HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS		QUICK SEARCH:   [advanced] Author: Keyword(s): Year:  Vol:  Page:

This Article Abstract Figures Only Full Text (PDF) Free Alert me when this article is cited Alert me if a correction is posted Services Similar articles in this journal Similar articles in ISI Web of Science Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via ISI Web of Science (9) Citing Articles via Google Scholar Google Scholar Articles by Djodjic, F. Articles by Ulén, B. Search for Related Content PubMed PubMed Citation Articles by Djodjic, F. Articles by Ulén, B. Agricola Articles by Djodjic, F. Articles by Ulén, B. Related Collections Surface Water Quality Best Management Practices Nutrient Management

TECHNICAL REPORTS
Surface Water Quality

A Decision Support System for Phosphorus Management at a Watershed Scale

^a Swedish Univ. of Agricultural Sciences, Division of Water Quality Management, Box 7072, S-750 07 Uppsala, Sweden
^b Univ. of Maryland, Biological Resources Engineering, College Park, MD 20742

* Corresponding author (Faruk.Djodjic{at}mv.slu.se)

Received for publication March 14, 2001.

	ABSTRACT

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS REFERENCES

 
Phosphorus (P) is one of the main nutrients controlling algal production in aquatic systems. Proper management of P in agricultural production systems can greatly enhance our ability to combat pollution of aquatic environments. To address this issue, a decision support system (DSS) consisting of the Maryland Phosphorus Index (PI), diagnosis expert system (ES), prescription ES, and a nonpoint-source pollution model, Ground Water Loading Effects of Agricultural Management Systems (GLEAMS), was developed and applied to an agricultural watershed in southern Sweden. This system can identify critical source areas (CSAs) regarding phosphorus losses within the watershed, make a diagnosis of probable causes, prescribe the most appropriate best management practices (BMPs), and test the environmental effects of the applied BMPs. The PI calculations identified small parts of the watershed as CSAs. Only 10.4% of the total watershed area in 1995 and 5.2% of the total watershed area in 1996 were classed as "high potential P movement." Four probable causes (high P level in soil, excessive P fertilization, stream proximity, and subsurface drainage) and three BMPs (riparian buffer strips, reduced P fertilizer application, and P fertilizer incorporation) were identified by a diagnosis and prescription expert system. The GLEAMS simulations conducted for one selected CSA field for a 24-yr period showed that the recommended BMP reduced runoff P losses by 55% and sediment P losses by 71%, if applied from the first year. Results showed that using DSS may enable us to select a proper BMP implementation strategy and to realize the beneficial effect of BMPs on a long-term basis.

Abbreviations: BMP, best management practice • CSA, critical source area • DSS, decision support system • ES, expert system • GLEAMS, Ground Water Loading Effects of Agricultural Management Systems • GIS, geographic information systems • NPS, nonpoint source • PI, Phosphorus Index

	INTRODUCTION

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS REFERENCES

 
PHOSPHORUS (P) IS ONE OF the main elements controlling algal production in aquatic ecosystems. In the low-salinity Baltic Sea, P, together with nitrogen (N), may be a factor in stimulating phytoplankton production (Granéli et al., 1990). A large part of the total P load comes from agricultural nonpoint sources (NPS). However, different fields within a watershed do not contribute equally to P export from the watershed. Sharpley and Tunney (2000) concluded that "without the capacity to quantify the importance of P sources within a watershed, remedial practices cannot be effectively targeted." In other words, the positive effects of best management practices (BMPs) are greatest if applied on P-export sensitive fields. Therefore, it is important to identify such fields for resource allocation and pollution abatement.

The complexity of the NPS control issue at the watershed scale demands simultaneous consideration of many factors, including hydrology, topography, land use, pollutant origin and properties, management practices, and soil properties. In the case of P, regardless of the great number of factors involved in P behavior, pollution occurs only when both P source and an effective transport mechanism are simultaneously present at the same site. Beegle (2000) concluded that "a key concept to managing nutrient pollution effectively is to focus on where these two factors overlap (i.e., where high source is coupled with high potential for transport)." Accuracy in the identification of critical source areas (CSAs), as well as the BMP selection process, depends on our ability to survey and process spatially variable data. Attempting such analysis by hand is very complex (with inherent error in simplifying terms) and time consuming. For these reasons quantitative and qualitative analysis tools are used to ease the process. These tools include geographic information systems (GIS), distributed parameter hydrologic and pollutant transport models, and expert systems (ES).

