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Pay-for-Performance Conservation Using SWAT Highlights Need for Field-Level Agricultural Conservation

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

Citation:  Transactions of the ASABE. 60(6): 1925-1937. (doi: 10.13031/trans.12379) @2017
Authors:   Rebecca Logsdon Muenich, Margaret M. Kalcic, Jonathan Winsten, Kristin Fisher, Monica Day, Glenn O’Neil, Yu-Chen Wang, Donald Scavia
Keywords:   Agricultural conservation, Pay-for-performance, Phosphorus, River Raisin, SWAT, Western basin of Lake Erie.

Abstract. Pay-for-performance (PFP) is a relatively new approach to agricultural conservation that attaches an incentive payment to quantified reductions in nutrient runoff from a participating farm. Similar to a payment for ecosystem services approach, PFP lends itself to providing incentives for the most beneficial practices at the field level. To date, PFP conservation in the U.S. has only been applied in small pilot programs. Because monitoring conservation performance for each field enrolled in a program would be cost-prohibitive, field-level modeling can provide cost-effective estimates of anticipated improvements in nutrient runoff. We developed a PFP system that uses a unique application of one of the leading agricultural models, the USDA‘s Soil and Water Assessment Tool, to evaluate the nutrient load reductions of potential farm practice changes based on field-level agronomic and management data. The initial phase of the project focused on simulating individual fields in the River Raisin watershed in southeastern Michigan. Here we present development of the modeling approach and results from the pilot year, 2015-2016. These results stress that (1) there is variability in practice effectiveness both within and between farms, and thus there is not one “best practice” for all farms, (2) conservation decisions are made most effectively at the scale of the farm field rather than the sub-watershed or watershed level, and (3) detailed, field-level management information is needed to accurately model and manage on-farm nutrient loadings.

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