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TECHNICAL REPORT
Surface Water Quality

Phosphorus Losses from Grasslands Fertilized with Broiler Litter

EPIC Simulations

^a Dep. of Crop and Soil Environmental Sciences, Virginia Tech, Blacksburg, VA 24061
^b Dep. of Crop and Soil Sciences, Univ. of Georgia, Athens, GA 30602
^c Animal and Dairy Science Dep., Univ. of Georgia, Athens, GA 30602
^d USDA Natural Resources Conservation Service, Athens, GA 30601
^e Dep. of Agric. Economics, Univ. of Missouri-Columbia, Columbia, MO 65201
^f Blackland Research Center, 808 East Blackland Rd., Temple, TX 76502

* Corresponding author (mcabrera{at}arches.uga.edu)

Received for publication July 7, 2000.

	ABSTRACT

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS Model Runs RESULTS AND DISCUSSION SUMMARY AND CONCLUSIONS REFERENCES

 
Broiler litter, a mixture of poultry excreta and bedding material, is commonly used to fertilize grasslands in the southeastern USA. Previous work has shown that under certain situations, application of broiler (Gallus gallus domesticus) litter to grasslands may lead to elevated levels of phosphorus (P) in surface runoff. The EPIC simulation model may be a useful tool to identify those situations. This work was conducted to evaluate EPIC's ability to simulate event and annual runoff volume and losses of dissolved reactive phosphorus (DRP) from tall fescue (Festuca arundinacea Schreb.)–bermudagrass [Cynodon dactylon (L.) Pers.] paddocks fertilized with broiler litter. The EPIC simulations of event runoff volume showed a trend toward underestimation, particularly for runoff events > 30 mm. On an annual basis, EPIC also tended to underestimate runoff, especially at runoff volumes > 100 mm. Both event and annual runoff estimations were strongly associated with observed values, indicating that model calibration could improve the simulation of surface runoff volume. The relationship between simulated and observed values of DRP loss was relatively poor on an event basis (r = 0.65), but was stronger (r = 0.75) on an annual basis. In general, EPIC tended to underestimate annual DRP losses. This underestimation was apparently caused by the lack of an explicit mechanism to model broiler litter on the soil surface. These results suggested that additional work on the EPIC P submodel would be warranted to improve its simulation of surface application of broiler litter to grasslands.

Abbreviations: AIP, active inorganic phosphorus • CV, coefficient of variation • DRP, dissolved reactive phosphorus • LIP, labile inorganic phosphorus • PSC, phosphorus sorption coefficient • RMSE, root mean square error • RRMSE, relative root mean square error • SD, standard deviation • SP, soluble phosphorus • SIP, stable inorganic phosphorus

	INTRODUCTION

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS Model Runs RESULTS AND DISCUSSION SUMMARY AND CONCLUSIONS REFERENCES

 
PHOSPHORUS from agricultural runoff can stimulate eutrophication in P-limited surface waters. The likelihood of P loss in runoff is increased by the recurrent application of manure from concentrated livestock operations (Edwards and Daniel, 1992; McFarland and Hauck, 1995; Shreve et al., 1995; Sharpley et al., 1996b). Surface application of broiler litter to pastures can elevate concentrations of dissolved reactive phosphorus (DRP) in surface runoff (Edwards and Daniel, 1993; Heathman et al., 1995; Vervoort et al., 1998; Sauer, et al., 1999). The concentration of DRP in a runoff event simulated 1d after application of 6.7 Mg ha^-1 of poultry litter to tall fescue was 13.5 mg P L^-1 (Sauer et al., 1999). Similar results have been found in other studies when runoff occurred soon after broiler litter application (Edwards and Daniel, 1994; Heathman et al., 1995; Sharpley, 1997; Kuykendall et al., 1999). The reason for these high concentrations is that without incorporation, surface applications are vulnerable to interaction with surface runoff water. Nutrient concentrations in runoff decrease with time after application because manure constituents are moved into the soil by rainfall and incorporated by fauna (Edwards and Daniel, 1994; Sharpley, 1997; Sauer et al., 1999).