The Phosphorus Index (PI) provides an opportunity to identify sensitive areas within a watershed. The USDA Natural Resources Conservation Service (NRCS) developed the PI as an assessment tool to quantify and rank the vulnerability of different fields to P losses within a watershed (Lemunyon and Gilbert, 1993). The PI is based on the above-mentioned "P source–P transport mechanism" approach and important factors for P behavior are grouped into two main sets (i.e., P source factors and P transport factors). In the original PI these two sets of factors were simply added, but in the newer, modified versions, the multiplication of these two sets of factors enables identification of the CSAs where P source and P transport factors overlap. The PI as a tool for targeting CSAs has been used in several studies. Gburek et al. (2000) applied the modified PI, augmented with the hydrologic return period concept, on a small watershed within the Susquehanna River basin in east-central Pennsylvania. Heathwaite et al. (2000) applied both P and N indices in the same subwatershed in the Chesapeake Bay basin to identify areas of high and low risk for P and N loss.

The next step after CSA identification is implementation of site-specific BMPs. The choice of BMP is dependent on a site-specific probable cause for P losses and site-specific conditions. Sharpley and Tunney (2000) suggested that BMPs can be grouped into source and transport categories. However, once the CSA is identified and the surveys of its topographic, hydrologic, and soil conditions are made in a GIS environment, we can apply our knowledge and develop sets of rules, which help us to process large series of spatially variable data. These sets of rules are also known as ES. In essence, an ES is a hierarchy of rules that describes the conditions under which a set of low-level constituent information (input data) is tested to prove or oppose a given hypothesis. In our case, the hypothesis is a certain probable cause for P losses (Fig. 1) in a diagnosis ES or BMP to reduce P losses (Fig. 2) in a prescription ES. The diagnosis ES in our case identifies the probable causes for P losses, based on field and soil properties, whereas the prescription ES recommends the most appropriate BMP for P management based on field-specific probable causes and relevant field and soil properties. A rule is a conditional statement, or a list of conditional statements about the values of variables. Rule names are arbitrary, and in this study rule names were chosen to correspond with a given probable cause (Fig. 1) or BMP (Fig. 2). Finally, the conditions are specified requirements defined for each rule that must be fulfilled to prove a given hypothesis. In Fig. 1 and 2 the conditions are preceded by question marks. Abbreviations used to denominate conditions are widely used. For instance, USLE K and LS are the erodibility and length-slope factor of the Revised Universal Soil Loss Equation (RUSLE; Renard et al., 1991).

View larger version (23K): [in this window] [in a new window]   Fig. 1. Diagnosis expert system knowledge tree for identification of probable causes for P losses.

View larger version (31K): [in this window] [in a new window]   Fig. 2. Prescription expert system knowledge tree for recommendation of best management practices (BMPs) to reduce P losses.

The use of PI, ES, and GLEAMS, in conjunction with each other, forms a decision support system (DSS) for NPS pollution control planning at the watershed scale. The usefulness of a DSS is dependent upon its ability to process data into information. This ability is directly related to the amount of qualitative and quantitative knowledge incorporated into the DSS and on the quality and quantity of data that the system is able to store and process. It is important to note that a DSS is an "aid" for decision-making and that it requires understanding and reason from the decision maker(s) to generate worthwhile information, as the information is, in most cases, a reflection of the quality of the input data. Young et al. (2000) developed a DSS that predicts "the likely response of key features of the riverine environment to proposed flow management scenarios." Gilliland (1998) used a DSS including GIS, a distributed hydrologic model, and Prolog scripting language to predict NPS pollution potential in the lower Elkhorn River basin in eastern Nebraska, based primarily on bacterial loads from three different land uses. The output of their DSS compared favorably to that of field observations and to data from previously validated bacterial loading models in the watershed. A practical, computer-based system (EMA) was developed at the University of Hertfordshire to measure environmental performance by comparing the actual farm practices with what is assumed to be the best practice for a given site (Lewis and Bardon, 1998). It acts as both a decision analysis and decision support system and uses a positive–negative eco-rating scale to describe actual farm practices from an environmental point of view.

The overall objective of this study was to develop and apply a multicomponent DSS consisting of PI, diagnosis ES, prescription ES, and a hydrologic–nonpoint-source pollution model (GLEAMS) in order to (i) identify CSAs within a watershed, (ii) make a diagnosis of the probable causes, (iii) prescribe the appropriate BMP, and (iv) test the effects of prescribed BMPs.

