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Assessing the Effectiveness of Winter Cover Crops for Controlling Agricultural Nutrient Losses
Authors:Elias Getahun  Laura L Keefer
Institution:1. Illinois State Water Survey, Prairie Research Institute, University of Illinois at Urbana-Champaign, Champaign, Illinois, USA;2. Illinois State Water Survey, Prairie Research Institute, University of Illinois at Urbana-Champaign, Champaign, Illinois, USA

Contribution: Resources, Visualization, Writing - review & editing

Abstract:A nutrient loss reduction strategy is necessary to guide the efforts of improving water quality downstream of an agricultural watershed. In this study, the effectiveness of two winter cover crops, namely cereal rye and annual ryegrass, is explored as a loss reduction strategy in a watershed that ultimately drains into a water supply reservoir. Using a coupled optimization-watershed model, optimal placements of the cover crops were identified that would result in the tradeoffs between nitrate-N losses reduction and adoption levels. Analysis of the 10%, 25%, 50%, and 75% adoption levels extracted from the optimal tradeoffs showed that the cover crop placements would provide annual nitrate-N loss reductions of 3.0%–3.7%, 7.8%–8.8%, 15%–17.5%, and 20.9%–24.3%, respectively. In addition, for the same adoption levels (i.e., 10%–75%), sediment (1.8%–17.7%), and total phosphorus losses (0.8%–8.6%) could be achieved. Results also indicate that implementing each cover crop on all croplands of the watershed could cause annual water yield reduction of at least 4.8%, with greater than 28% in the months of October and November. This could potentially be detrimental to the storage volume of the downstream reservoir, especially in drought years, if cover crops are adopted in most of the reservoir's drainage area. Evaluating water yield impacts, particularly in periods of low flows, is thus critical if cover crops are to be considered as best management practices in water supply watersheds.
Keywords:agricultural nutrient losses  best management practices  cover crops  hydrologic and water quality modeling  mulit-objective evolutionary algorithms (MOEA)  nutrient loss reduction strategy  watershed systems modeling
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