Some states are beginning to regulate or discourage manure applications to soils with relatively high concentrations of available P (National Resources Conservation Service, 1994). One of the challenges for implementing this P-management strategy is the identification of soil test P levels that cause unacceptable levels of P loss in runoff. Establishing these levels is often controversial because the data relating soil test phosphorus (STP) to runoff P is limited and, when available, is site specific. In addition, Sharpley et al. (1996a) concluded that STP should not be the sole criterion to determine the potential for P enrichment in runoff. Instead, a desirable approach would be one that integrates STP with estimates of potential runoff and erosion losses, as well as local climatic, topographic, and agronomic factors (Sharpley et al., 1996a; Lemunyon and Gilbert, 1993). One way to integrate all of these factors is through the use of simulation models.

The Erosion Productivity Impact Calculator (EPIC) is a comprehensive, continuous, lumped parameter, fieldscale simulation model capable of estimating runoff and runoff transport of nitrate N, organic N, soluble P, total P, and sediment yield (Williams, 1995). The EPIC model runs on a daily time step and can consider a drainage area of up to 100 ha. The model was designed to simulate soil erosion and its long-term effects on crop productivity for a wide variety of soils, climates, crops, and soil conservation practices using readily available data inputs.

The P submodel of EPIC simulates soil organic, inorganic, and plant P dynamics on a daily time step. It simulates P uptake and transformations in up to 10 soil layers, and considers soil chemical and physical properties (i.e., sorption characteristics), crop P requirements, fertilizer rate, tillage practice, soil temperature, and soil water content (Jones et al., 1984; Sharpley and Williams, 1990; Williams, 1995).

Whereas the crop growth submodel of EPIC has been tested with different crops (Kiniry et al., 1990; Martin et al., 1993) and the water erosion submodel has been validated under different conditions (Edwards et al., 1994; Purveen et al., 1997), the P submodel in EPIC has received little attention, especially where animal manure applications are involved. Working with a 1.2-ha, grazed paddock fertilized with poultry manure slurry in Arkansas, Edwards et al. (1994) found a significant relationship (r² = 0.74, p = 0.05, n = 31) between observed and EPIC-simulated DRP losses in individual runoff events. In the same work, however, no significant relationship (p = 0.05, n = 30) was found between these variables for a 1.1-ha, grazed paddock that was fertilized with broiler litter. Although the reason for this difference was not clear, it may have been related to the different physical characteristics of poultry manure slurry (wet) and broiler litter (dry). Clearly, additional validation work of the P submodel in EPIC is needed for grasslands fertilized with broiler litter. The objective of this study was to evaluate the ability of the EPIC model to simulate runoff volume and runoff P losses from six paddocks that were planted with tall fescue and common bermudagrass and were fertilized with broiler litter.

	MATERIALS AND METHODS

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS Model Runs RESULTS AND DISCUSSION SUMMARY AND CONCLUSIONS REFERENCES

 
Description of Monitored Site
Six fescue–common bermudagrass paddocks (0.72 to 0.79 ha, 6–8% slope) located at the Central Georgia Branch Station of the University of Georgia (33°24'N, 83°29'W) were used for this study (Table 1). The soil series present at the site are Cecil (fine, kaolinitic, thermic Typic Kanhapludults), Altavista (fine-loamy, mixed, semiactive, thermic Aquic Hapludults), Helena (fine, mixed, semiactive, thermic Aquic Hapludults), and Sedgefield (fine, mixed, active, thermic Aquultic Hapludalfs).

Three of the paddocks were grazed under continuous stocking and three under rotational stocking, but for this study we considered each paddock a replication because we found no effect (p < 0.10) of stocking method on runoff quantity or quality (Kuykendall et al., 1999). From January 1995 until March 1997, the stocking rate on each paddock was adjusted to maintain approximately 1300 to 1700 kg forage ha^-1 on a dry matter basis. After March 1997, all the paddocks were used for hay production.

Broiler litter was applied in March and October 1995 and in March and September 1996 (Table 2). Urea–ammonium nitrogen solution (UAN) was applied in March 1997 (67 kg N ha^-1) and in March 1998 (56 kg N ha^-1). Broiler litter samples were analyzed for total N, total P, inorganic N, and water-soluble P, as described in Kuykendall et al. (1999).