	MATERIALS AND METHODS

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS REFERENCES

 
Site Description
The study area was the Vemmenhög watershed (900 ha), situated in southwestern Sweden (55°26' N, 13°27' E) and forming the upper reach of the Vemmenhög Stream drainage basin. Approximately 95% of its total area (855 ha) is in agricultural land use, divided among 35 farms (Hoffmann and Johnsson, 1999). Four crops dominate and a typical crop rotation is sugar beet (Beta vulgaris L.), spring barley (Hordeum vulgare L.), winter rape (Brassica rapa L.)–winter barley, and winter wheat (Triticum aestivum L.) (Kreuger, 1998). The Vemmenhög watershed was selected for developing NPS pollution modeling and management strategies for implementation on a broader scale because of its proximity to the Baltic Sea. The Baltic Sea is considered a critical asset to Sweden and neighboring countries with regard to fishing and recreational values and its eutrophication problems have been documented (Stålnacke, 1996; Enell, 1996). The Vemmenhög watershed is one of approximately 35 watersheds used to monitor and evaluate agricultural effects on water quality in Sweden (Kyllmar and Ulén, 1998). Consequently, a detailed set of data and maps representing soil properties, topography, land use, and management was gathered to properly describe site characteristics. The information includes, for example, stream network (Fig. 3a) , soil properties such as texture (Fig. 3b), soil P test values (Fig. 3c) (P content in ammonium lactate extract [0.1 M NH₄ lactate + 0.4 M CH₃COOH], i.e., P-AL), soil organic matter content (Fig. 3d), topography, saturated hydraulic conductivity and soil water retention curves, as well as crop rotation and rates and methods of fertilizer and/or manure applications. About 40% of the fields are systematically drained with tiles spaced at about 16 m and at a depth of 1 m. On the remaining area tile drains are installed in a nonregular manner following the natural drainage routes and connect isolated depressions to a culvert system (Kreuger, 1998). Open ditches were replaced in the late 1950s with this culvert system. Since backfills from digging conduct water very rapidly (Øygarden et al., 1997), a culverted stream was treated as an open stream. Soil sampling for P-AL and organic matter content determination was made according to the recommendations from the Swedish Environmental Protection Agency (i.e., with a density of one sample per 10 hectares). In total, 91 topsoil samples (0–25 cm) and 21 subsoil samples (25–50, 50–75, and 75–100 cm) were collected. The most detailed crop and P management data (Fig. 4) were available for two years (1995 and 1996) and hence the PI was computed for each of these two years.

View larger version (53K): [in this window] [in a new window]   Fig. 3. The Vemmenhög watershed: (a) water courses, (b) clay content (%), (c) phosphorus content (extracted with ammonium lactate [P-AL]), and (d) organic matter content (%).

View larger version (66K): [in this window] [in a new window]   Fig. 4. The Vemmenhög watershed: (a) land use 1995, (b) land use 1996, (c) P fertilizer and manure application (in kg P ha-1) 1995, and (d) P fertilizer and manure application (in kg P ha-1) 1996.

The Maryland Phosphorus Site Index (Coale, 2000) combines P loss potential due to site and transport characteristics and P loss potential due to management practice and source characteristics. Soil erosion, runoff class, subsurface drainage, leaching potential, distance from the edge of field to surface water, and priority of receiving water are the subcategories of site and transport characteristics used to describe the potential for P removal both by surface runoff and subsurface drainage. All subcategories, except the soil erosion subcategory, are divided into subclasses with a numerical value for each subclass. The soil erosion value is calculated with the Revised Universal Soil Loss Equation. The sum of the subcategory values represents the total site and transport value. Similarly, the management practice and source characteristics (the soil test P fertility index value, P fertilizer and/or manure application rate, and methods of application) are combined to obtain a total management and source value. Multiplying the total site and transport value by the total management and source value results in a P loss rating value (PLRV). Based on PLRV, all fields are divided into four P index rating classes: LOW potential for P movement (PLRV < 600), MEDIUM potential for P movement (600 < PLRV < 1200), HIGH potential for P movement (1200 < PLRV < 1800), and VERY HIGH potential for P movement (PLRV > 1800). IMAGINE 8.4 and Knowledge Engineer were used in this study to produce, compute, and modify all input data layers, as well as to compute and classify fields in PLRV classes. Measured data were input as much as possible and missing data were obtained from default values and the literature (Novotny and Olem, 1994).

