = 0.5 (but by many authors referred to as , see Point 5) and its effect on DPS.
   
3. The determination of F_r is subject to experimental conditions, particularly solution concentration, initial soil P concentration, and time (Hooda et al., 2000), as well as the use of sorption isotherm vs. one-point P sorption (Olsen and Watanabe, 1957; Bache and Williams, 1971).
   
4. A further source of variation in the _m determination has involved the use of the 1.8 constant to adjust F_r to the remaining long-term P sorption capacity of the soil. Ignoring this constant overestimates DPS. Also, applying this 1.8 scaling factor to instead of F_r (Paulter and Sims, 2000) does not yield the same results and significantly overestimates DPS.
   
5. The lack of consistent use of the terms and _m, particularly if DPS is calculated according to Eq. [5], limits subsequent interpretation/comparisons with other results due to this uncertainty.
   

View larger version (11K): [in this window] [in a new window]   Fig. 4. Relationship of effective degree of P saturation (DPS) calculated with empirically determined m versus the use of 0.50 as a constant for m.

The omission of F_r, and consequently _m, helps from a practical standpoint to streamline DPS determination for more routine and less time-consuming laboratory analyses. We support this omission and encourage the use of DPS according to Eq. [2]. The use of a constant for _m, greatly overestimates the effective DPS. We show this using our combined data in Fig. 4 where we plotted the effective DPS (Eq. [5]) versus effective DPS with _m = 0.5. To use a constant with reasonable confidence, the soil samples would have to be very homogenous in their soil P sorption–desorption properties, which is seldom the case. The relationship of effective DPS (/_m) vs. is not linear (Fig. 5) , but similar to that of (/_m) vs. (/_m, where _m = 0.5) (Fig. 4). However, it preserves the general concept that at DPS < 100% F_r is positive, but negative at DPS > 100%, and the number of observations falling below or above DPS = 100% remain the same.

View larger version (11K): [in this window] [in a new window]   Fig. 5. Relationship of effective degree of P saturation (DPS) calculated with empirically determined m versus DPS defined as .

	CONCLUSIONS

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION CONCLUSIONS REFERENCES

The good fit and differentiation of our data into separate models to predict DPS based on M1-P for the three physiographic regions supports this as an approach to more accurately assess the soil P sorption–desorption status for use in a P index. Our results and discussion about the variable should result in a more standardized method for the determination of DPS. Such standardization would aid more widespread comparison of results. This will be particularly important as more studies are being conducted to relate P leaching and runoff potential directly to DPS.

	ACKNOWLEDGMENTS

 
We thank Mike Brosius, Steve Nagle, and Phil Schroeder for their help in field sampling and W.T. Price for assistance in the laboratory. This work was supported by the Virginia Dep. of Conservation and Recreation Nutrient Management Program administered by H. Russ Perkinson.

Received for publication January 6, 2003.

	REFERENCES

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION CONCLUSIONS REFERENCES

 

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