The plant growth submodel of EPIC operates on a daily time step to simulate water and nutrient uptake and the interception and conversion of energy to aboveground biomass, crop yield, and root growth for most common crops. Plant growth is constrained by water, nutrient, and air temperature stresses. Growth for both annual and perennial crops can be simulated.

The EPIC model describes the soil as a series of up to 10 layers of varying thickness, each with its own bulk density, hydraulic conductivity, available water capacity, and other soil characteristics. The EPIC model has an extensive soil database consisting of soil chemical, physical, and taxonomic data available in the U.S. Soil Conservation/State Agricultural Station Soil Survey Investigative Reports (SSIR's) and USDA Soil Conservation Service pedon descriptions.

The hydrology submodel uses daily rainfall to estimate runoff using the Soil Conservation Service curve number method (Soil Conservation Service, 1972). A modified rational formula method is used to estimate peak discharge (Soil Conservation Service, 1986). A stochastic element is included in the Rational Equation to allow realistic simulation of peak runoff rates given only daily rainfall and monthly rainfall intensity.

The EPIC model's livestock grazing option is simulated as a daily harvest operation. Daily grazing rates in kg ha^-1, minimum grazing height in mm, harvest efficiency, and grazing begin and end dates are entered as input. Harvest efficiency estimates the fraction of grazed plant material used by animals and not returned as manure and urine. Hay harvests are simulated by removing an indicated amount of biomass from the field.

Phosphorus in EPIC
The structure of the soil and plant P submodel in EPIC provides for pools of stable, active, and labile inorganic P; fresh organic and stable organic P; and grain, stover, and root P (Jones et al., 1984). Labile inorganic phosphorus (LIP) is defined as the P extracted by anion exchange resin (Sharpley et al., 1984). Because this soil P pool is not routinely measured by soil testing laboratories, Sharpley et al. (1984) developed equations to estimate LIP from soil test P (Bray 1, Olsen, and Mehlich 1). Active inorganic phosphorus (AIP) is estimated from LIP and the phosphorus sorption coefficient (PSC) with the equation: AIP = LIP (1 - PSC)/PSC. The P sorption coefficient is estimated from physical and chemical soil properties (Sharpley et al., 1984) and in our study it had values of 0.28 for Altavista, 0.22 for Cecil, 0.23 for Helena, and 0.29 for Sedgefield. Stable inorganic phosphorus (SIP) is estimated from AIP, assuming that at equilibrium SIP is four times as large as AIP.

Fertilizer P is labile at application, but may be quickly transferred into the active inorganic P pool. In each soil layer, flow between labile and active inorganic P pools is governed by the equilibrium equation:

where FLA is the inorganic flow rate between labile and active P in kg ha^-1 d^-1; LIP is the amount of labile inorganic P in kg ha^-1; k is a rate constant in d^-1; AIP is the amount of active inorganic P in kg ha^-1; and PSC is the P sorption coefficient defined as the fraction of fertilizer P remaining in the labile P pool after the initial rapid phase of P sorption is complete.

The amount of P computed with the above formula is subtracted daily from the labile inorganic P pool and added to the active inorganic P pool. When FLA is negative, the flow reverses.

In each soil layer, the flow between the active inorganic P pool and stable inorganic P pool is governed by the equation:

where FAS is the flow rate between active and stable inorganic P pools in kg ha^-1 d^-1; FC is the flow coefficient in d^-1; SIP is the amount of stable inorganic P in kg ha^-1; and AIP is the amount of active inorganic P pool in kg ha^-1.

When SIP > 4 AIP, the flow reverses and is multiplied by 0.1, because reverse flow is much slower. This amount of P is added to the stable inorganic P pool and subtracted from the active inorganic P pool daily to simulate slow adsorption of P. The flow coefficient is a function of PSC and is expressed by one of two equations: one for calcareous and one for noncalcareous soils.

When organic fertilizer is added, a certain proportion of the P is added to the fresh organic P pool and the remaining P is added to the labile inorganic P pool. These values can be entered as input if the user has this data for a specific fertilizer or manure. We used this option to specify the composition of the broiler litter added (Table 2). The partitioning between organic and inorganic P was done assuming that organic P constituted 10% of the total P measured in the litter (Sharpley and Moyer, 2000).