Diagnosis ES includes seven different probable causes for P losses: high P level in soil, excessive P fertilization, high soil erodibility, low soil cover, stream proximity, moderate erodibility, and subsurface drainage (Fig. 1). The first two probable causes may be categorized as P source–related and the other five as P transport–related. It is important to emphasize that the rules in the diagnosis ES were developed in such a way that a certain probable cause would be diagnosed only if both P source and P transport criteria were met at the same area of interest (raster, field). For example, subsurface drainage as a transport-related probable cause could be identified as a probable cause only if the high P source condition was also fulfilled (Fig. 1). Similarly, high P level in soil as a source-related probable cause could be identified as a probable cause only if the efficient P transport condition was satisfied. In such a way, diagnosis ES follows the "P source–P transport mechanism" approach and refines PI calculations. "High P source" and "efficient P transport" in diagnosis ES stand for "total management and source value" and "total site and transport value" in PI, respectively.

Prescription ES consists of 10 BMPs aimed to reduce P losses: riparian buffer strips, no tillage, crop rotation, grassed waterway, reduced fertilizer application, P fertilizer incorporation, contour strip cropping, constructed wetlands, terracing, and conservation tillage (Fig. 2). The identification of the proper BMP is based on the results from the diagnosis ES and site-specific conditions such as slope. Both diagnosis and prescription ES were applied only on fields with PLRV higher than 1200 (i.e., fields that were categorized as HIGH PI class).

One of the fields categorized as HIGH PI class with a related probable cause and advised BMP was used as an example in GLEAMS simulations to determine the effectiveness of the prescribed BMP. This field is hereafter referred to as Field 16A. Field 16A is tile-drained and has an area of 3.2 ha, and an average slope of 1.3%. Based on the GIS data and according to the Swedish P-classification system (PAL), it has been classed as the highest soil-P class (>160 mg P kg^-1). The soil is characterized as a sandy loam with 17 to 18% clay and 27 to 29% silt, and 2.8% organic matter in the topsoil. The GLEAMS model is a field-scale hydrological model useful in assessing the relative effects of different agricultural management practices on nonpoint-source pollution (Knisel and Davis, 1999). It consists of three submodels: hydrology, erosion and/or sediment yield, and chemical transport. The chemical transport submodel is further divided into nutrient and pesticide components. The GLEAMS model has been used in several applications around the world (Morari and Giupponi, 1997; Gottesbüren et al., 2000; Knisel and Turtola, 2000). Also, it has been successfully tested for Swedish conditions at two different locations (Shirmohammadi et al., 1998; Ulén et al., 1998). The GLEAMS hydrology submodel was calibrated with discharge measurements from a field at the Näsbygard farm, which is situated within the Vemmenhög watershed. The actual measurements of soil physical and chemical properties and management practices were used as input data for the chosen field. The actual input data for eight years (1990–1997) were used in the GLEAMS model simulations and were repeated three times (3 x 8 = 24 yr in total) to examine the long-term effects of applied BMPs. Three different simulation treatments were conducted:

(i) Business as Usual, or continuation of the management practices that were the reasons behind the identification of this field as a critical source area;

(ii) BMP, or application of the BMP from the first year; and

(iii) INTER, or combination of the first two treatments, that is, Business as Usual in the first eight years and BMP application in the following 16 years.

	RESULTS AND DISCUSSION

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS REFERENCES

 
Phosphorus Index
The results of PI computations performed on the Vemmenhög watershed for 1995 and 1996 are shown in Table 1. All fields within the Vemmenhög watershed had PLRV values lower than 1800 and none of the fields was therefore ranked in a very high PI rating class. Only a small part of the total area had a high potential for P movement (10.4 and 5.2% in 1995 and 1996, respectively). For instance, Pionke et al. (1997) concluded that "nearly 90% of bioavailable P exported from the Brown catchment in Pennsylvania originated from less than 10% of the land area." Targeting these most sensitive areas when applying BMPs should effectively reduce P losses from the watershed. However, some fields in this study were classed in different PI classes in different years, due to the effects of P fertilizer applications and crop characteristics on PI rating. Therefore the analysis was year specific and cannot be extended across multiple years. One way to account for annual variations would be to compute PI for the whole crop-rotation period. Unfortunately, in the case of the Vemmenhög watershed, the available data were not sufficient to calculate a PI rating for the whole crop-rotation period. This raises the question about the ability of the software used for PI and DSS development to account for temporal variations. The spatial distribution of PI classes for fields in the watershed, combined for both years, is shown in Fig. 5a .

