AgEvaluate – Integrating NbS Monitoring with Microprofiling and LiDAR for Policy-Ready Insights

Description: 
This poster was presented at the Trinity School of Engineering Research & Innovation Showcase (May 27–28, 2025) in The Science Gallery, Trinity College Dublin, and summarizes the AgEvaluate project—an EPA-funded effort to assess and optimize Nature-Based Solutions (NbS) for improving water quality, biodiversity, and climate resilience in Irish agricultural landscapes.

Building on partnerships with ACRES West Connacht and the Farming for Water EIP, AgEvaluate implements a multi-scale, policy-led workflow that transforms real-world field data into actionable guidance for advisors, policymakers, and farmers. Key components include:

• Field measurements: In situ monitoring of pH, temperature, dissolved oxygen, and electrical conductivity (TDS) using portable meters.

• Laboratory nutrient analysis: Discrete analysis of water samples on a Thermo Scientific Gallery Analyzer to quantify TON-N, NO₃⁻–N, NO₂⁻–N, PO₄³⁻–P, and NH₄⁺–N.

• Microprofiling: High-resolution redox and oxygen profiling of intact peat cores with Unisense microsensors at millimeter scale.

• Geospatial mapping: QGIS integration of high-resolution LiDAR data (EPSG:2157) to detect topographic change, identify critical source areas, and evaluate NbS scalability across catchments.

• Policy-feedback loop: Formulate policy questions (e.g., “Which NbS maximize water protection per euro invested?”), select partner farms for monitoring, analyze trends and map outcomes, then consult with EPA, LAWPRO, and DAFM to refine scheme guidelines and site selection.

Expected outcomes & impact
By quantifying sediment retention, flow attenuation, water-quality improvements (turbidity and nutrient reductions), habitat restoration, and economic returns, AgEvaluate will:

• Identify cost-effective NbS tailored to diverse farming systems

• Develop GIS-based decision-support tools

• Provide evidence-based recommendations to policymakers and advisors

All datasets (sensor time series, nutrient analyses, LiDAR derivatives), data-processing scripts, and the full poster PDF are provided under a CC BY 4.0 license to ensure open access, reproducibility, and reuse.