**3. Summary and discussion**

Veith et al. [1] optimized BMP locations in a watershed of 1,014 ha located in the State of Virginia. An approach using the universal soil loss equation (USLE) with a sediment delivery ratio was used to estimate sediment loads, and GA was used to optimize BMP locations in the study area. BMPs were, of course, optimized by pollution reduction and BMP implementation costs. However, distinctive feature of the study was that the variance of crop production by BMP implementations was considered by two additional criteria. One was that the preference of feed production and nutrient management requirement was applied to the farms; the other

In the study performed by Srivastava et al. [39], two BMP optimization processes were compared which used design storm and continuous climate data with a model to estimate pollutant loads. An Annualized Agricultural Nonpoint Source pollution model (AnnAGNPS) [40] was used to estimate sediment, sediment nitrogen, dissolved nitrogen, sediment organic carbon, and sediment phosphorus in a watershed of 725 ha located in Northumberland County, Pennsylvania. The study area was comprised 47% cropland. Fifteen current crop rotations were considered, two design storms (69.85 mm as 2 year return period storm event, and 88.90 mm as 5 year return period storm event), and climate data for five years were used for BMP optimizations, since the hypothesis of the study was that a BMP scheme with an optimization process using accumulated pollutant loads from continuous simulation would be more applicable than the process using pollutant loads from several critical (or extreme) storms. Based on the results supporting the hypotheses, the authors suggested that long-term pollutant loads from continuous simulations need to be considered in a BMP optimization

A simple technique to optimize BMPs in a watershed was used by Park et al. [18]. They

computed by Equation 1. Sophisticated techniques (e.g., a genetic algorithm) are often used in BMP optimization processes. The researchers, however, performed the optimization process by a straightforward approach using BMP implementation costs for unit mass reduction (or cost per 1 kg reduction of pollutant). The study had two applications with potential area for BMPs. One was that it is possible to apply a filter strip of up to 100% of the agricultural area

), the other was that it is possible to apply a filter strip on up to 10 km2

In the alternate application, the estimated annual cost was \$17,400, which resulted from \$7,650 for 10 km2 of a filter strip in the agricultural area, \$7,710 for 10 km2 of reduced tillage system

applications were to demonstrate the fact that BMP scenarios and implementation costs can

In this section, several recent research studies optimizing BMPs were introduced. Optimization techniques are complex but are used widely to solve problems; GAs, for example, have been applied in BMP optimizations with various hydrologic models. On the other hand, BMP optimizations have been performed by adopting a straightforward approach based on BMP

, based on annual BMP implementation costs

filter strip at an estimated annual cost of \$12,870.

of vegetative filter strip in the urban area. The

of agricultural area. In the first application, pollutant reduction

and reduce

was that it avoided applying BMPs to a few farms.

optimized BMPs for a watershed of 129.1 km2

met the requirement for application of 17 km2

in the agricultural area, and \$2,040 for 4 km2

analyses.

180 Agroecology

(79 km2

tillage systems in up to 10 km2

vary by watershed conditions.

NPS pollution has caused water quality degradation in streams and rivers, therefore, various research projects were concerned to perform NPS reduction. Research indicates that agricul‐ tural areas were typically major source of NPS in watersheds; therefore, there is a need to perform BMP implementations. Models (or computer software) and sophisticated techniques were often used to suggest optimized BMPs for watersheds. It can be stated that the BMP optimization processes are typically composed of four components: selecting available BMPs for the watershed (or site), gathering and computing annual BMP implementation costs, identifying optimization technique, and selecting a model to estimate pollutant loads and the impact of BMPs. The first two components are site-specific, since some BMPs have a limited application in certain watersheds, and BMP costs vary by location. As the researchers men‐ tioned, BMPs optimized in other research studies cannot be selected and implemented identically without consideration of regional characteristics. Pollutant behaviours can differ by the locations of the source area in a watershed; thus, the locations of BMPs would be one of the important factors to consider. To summarize, BMP optimization processes should answer the following questions: 'what BMPs need to be selected?', 'what size of BMP needs to be applied?', 'where do BMPs need to be placed?', and 'how much does it cost to implement BMPs?'. The process will be very complex and will require a lot of effort; an optimization technique would therefore be required to examine varying BMP impacts, and this is why optimization techniques are often employed. Although optimization techniques provide convenience in BMP evaluations, recognizing limitations in hydrology models and optimiza‐ tion techniques is still required.
