Descriptive Fields
Following the identification and structured documentation of potential NSWRM, OPTAIN applied a systematic prioritisation process to determine which measures and combinations should be further analysed within the modelling and optimisation framework.
Prioritisation did not aim at selecting measures for real-world deployment. Instead, it served to define modelling scenarios and optimisation portfolios that would be evaluated at catchment scale.
Multi-criteria assessment
The prioritisation process considered a range of criteria reflecting environmental, agronomic and socio-economic dimensions. These typically included:
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expected water retention efficiency,
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nutrient retention performance,
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potential effects on agricultural productivity,
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cost-related indicators,
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feasibility under local soil and climate conditions.
These criteria were aligned with the performance indicators defined in the project and later used in the multi-objective optimisation process.
Rather than focusing on a single “best” measure, the approach recognised that trade-offs exist between objectives. For example, a measure that maximises nutrient retention may affect productivity or involve higher costs. Prioritisation therefore aimed to identify balanced and context-adapted portfolios suitable for simulation.
Collaborative evaluation
Stakeholder input played an important role in the prioritisation stage. Through workshops and Multi-Actor Reference Group (MARG) activities, participants contributed to:
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evaluating the perceived relevance and feasibility of measures,
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discussing potential constraints and opportunities,
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expressing preferences among alternative measure combinations.
This participatory component ensured that modelling scenarios reflected realistic agricultural contexts and stakeholder perspectives, while remaining strictly analytical.
Link to optimisation
The prioritised measures formed the basis for the multi-objective optimisation process, where different spatial allocations and combinations were systematically explored. Using optimisation algorithms, the project identified Pareto-optimal portfolios, representing efficient trade-offs between competing objectives at catchment scale.
In this way, prioritisation served as a bridge between qualitative assessment and quantitative optimisation, enabling structured comparison of alternative NSWRM strategies under current and future climate conditions.