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Despite decades of policy that strives to reduce nutrient and sediment export from agricultural fields, surface water quality in intensively managed agricultural landscapes remains highly degraded. Recent analyses show that current conservation efforts are not sufficient to reverse widespread water degradation in Midwestern agricultural systems. Intensifying row crop agriculture and increasing climate pressure require a more integrated approach to water quality management that addresses diverse sources of nutrients and sediment and off-field mitigation actions. We used multiobjective optimization analysis and integrated three biophysical models to evaluate the costeffectiveness of alternative portfolios of watershed management practices at achieving nitrate and suspended sediment reduction goals in an agricultural basin of the Upper Midwestern United States. Integrating watershed-scale models enabled the inclusion of nearchannel management alongside more typical field management and thus directly the comparison of cost-effectiveness across portfolios. The optimization analysis revealed that fluvial wetlands (i.e., wide, slow-flowing, vegetated water bodies within the riverine corridor) are the single-most cost-effective management action to reduce both nitrate and sediment loads and will be essential for meeting moderate to aggressive water quality targets. Although highly cost-effective, wetland construction was costly compared to other practices, and it was not selected in portfolios at low investment levels. Wetland performance was sensitive to placement, emphasizing the importance of watershed scale planning to realize potential benefits of wetland restorations. We conclude that extensive interagency cooperation and coordination at a watershed scale is required to achieve substantial, economically viable improvements in water quality under intensive row crop agricultural production. ; NSF under the Water Sustainability and Climate Program (WSC) through an Observatory grant [EAR1209402]; WSC Science, Engineering and Education [EAR-1415206]; US Department of Agriculture-Agriculture and Food Research Initiative (USDA AFRI) National Institute of Food and Agriculture grant; USDA National Resource Conservation Service grant [CPT0011193] ; Published version ; This research was funded by the NSF under the Water Sustainability and Climate Program (WSC) through an Observatory grant (EAR1209402) : REACH (Resilience under Accelerated Change) , a WSC Science, Engineering and Education for Sustainability Fellows grant (EAR-1415206) to A.T.H., US Department of Agriculture-Agriculture and Food Research Initiative (USDA AFRI) National Institute of Food and Agriculture grant to S.R., and a USDA National Resource Conservation Service grant to P.B. (CPT0011193) . Special thanks to Iowa State's Center for Agriculture and Rural Development for model integration and executing the optimization algorithm. ; Public domain authored by a U.S. government employee