Projects per year
Abstract
Nutrients are essential resources for food production but are used inefficiently, and thereby they pollute inland and coastal waters and are lost into the oceans. Nutrient conservation by retention and consecutive reuse would prevent nutrient losses to the atmosphere and downstream ecosystems. We present Smart Nutrient Retention Networks (SNRNs) as a novel management approach to achieve nutrient conservation across networks of connected waterbodies through strategic water quality management. To present the key features of SNRNs, we review existing knowledge of nutrient retention processes in inland waters, water quality management options for nutrient conservation, and nutrient retention models to develop SNRNs. We argue that successful nutrient conservation, even at a local level, through SNRN management strategies requires clearly formulated goals and catchment-wide system understanding. Waterbody characteristics, such as hydraulic residence time and the presence of macrophytes, shape local nutrient retention with potential network-wide cascading effects of improved water quality and are therefore key targets of SNRN management strategies. Nutrient retention models that include the self-reinforcing feedback loop of ecological water quality, nutrient retention, and nutrient loading in networks of inland waters in relation to management options can support the development of SNRNs. We conclude that SNRNs can contribute to sustainable use of nutrients in human food production.
Original language | English |
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Pages (from-to) | 138-153 |
Number of pages | 16 |
Journal | Inland Waters |
Volume | 12 |
Issue number | 1 |
Early online date | 2021 |
DOIs | |
Publication status | Published - 2022 |
Keywords
- Plan_S-Compliant_OA
- national
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Dive into the research topics of 'Smart Nutrient Retention Networks: a novel approach for nutrient conservation through water quality management'. Together they form a unique fingerprint.Projects
- 1 Finished
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NWO - Predicting and preventing emergent cyanobacterial blooms in complex man-made Dutch hydrological networks on daily and yearly time scales
01/04/2019 → 31/05/2024
Project: Research
Datasets
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Data for: Smart Nutrient Retention Networks
van Wijk, D. (Creator), Teurlincx, S. (Creator) & Brederveld, B. (Creator), Figshare, 21 Apr 2021
DOI: 10.6084/m9.figshare.14459575.v1
Dataset