View larger version (42K): [in this window] [in a new window]   Fig. 5. The Vemmenhög watershed: (a) Phosphorus Index classes, (b) probable causes of phosphorus losses, and (c) recommended best management practices to reduce P losses. Each figure shows combined data for 1995 and 1996.

Diagnosis Expert System
The results of diagnosis ES computations performed on the Vemmenhög watershed for 1995 and 1996 are shown in Table 1. The results are reasonable knowing the P levels in soil, P fertilizer application rates, and the transport mechanisms for P movement in different parts of the watershed. Subsurface drainage was indicated as the largest single probable cause for P losses within the watershed. This is understandable, knowing that the total watershed area is tile drained (Kreuger, 1998). High P losses through subsurface drainage have been measured elsewhere (Gaynor and Findlay, 1995; Heckrath et al., 1995; Beauchemin et al., 1998; Stamm et al., 1998; Simard et al., 2000). However, according to PI, all tile-drained fields were classed within the same risk value class regarding soil drainage class, irrespective of the soil vulnerability to preferential flow losses of P. Stream proximity was a second transport-related probable cause. Lack of buffer zones, zones where no P application occurred, and the extensive culvert system within the Vemmenhög watershed were the main reasons for this. Excessive P fertilization may be somewhat overestimated as a probable cause. Namely, diagnosis ES assumes high fertilizer and/or manure application rates to be annual (due to annual based calculations), although, in reality the common practice is to apply P fertilizer or manure once every three to four years. The spatial distribution of probable causes for high-risk fields in the watershed, combined for both years, is shown in Fig. 5b.

Prescription Expert System
The results of prescription ES computations performed on the Vemmenhög watershed for 1995 and 1996 are shown in Table 1. The spatial distribution of recommended BMPs for high-risk fields in the watershed, combined for both years, is shown in Fig. 5c. The advised BMP pattern resembles the pattern of the probable causes (Fig. 5b). This is understandable knowing that the prescription of BMPs in the prescription ES knowledge tree (Fig. 4) was based on certain probable causes. Consequently, riparian buffer strips were recommended as a BMP for fields where the diagnosed probable cause was stream proximity.

The counter measure (BMP) for subsurface drainage losses was P fertilizer incorporation. This assumption was based on results of a lysimeter study in which P leaching losses were compared when P fertilizer was either incorporated, or applied on the surface of conventionally tilled and nontilled soils (Djodjic et al., 2002). Incorporation of P fertilizer resulted in a significant reduction of P leaching losses during the first year after P fertilizer application, compared with the other two treatments. This was explained by the fact that water percolating through macropores bypassed the soil matrix where the incorporated P fertilizer was located. According to Bergström and Shirmohammadi (1999), macropore or preferential flow was the dominant water and solute transport pathway in structured clay soils, such as the one used in the above-mentioned study. Soils within the Vemmenhög watershed commonly have a lower clay content (Fig. 1b), but preferential flow has been observed in soils with clay contents as low as 12% (Kladivko et al., 1991). Also, Laubel et al. (1999) concluded that preferential flow accounted for elevated dissolved and particulate P concentrations in drainage from a sandy loam soil with clay contents of 15 to 20%. Therefore, it is reasonable to assume that P fertilizer incorporation may have reduced P leaching losses from soils in the Vemmenhög watershed. Finally, reduced P fertilizer and/or manure rate was prescribed on fields where high P level in soils or excessive P fertilization were diagnosed as the main probable causes.

A relatively low number of the diagnosed probable causes (four out of seven) and prescribed BMPs (three out of ten) was probably due to the uniformity of the Vemmenhög watershed in soil properties and topography. It is important to stress that the ES selected the first probable cause–BMP for which all demands (rules) were fulfilled. Therefore, one should keep in mind the importance of the arrangement of the probable causes–BMPs within the ES knowledge tree, both during the development of ES and during the evaluation of the results.