Crop P uptake from a soil layer is governed by amounts of labile P, soil water, and roots in the layer. Stover and root P are added to the fresh organic P pool after their death and/or incorporation into the soil. Decomposition of fresh and stable organic matter may result in net immobilization of labile P or net mineralization of organic P (Jones et al., 1984). The daily amount of immobilization is computed by subtracting the amount of P contained in the crop residue from the amount assimilated by the microorganisms. Phosphorus mineralization from the fresh organic P pool is a function of the amount of organic P in crop residue in each soil layer and a decay rate constant. Mineralization of organic P associated with humus is estimated by a separate equation that takes into account the organic P content in each soil layer, soil water and temperature, and the bulk density of the soil. At the end of each day, mineralized residue is subtracted from the fresh organic P pool, humus mineralization is subtracted from the organic P pool, 20% of the organic P mineralized (from the fresh organic P pool and humus) is added to the organic P content of the soil, and 80% is added to the labile inorganic P pool.

The traditional EPIC approach to soluble P loss in surface runoff is based on the concept of partitioning pesticides into the solution and sediment phases as described in Leonard and Wauchope (Knisel, 1980). Because EPIC assumes that P is mostly associated with the sediment phase, the soluble P loss in runoff is expressed as:

where SP is soluble P lost in runoff in kg P ha^-1; Q is runoff volume in 10 m³ ha^-1; CLP is the concentration of labile P in Soil Layer 1 in g Mg^-1; and k_d is a constant obtained by dividing the P concentration of the sediment by that of the water in m³ Mg^-1. The EPIC model assumes that k_d equals 175 (Williams, 1995).

We assumed that the simulated losses of SP were equivalent to the DRP losses we measured. Additional model descriptions and input data requirements can be found in Sharpley and Williams (1990), Williams et al. (1990), and Williams (1995).

	Model Runs

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS Model Runs RESULTS AND DISCUSSION SUMMARY AND CONCLUSIONS REFERENCES

 
An EPIC input file was constructed for each soil found in a paddock. Values of selected parameters (Table 3) were determined from readily available, published sources. The EPIC model's default options were selected when given a choice.

To calculate EPIC-simulated values for each paddock, we ran a separate simulation with each of the soils found in a paddock and then weighted the results according to the percentage of a paddock's area occupied by each soil. Daily estimates of runoff (mm) and runoff losses of DRP (SP, kg ha^-1) were obtained for the period of 1 Jan. 1995 to 31 Dec. 1998.

To assess model performance, we used the following statistical and graphical methods as proposed by Addiscott and Whitmore (1987) and Loague and Greene (1991): the correlation coefficient (r), a measure of linear association between measured and simulated results; the root mean square error (RMSE), a measure of the inherent error of the model; the relative root mean square error (RRMSE = RMSE/observed mean x 100), a measure of the error in relationship to the mean; and graphs of measured versus simulated results. We also regressed measured (y) against simulated (x) values and tested slopes and intercepts to determine if they were significantly different from 1 and 0, respectively (SAS Institute, 1994).

	RESULTS AND DISCUSSION

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS Model Runs RESULTS AND DISCUSSION SUMMARY AND CONCLUSIONS REFERENCES

 
Event Runoff Volume
There were strong linear relationships (r = 0.81 to 0.89; p = 0.05) between observed and estimated values of event runoff in each plot (Fig. 1, Table 4). These results are similar to those of Edwards et al. (1994), who used the EPIC model to estimate runoff volume and quality from fescue fields treated with poultry manure slurry. They found that EPIC estimations of event runoff volume were significantly related (r = 0.93, n = 35, p = 0.05) to corresponding observations, with an intercept significantly different from zero (1.4 mm) but a slope not significantly different from one. In our study, regression of observed against estimated runoff values for individual plots yielded intercepts that were significantly different from zero (3.7 to 10.9 mm) and slopes that, with the exception of Plots 2 and 5, were significantly different from one (0.77 to 1.35). These results indicated that EPIC tended to underestimate event runoff volume. This underestimation was particularly large for some runoff events > 30 mm (Fig. 1)

View larger version (24K): [in this window] [in a new window]   Fig. 1. Observed versus EPIC-simulated event runoff volumes for individual plots.

