variables and pathways were preserved, and less sensitive factors were either 

 omitted or combined. 



The general structure of SEMM, illustrated in figure 8, is comprised of 

 15 state variables organized into 5 groups: 1) the Autotrophs or photosynthetic 

 organisms all competing for limited light and nutrient resources; 2) the Sedi- 

 ments and their associated chemistry; 3) the Water with its dissolved nutrients 

 and herbicides, as well as suspended sediments (seston); 4) the Herbivorous Inver- 

 tebrates at the lower end of the food-chain; and 5) the Carnivorous Fish at the 

 top of the food-chain. These state variables are driven by 11 seasonally varying 

 external forcing functions. SEMM includes 2 new state variables (aqueous and 

 adsorbed herbicide, atrazine) not occurring in the ecological submodels but 

 included here because of the potential importance in resource management. The 

 differential equations which formalize this model are essentially similar to 

 those used in the detailed submodels. However, they tend to be less mechanistic 

 and more linear in form. This is consistent with the concept of increasing 

 linearity of systems with increasing degree of aggregation (e.g., Patten 1975; 

 Odum 1983). 



Multiple simulation experiments with SEMM allowed us to consider the 

 relative effects of herbicide, sediment, nutrient loading on SAV production 

 (fig. 9). Here, growth of SAV exhibits little response to changes in herbi- 

 cide loading from the watershed. Sediment inputs produce a more dramatic effect 

 on SAV; however, it appears that much of the total estuarine sediment loading 

 is derived from natural processes such as shore erosion and is therefore less 

 managable. Nutrient (and in particular nitrogen) loading at low levels causes 

 an enhanced SAV growth, whereas reduced SAV production results from inputs greater 

 than estimated 1960 rates. The reduction in SAV photosynthesis at high nitrogen 

 levels results from enhanced growth of planktonic and epiphytic algae, which 

 effectively reduce light available to SAV. 



Management Options and Socio-Economic Tradeoffs 



Results of SEMM simulations were integrated into a larger analytical 

 framework in which the socio-economic consequences of several management options 

 were evaluated using the framework presented in figure 7. Preliminary estimates 

 of economic and ecological trade-offs between agriculture and fisheries were made 

 for selected management scenarios. Agricultural costs and estuarine benefits 

 are summarized in table 1 for three such scenarios: 1) Reversion back to 100 

 percent "conventional" tillage from the current 50/50 split with minimum tillage; 

 2) 25 percent reduction in fertilization rates; and 3) 10 percent reduction 

 of area in cultivation. We have estimated costs and benefits in economic 

 ("surplus value") terms. In table 1 economic benefits and costs ranged from 

 $20,000 to $900,000 and benefit:cost ratios were always substantially less 

 than 1.0. The impact on agriculture was at least 3 times greater than on the 

 estuarine resources, but given the fact that these analyses are very preliminary 

 approximations, management options with ratios approaching 0.3 should probably 

 be further considered for insights and implications. The relative impact of 

 each cost or benefit on agricultural and fisheries sectors of the regional 

 economy was also calculated as the percentage of "gross sectorial product" for 

 the respective sector. It can be seen that, while the gross economic effects 

 are greater for agriculture than for fisheries, the relative impacts on the 

 regional sectorial economy are generally equal or greater for fisheries (table 

 1). This latter perspective suggests that while there might be short-term 



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