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The Agricultural BMP Database - A Growing Repository of Field Performance Data

Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan

Citation:  2016 10th International Drainage Symposium Conference, 6-9 September 2016, Minneapolis, Minnesota  .(doi:10.13031/IDS.20162493032)
Authors:   Marc A. Leisenring, Jane Clary, Eric Strecker, Jon Jones
Keywords:   best management practice, conservation practice, data analysis, decision support, field scale, meta-analysis, monitoring, water quality.

Abstract. The Water Environment & Reuse Foundation, the National Corn Growers Association, and the United Soybean Board are supporting a collaborative effort to expand the International Stormwater BMP Database project to include agricultural best management practices (BMPs), such as controlled drainage, cover crops, and conservation tillage. Selection of appropriate BMPs for a site requires an understanding of the types of practices that are compatible with and effective for a given crop, climate, soil type, and landform. A populated Agricultural BMP Database (AgBMPDB) will provide the technical and quantifiable basis for recommending BMPs that improve water quality, minimize impacts to operations, and support long-term profitability and sustainability.

The AgBMPDB includes performance data and metadata that document the many variables that affect BMP performance, such as geographic area, field conditions (e.g., soils, slopes), in-field practices (e.g., tillage, nutrient management) and edge-of-field practices (e.g., buffers, constructed wetlands). The initial release of the AgBMPDB focuses on row crops, particularly corn and soybeans. The initial data summary indicates that no-till and riparian buffers can provide significant reductions in phosphorus and sediment loads. Nutrient management and controlled drainage can provide significant reductions in nitrate loads. Cover crops do not show significant pollutant reductions, but there is wide variability among the studies indicating the effects are confounded by other variables. As the database grows, more advanced meta-analyses of the data contained in the database may be conducted and eventually support science-based decisions. The initial analysis and complete database can be downloaded in Microsoft Access from

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