Annual Runoff Volume
Simulated values of annual runoff showed a strong and significant (r = 0.78, p = 0.05) linear relationship with observed annual values (Fig. 2, Table 4). Regression analysis yielded an intercept that was not significantly different from zero but a slope that was larger than one (1.75), indicating an overall underestimation of annual runoff. The average simulated runoff was 89 mm (standard deviation [SD] = 38 mm) whereas the average observed value was 154 mm (SD = 83 mm). In general, the underestimation was larger for 1997 and 1998, which had runoff volumes > 100 mm. As for the case of event runoff, the variability of simulated values (CV = 43%) was similar to that of observed values (CV = 54%).

View larger version (19K): [in this window] [in a new window]   Fig. 2. Observed versus EPIC-simulated annual runoff from 1995 through 1998.

View larger version (22K): [in this window] [in a new window]   Fig. 3. Observed versus EPIC-simulated event dissolved reactive phosphorus (DRP) loss for individual plots.

Annual Loss of Phosphorus in Runoff
There was a relatively strong association between simulated and observed annual DRP losses (r = 0.75; Table 4). Although regression analysis showed an intercept not significantly different from zero (1.66 kg P ha^-1) and a slope not significantly different from one (1.39), it can be clearly seen that simulated values tended to underestimate observed values (Fig. 4). The mean of simulated values was 3.3 kg P ha^-1 (SD = 2.6), whereas the mean of observed values was 6.3 kg P ha^-1 (SD = 4.6). In general, the errors in estimation were large in 1995, 1996, and 1997, but were small in 1998. The reason for the small errors in 1998 may be that the last broiler litter application was made in the fall of 1996, allowing sufficient time for litter P to infiltrate into soil layers and behave like soil P. Litter P on the soil surface does not behave like soil P because it is not in intimate contact with soil and therefore it is not exposed to the adsorption reactions modeled in EPIC. Careful observation of the simulated annual runoff volumes for 1998 shows, however, that EPIC tended to underestimate runoff volume in that year. This observation suggested that improving the annual runoff simulation would lead to an overestimation of annual DRP losses in 1998. In that case, the overestimation would probably be caused by an incorrect P sorption coefficient (PSC).

View larger version (19K): [in this window] [in a new window]   Fig. 4. Observed versus EPIC-simulated annual dissolved reactive phosphorus (DRP) loss in runoff from 1995 through 1998.

	SUMMARY AND CONCLUSIONS

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS Model Runs RESULTS AND DISCUSSION SUMMARY AND CONCLUSIONS REFERENCES

 
The EPIC model simulations of runoff volume for individual events showed a trend toward underestimation, particularly for some runoff events > 30 mm. On an annual basis, the runoff volumes simulated by EPIC also tended to underestimate observed values, especially at runoff volumes > 100 mm. Both event and annual estimations were strongly associated with observed values, indicating that model calibration could improve the simulation of surface runoff. The relationship between simulated and observed values of DRP loss was relatively poor on an event basis (r = 0.65), but was stronger (r = 0.75) for annual P losses. In general, EPIC tended to underestimate annual DRP losses. This underestimation was apparently caused by the lack of an explicit mechanism to model broiler litter P on the soil surface. These results suggested that additional work on the EPIC P submodel would be warranted to improve its simulation of surface application of broiler litter to grasslands.

	ACKNOWLEDGMENTS

 
The authors gratefully appreciate the assistance of Vaughn Calvert, Joseph Garner, Ray Harwell, and Frank Newsome at the Central Georgia Branch Station in Eatonton, Georgia. Grateful acknowledgment is also extended to Galen Harbers for equipment design and installation, and to John Rema, Odeta Qafoku, Erica Sciara, Juli Leonard, Crandall Parlor, and Nicole Wilson for help with laboratory analyses. This work was supported in part by funds received from the USDA Natural Resources Conservation Service and from the Pratt Fellowship at Virginia Tech, Blacksburg, VA.

	REFERENCES

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS Model Runs RESULTS AND DISCUSSION SUMMARY AND CONCLUSIONS REFERENCES

 

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