Whereas, WB cyanobacteria blooms appear to be driven by relatively short-term loads of immediately available P (Michalak et al., 2013, Stumpf et al., 2012 and Wynne et al., 2013). Thus, while a recent assessment demonstrated that the Detroit River had little impact on the massive 2011 cyanobacteria bloom (Michalak et al., 2013), it does not mean that the river is not an important driver AZD6244 supplier for hypoxia; hypoxia development is a cumulative process that can be influenced
by longer term loads of both immediately available DRP and P that is made available through internal recycling mechanisms over the summer. Thus, a new loading target aimed at reducing or eliminating cyanobacteria blooms might be insufficient in both magnitude and geographic proximity to reduce hypoxia. Because the major components of the P load are now DAPT supplier from non-point sources, and because resources available to address those sources will always be limited, management efforts will be most cost effective if placed on sub-watersheds that deliver the most P. We now have the ability to identify not only the most important contributing watersheds (e.g., Detroit, Maumee, Sandusky), but also the regions within those tributary watersheds that release the most P. This knowledge should allow for more effective targeting of BMPs to high-load subwatersheds, assuming that the stakeholders in those regions are open to these
options. For this reason, research that identifies factors that drive land-use decision-making
behavior and how these motivations and behaviors vary across the watershed will be essential to help policy-makers determine the ability to meet any newly developed loading targets through implementation of spatially-targeted BMPs. For example, current farm policy is based on volunteer, incentive-based adoption of Pembrolizumab supplier BMPs. The 2014 U.S. Farm Bill includes a focus on special areas and replacing subsidies with revenue insurance, providing opportunities to employ more targeted approaches. Daloğlu et al. (in press) point out that farmer adoption will be critical, and their analysis suggests that coupling revenue insurance to conservation practices reduces unintended consequences. For example, using a social-ecological-system modeling framework that synthesizes social, economic, and ecological aspects of landscape change under different agricultural policy scenarios, Daloğlu (2013) and Daloğlu et al. (in press) evaluated how different policies, land management preferences, and land ownership affect landscape pattern and subsequently downstream water quality. This framework linked an agent-based model of farmers’ conservation practice adoption decisions with SWAT to simulate the influence of changing land tenure dynamics and the crop revenue insurance in lieu of commodity payments on water quality over 41 years (1970–2010) for the predominantly agricultural Sandusky River watershed.