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A Decision Support System for Phosphorus Management at a Watershed Scale

Faruk Djodjic*,a, Hubert Montasb, Adel Shirmohammadib, Lars Bergströma and Barbro Uléna

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



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Fig. 1. Diagnosis expert system knowledge tree for identification of probable causes for P losses.

 


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Fig. 2. Prescription expert system knowledge tree for recommendation of best management practices (BMPs) to reduce P losses.

 


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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 (%).

 


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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.

 


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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.

 


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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.

 


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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).

 





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