An Integrated Spatial Fuzzy-Based Site Suitability Assessment Framework for Agricultural BMP Placement

Assigning crisp class boundaries to landscape features can result in the loss of vital information for land evaluation objectives, especially when these boundaries lack clear definitions. This challenge becomes particularly pronounced when land suitability is assessed for implementing agricultural best management practices (BMPs)—conservation measures aimed at reducing the environmental risks of farming activities to aquatic ecosystems while simultaneously achieving water quality and economic objectives. To address the limitations associated with Boolean suitability assessment frameworks, we have introduced an integrated spatial, fuzzy-based land evaluation framework that considers a range of hydrological and economic determinants for BMP placement. By employing data-driven fuzzy membership functions and overlay operators, this framework generates a joint suitability index for BMP placement across agricultural watersheds. The application of the proposed framework to the Thames River Watershed in southwestern Ontario, Canada, produced the first joint suitability index of the watershed. Further analysis of the average farm-level joint suitability scores identified statistically significant clusters of highly suitable and unsuitable lands for BMP placement, with 85% of highly suitable lands being situated in the upper basin areas. The proposed framework is adaptable to various agricultural production geographies, especially in data-limited environments, allowing for strategic BMP placement to mitigate the global impacts of anthropogenic nutrient loadings on aquatic ecosystems. For optimal results, context-specific applications should prioritize research on locally relevant fuzzy membership functions and BMP implementation drivers.