GLEAMS Simulations
From the created PI, probable cause, and BMP maps, a field situated in the central part of the Vemmenhög watershed (Field 16A, Fig. 5c) was chosen for GLEAMS simulations. The hydrology submodel of GLEAMS was calibrated using the discharge measurements from the Näsbygård field (Fig. 5c), situated in the northeastern part of the Vemmenhög watershed. The GLEAMS model overestimated discharge during dry years, but the overall model efficiency, which was estimated with a method proposed by Loague and Green (1991), was high (0.94). The optimum value for model efficiency is 1, whereas negative values indicate poor fit. An explanation for the overestimation might be low ground water levels during dry periods. Part of the root-zone discharge simulated with GLEAMS would, under such conditions, be used for ground water recharge. Also, precipitation measured at the rain gauge located 4 km northeast of the watershed could occasionally be somewhat different from precipitation within the watershed (Hoffmann and Johnsson, 1999), especially during intensive summer rainfall.

Field 16A was categorized in the high PI ranking class in both years. In 1995 the diagnosed probable cause was stream proximity and in 1996, high P levels in soil. Accordingly, the prescribed BMP in 1995 was riparian buffer strip and in 1996 reduced P fertilizer and/or manure application. In this study, the 1996 BMP (reduced fertilizer and/or manure application) was tested in GLEAMS simulations. The excessive manure applications in a Business as Usual scenario were replaced in the other two scenarios (INTER and BMP) with lower rates that were based on crop demand of P (Table 2). Table 3 and Fig. 6 show the amount of P losses during the 24-yr GLEAMS simulation period. The recommended BMP reduced runoff P losses by 55% and sediment P losses by 71% compared with the Business as Usual scenario, if applied from the initial year. If applied after 8 yr (INTER), the reduced manure rate would decrease P losses in runoff by 31% and in sediment by 33%. On the other hand, there was no reduction in P leaching losses.

View larger version (23K): [in this window] [in a new window]   Fig. 6. Ground Water Loading Effects of Agricultural Management Systems (GLEAMS) simulations: dissolved phosphorus losses in (a) runoff and phosphorus losses in (b) sediment for the three different treatments during the 24-yr period in Field 16A in the Vemmenhög watershed.

View larger version (33K): [in this window] [in a new window]   Fig. 7. Ground Water Loading Effects of Agricultural Management Systems (GLEAMS) simulations: (a) stable and (b) active mineral phosphorus in topsoil and subsoil for the three treatments in Field 16A in the Vemmenhög watershed. Note that the lines for all three treatments at the 25- to 50-cm depth overlap (i.e., there were no differences between treatments).

	CONCLUSIONS

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS REFERENCES

However, even with the above shortcomings, the DSS is a valuable tool for overview, management, and processing of spatially variable data, and provides us with information and answers in a very short time period. Once the knowledge is developed and the necessary input data is gathered, the analysis time is significantly reduced with the use of DSS. Such a system may help us not only to find the "hot spots" and determine the proper "cure" for certain problems, but also to determine representative sites for closer monitoring. Inclusion of remote sensing and a software interface to connect different parts of DSS is probably the next step in the development. The DSS developed in this study clearly showed that using a combination GIS and NPS pollution model can help us to better allocate our resources for pollution abatement on a watershed scale.

The final test of the DSS described in this study will be its usefulness to regulators, advisors, and farmers in their everyday practice. The suggested improvements combined with a user-friendly interface should facilitate its applications.

	ACKNOWLEDGMENTS

 
This study was conducted within the multidisciplinary Swedish project FOOD 21 with financial support from the Foundation for Strategic Environmental Research (MISTRA).

	REFERENCES

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS REFERENCES

 

