Linking field-scale soil water regimes with vegetation response using CRNS and soil hydrophysical thresholds: a case study in Ireland

Reliable assessment of soil water regime at the field scale is essential for understanding plant–soil interactions in managed grassland systems, yet remains challenging due to strong spatial heterogeneity and scale mismatches between soil moisture observations and vegetation response. Point-scale sensors provide detailed local measurements but often fail to represent field-scale conditions, while integrative approaches require independent validation to ensure their relevance for agrosystem functioning.

This study presents an integrated framework combining Cosmic-Ray Neutron Sensing (CRNS) with soil hydrophysical characterisation based on Soil Water Retention Curves (SWRC) to assess soil water regime dynamics and their relationship with vegetation response. CRNS-derived volumetric water content was interpreted relative to physically meaningful hydrophysical thresholds obtained from SWRC analysis, enabling continuous classification of soil moisture conditions across wet, optimal, and water-limited regimes.

Vegetation data were used as an independent indicator of soil water status to evaluate the consistency of CRNS–SWRC-derived regimes with observable plant responses. Field-scale grass growth dynamics were compared against classified soil moisture regimes to assess whether transitions in soil water availability were reflected in changes in vegetation productivity. This comparison allowed the identification of periods where vegetation response deviated from expected soil moisture conditions, highlighting potential anomalies related to root-zone decoupling, management interventions, or sub-footprint soil heterogeneity.

The results demonstrate that the combined CRNS–SWRC approach captures seasonal and event-scale variability in soil water regimes that correspond with observed grass growth patterns. At the same time, mismatches between soil moisture regimes and vegetation response provide valuable diagnostic information, enabling the detection of anomalous conditions not evident from soil moisture data alone.

The proposed framework extends beyond soil moisture monitoring by linking integrative hydrological measurements with biological response, offering a robust tool for field-scale assessment of soil–plant water interactions. This approach supports improved interpretation of soil water dynamics in heterogeneous agricultural landscapes and provides a foundation for anomaly detection and decision support in grassland management.