Developing and evaluating the Agricultural Conservation Decision Support Tool (AgConDST)

Abstract

High levels of nutrients and sediments in the Upper Mississippi River Basin (UMRB) have been shown to contribute to the Gulf of Mexico hypoxic zone and negatively impacting ecosystem quality in the UMRB. Conservation management practices, such as Vegetative Filter Strips (VFS), grassed waterways, contour buffer strips, and other Best Management Practices (BMPs) are well known for reducing sediment pollution and water quality degradation from nonpoint sources. Although over 30 years of coordinated research and implementation efforts have been underway to reverse these adverse effects, the application of several types of BMPs at various locations throughout agricultural landscapes has only been partially successful in mitigating these negative impacts at various watershed scales. The effectiveness of these BMPs at both the field and watershed-scale depends largely on the placement of the BMPs due to the unique nature of the biophysical relationships between the conservation BMP and the resulting water quality impacts. Many of these BMPs, however, do not adequately treat surface runoff due to concentrated surface flow channels, improper cropland to buffer area ratios, or improperly placed buffers that do not intercept runoff. To effectively assess BMPs that can provide effective pollutant reduction potential, this research intends to integrate computational modeling, geospatial science data management, and analytics and spatial visualization tools to enhance the science-based and data-driven assessment of agricultural BMPs. This research developed an Agricultural Conservation Decision Support Tool (AgConDST), an advanced computational data-driven tool for the evaluation of conservation practices such as BMPs that have been known to maintain agricultural productivity and enhance ecosystems services and decision-making at multiple spatial-scales. The AgConDST framework is currently compatible with the ArcGIS desktop application. In the future, more work needs to be done to develop this framework as an independent tool and can be made compatible with other geospatial management platforms e.g., QGIS, tablet, and mobile devices. This provides a valuable education and training tool for students that can be widely disseminated and made publicly available.