• Beauchemin, S., R.R. Simard, and D. Cluis. 1998. Forms and concentrations of phosphorus in drainage water of twenty-seven tile-drained soils. J. Environ. Qual. 27:721–728.[Abstract/Free Full Text]
• Beegle, D. 2000. Integrating phosphorus and nitrogen management at the farm level. p. 159–168. In A.N. Sharpley (ed.) Agriculture and phosphorus management—The Chesapeake Bay. Lewis Publ., Boca Raton, FL.
• Bergström, L.F., and A. Shirmohammadi. 1999. Areal extent of preferential flow with profile depth in sand and clay monoliths. J. Soil Contam. 8:637–651.
• Coale, F. 2000. The Maryland Phosphorus Site Index—Technical user's guide. SFM-7. Info. Publ. Maryland Coop. Ext., Univ. of Maryland, College Park.
• Djodjic, F., L. Bergström, and B. Ulén. 2002. Phosphorus losses from a structured clay soil in relation to tillage practices. Soil Use Manage. 18:1–6.
• Djodjic, F., L. Bergström, B. Ulén, and A. Shirmohammadi. 1999. Mode of transport of surface-applied phosphorus-33 through a clay and sandy soil. J. Environ. Qual. 28:1273–1282.[Abstract/Free Full Text]
• Enell, M. 1996. Utsläpp från olika källor. Belastning av kväve och fosfor på Östersjön-svenskt och internationellt perspektiv. In Kväve och fosfor i mark och vatten-en ödesfråga inför 2000-talet. Seminarium den 22–31 mars 1995. (In Swedish). K. Skogs Lantbruksakad. Tidskr. 135(3):109–118.
• ERDAS. 1999. IMAGINE 8.4. ERDAS, Atlanta, GA.
• Gaynor, J.D., and W.I. Findlay. 1995. Soil and phosphorus loss from conservation and conventional tillage in corn production. J. Environ. Qual. 24:734–741.[Abstract/Free Full Text]
• Gburek, W.J., A.N. Sharpley, L. Heathwaite, and G.J. Folmar. 2000. Phosphorus management at the watershed scale: A modification of the Phosphorus Index. J. Environ. Qual. 29:130–144.
• Gilliland, M.W. 1998. A decision support system to predict nonpoint source pollution potential. Water Res. Bull. 12:191–208.
• Gottesbüren, B., K. Aden, I. Bärlund, C. Brown, M. Dust, G. Görlitz, N. Jarvis, S. Rekolainen, and H. Schäfer. 2000. Comparison of pesticide leaching models: Results using the Weiherbach data set. Agric. Water Manage. 44:153–181.
• Granéli, E., K. Wallström, U. Larsson, W. Granéli, and R. Elmgren. 1990. Nutrient limitation of primary production in the Baltic Sea area. Ambio 19:142–151.
• Heathwaite, L., A.N. Sharpley, and W.J. Gburek. 2000. A conceptual approach for integrating phosphorus and nitrogen management at watershed scales. J. Environ. Qual. 29:158–166.[Abstract/Free Full Text]
• Heckrath, G., P.C. Brookes, P.R. Poulton, and K.W.T. Goulding. 1995. Phosphorus leaching from soils containing different phosphorus concentrations in the Broadbalk experiment. J. Environ. Qual. 24:904–910.[Abstract/Free Full Text]
• Hoffmann, M., and H. Johnsson. 1999. Test of a modelling system for estimating N leaching—A pilot study in a small agricultural catchment. In Assessment of leaching loss estimates and gross load of nitrogen from arable land in Sweden. Hoffmann Doctoral Thesis, Swedish Univ. of Agric. Sci., Uppsala.
• Kladivko, E.J., G.E. Van Scoyoc, E.J. Monke, K.M. Oates, and W. Pask. 1991. Pesticide and nutrient movement into subsurface tile drains on a silt loam soil in Indiana. J. Environ. Qual. 20:264–270.[Abstract/Free Full Text]
• Knisel, W.G., and F.M. Davis. 1999. GLEAMS: Groundwater Loading Effects of Agricultural Management Systems. Version 3.0. Users manual. USDA Agric. Res. Serv., Southeast Watershed Res. Lab., Tifton, GA.
• Knisel, W.G., and E. Turtola. 2000. GLEAMS model application on a heavy clay soil in Finland. Agric. Water Manage. 43:285–309.
• Kreuger, J. 1998. Pesticides in stream water within an agricultural catchment in southern Sweden, 1990–1996. Sci. Total Environ. 216:227–251.[Medline]
• Kyllmar, K., and B. Ulén. 1998. The Swedish monitoring programme. 1998. In L. Øygarden and P. Botterweg (ed.) Measuring runoff and nutrient losses from agricultural land in Nordic countries. TemaNord 575:67–71.
• Laubel, A., O.H. Jacobsen, B. Kronvang, R. Grant, and H.E. Andersen. 1999. Subsurface drainage loss of particles and phosphorus from field plot experiments and a tile-drained catchment. J. Environ. Qual. 28:576–584.[Abstract/Free Full Text]
• Lemunyon, L.E., and R.G. Gilbert. 1993. The concept and need for a phosphorus assessment tool. J. Prod. Agric. 6:483–496.
• Lewis, K.A., and K.S. Bardon. 1998. A computer-based informal environmental management system for agriculture. Environ. Model Software 13:123–137.
• Loague, K., and R.E. Green. 1991. Statistical and graphical methods for evaluating solute transport models: Overview and application. J. Contam. Hydrol. 7:51–73.
• Maryland Clear Water Action Plan Technical Workgroup. 1998. Maryland Clear Water Action Plan. Final 1998 report on unified watershed assessment, watershed prioritization and plans for restoration action strategies. Maryland Dep. of Natural Resour., Annapolis.
• Morari, F., and C. Giupponi. 1997. Effects of four cultivation systems for maize on nitrogen leaching 2. Model simulation. Eur. J. Agric. 6:113–123.
• Novotny, V., and H. Olem. 1994. Water quality: Prevention, identification, and management of diffuse pollution. John Wiley & Sons, New York.
• Øygarden, L., J. Kværner, and P.D. Jenssen. 1997. Soil erosion via preferential flow to drainage systems in clay soils. Geoderma 76:65–86.
• Pionke, H.B., W.J. Gburek, A.N. Sharpley, and J.A. Zollweg. 1997. Hydrologic and chemical controls on phosphorus loss from catchments. p. 225–242. In H. Tunney (ed.) Phosphorus loss to water from agriculture. CAB Int. Press, Cambridge.
• Renard, K., G. Foster, G.A. Weisis, and J.P. Porter. 1991. RUSLE—Revised universal soil equation. J. Soil Water Conserv. 46:30–33.
• Sharpley, A.N., and H. Tunney. 2000. Phosphorus research strategies to meet agricultural and environmental challenges of the 21st century. J. Environ. Qual. 29:176–181.[Abstract/Free Full Text]
• Shirmohammadi, A., B. Ulén, L. Bergström, and W.G. Knisel. 1998. Simulation of nitrogen and phosphorus leaching in structured soil using GLEAMS and a new submodel, "PARTLE." Trans. ASAE 41:353–360.
• Simard, R.R., S. Beauchemin, and P.M. Haygarth. 2000. Potential for preferential pathways for phosphorus transport. J. Environ. Qual. 29:97–105.
• Stamm, C., H. Flühler, R. Gächter, J. Leuenberger, and H. Wunderli. 1998. Preferential transport of phosphorus in drained grassland soils. J. Environ. Qual. 27:515–522.[Abstract/Free Full Text]
• Stålnacke, P. 1996. Nutrient loads to the Baltic Sea. Linköping. Studies in Arts and Sci. 146. Dissertation. Linköping Univ., Sweden.
• Ulén, B., A. Shirmohammadi, and F. Djodjic. 1998. The use of GLEAMS model to predict phosphorus losses from drained clay soils. Vatten 54:53–60.
• Young, W.J., D.C.L. Lam, V. Ressel, and I.W. Wong. 2000. Development of an environmental flows decision support system. Environ. Model Software 15:257–265.

