Farmers can influence their neighbors’ key decisions and operational practices regarding land management. These influences operate through sharing common natural and socioeconomic constraints, but also through various social mechanisms, including observations of each other’s practices, shared resources, and shared vulnerabilities to climate extremes or disturbance agents. This results in similarities in cropping patters, technical efficiency, drainage practices, irrigation systems, and conservation enhancements, all of which influence the water quality of receiving systems. These practices are often driven by imitation within local networks, reflecting the principle that “everything is related to everything else, but near things are more related than distant things”. Spatial data and its scale do matter in determining the dependencies despite the spatial heterogeneity across region. This study uses spatial information on farm practices to investigate the spatial dependencies at both farm parcel and county levels across Great Lakes Region (GLR). By incorporating previously overlooked variables such as cropping patterns, crop rotation, irrigation, tile drainage, and conservation easement programs, this study analyzes the extent and influence of farmer networks with each practice under variant neighborhood sizes. Using Exploratory Spatial Data Analysis (ESDA) tools like Local Indicators of Spatial Autocorrelation (LISA) and Moran’s I, spatial aggregations and optimal neighborhood sizes are highlighted with variations in granularity. We then assess how risks to water quality impairment vary among neighborhood assignments. These insights will aid land-use planners and agricultural authorities in implementing spatially targeted interventions, whether at the parcel or county level, and guide researchers in understanding spatial dependencies in farming practices across the GLR.