Piemontese et al. (2026) A statistical modelling approach to site suitability of small agricultural reservoirs

Identification

• Journal: Agricultural Water Management
• Year: 2026
• Date: 2026-06-29
• Authors: Luigi Piemontese, Chiara Bocci, Elisa Michelotti, Tobia Papini, Giulio Castelli, Yamuna Giambastiani, Elena Bresci, Federico Preti
• DOI: 10.1016/j.agwat.2026.110577

Research Groups

• University of Florence, Department of Agricultural, Food, Environment, and Forestry Sciences and Technologies
• Giuseppe Parenti Department of Statistics Informatics and Applications, University of Florence

Short Summary

This study presents a data-driven statistical modelling approach to site suitability analysis for Small Agricultural Reservoirs (SmARs) in Tuscany, Italy. The proposed method outperforms conventional Multi-Criteria Decision Analysis (MCDA) by achieving a 71% match with a validation sample, compared to MCDA's 5%.

Objective

• To develop an occurrence-based approach to site suitability of SmARs that learns from the spatial characteristics of existing infrastructure.
• To compare this new approach with traditional MCDA methods and highlight strengths, limitations, and potential further development.

Study Configuration

• Spatial Scale: Regional scale (Tuscany, Italy)
• Temporal Scale: Historical context (construction period of most reservoirs: 1960s-1980s)

Methodology and Data

• Models used: Inhomogeneous Poisson process with marked point process extension for different SmAR dimensions.
• Data sources: Existing SmAR locations in Tuscany, Italy; land cover data from 1978 and 2019.

Main Results

• The statistical model estimates the intensity of SmAR locations as a function of six criteria: elevation, slope, soil texture (clay content), land cover, catchment area, and water availability.
• The estimated coefficients indicate that higher elevations, slopes, and clay content are negatively associated with SmAR suitability, while arable land and grassland areas have high suitability scores.
• The statistical model outperforms MCDA in terms of accuracy and agreement between the two methods.

Contributions

• This study presents a novel occurrence-based approach to site suitability analysis for SmARs that learns from observed infrastructure locations.
• The proposed method can partially compensate for missing or simplified criteria in conventional MCDA frameworks.

Funding

• No specific funding information is provided.

Citation

@article{Piemontese2026statistical,
  author = {Piemontese, Luigi and Bocci, Chiara and Michelotti, Elisa and Papini, Tobia and Castelli, Giulio and Giambastiani, Yamuna and Bresci, Elena and Preti, Federico},
  title = {A statistical modelling approach to site suitability of small agricultural reservoirs},
  journal = {Agricultural Water Management},
  year = {2026},
  doi = {10.1016/j.agwat.2026.110577},
  url = {https://doi.org/10.1016/j.agwat.2026.110577}
}