This article has been cited by other articles:

				B. Kronvang, M. Bechmann, H. Lundekvam, H. Behrendt, G. H. Rubaek, O. F. Schoumans, N. Syversen, H. E. Andersen, and C. C. Hoffmann Phosphorus Losses from Agricultural Areas in River Basins: Effects and Uncertainties of Targeted Mitigation Measures J. Environ. Qual., November 7, 2005; 34(6): 2129 - 2144. [Abstract] [Full Text] [PDF]

				W. J. Dougherty, N. K. Fleming, J. W. Cox, and D. J. Chittleborough Phosphorus Transfer in Surface Runoff from Intensive Pasture Systems at Various Scales: A Review J. Environ. Qual., November 1, 2004; 33(6): 1973 - 1988. [Abstract] [Full Text] [PDF]

This Article Abstract Figures Only Full Text (PDF) Free Alert me when this article is cited Alert me if a correction is posted Services Similar articles in this journal Similar articles in ISI Web of Science Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via ISI Web of Science (9) Citing Articles via Google Scholar Google Scholar Articles by Djodjic, F. Articles by Ulén, B. Search for Related Content PubMed PubMed Citation Articles by Djodjic, F. Articles by Ulén, B. Agricola Articles by Djodjic, F. Articles by Ulén, B. Related Collections Surface Water Quality Best Management Practices Nutrient Management