Seeding rate (SDR) is one of the most critical production practices impacting productivity and economic return for corn (Zea mays L.) By changing SDRs in different zones within a field, herein termed as site-specific management, better economic results can be produced as the outcome of reducing SDRs in low productivity areas and increasing SDRs under high-yielding environments, relative to the uniform SDR management performed by the producer. The aim of this study was to analyze yield responses to SDR factor at on-farm research (OFR) studies with precision agriculture techniques. Five OFR corn SDRs were conducted in 2014 (two) and 2105 (three) in the central region of Kansas, US. Seeding rates for all OFR studies ranged between 40,000 to 90,000 seeds per hectare. The experimental design employed at all OFR studies was completely randomized design with three replications in strips across the field. Treatments were harvested with combine equipped with yield monitor and GPS. Yield response to SDR factor was estimated via the following equation: Yield=a+bSD+cSD2. All parameters in the fitted equation were modeled using spatial statistics by GeoDa™ software. Spatial autocorrelation of the data analyzed was considered in the spatial statistics. Agronomical Optimum Seeding rate (AOSR) and Economical Optimum Seeding Rate was calculated as: AOSD= -b/(2c) and EOSR= (Seed Price/Corn Price)-b/2c. Corn and seeds prices were expressed in U$S per kilogram and U$S per seed, respectively. Corn yield responses to SDR factor were statistically detected in 3 among all 5 sites. All parameters obtained for model calibration are site-specific (soil-weather-genotype). By conducting OFR studies in a wide array of environments, site-specific response models are generated that can incorporate inter annual temporal variability. On-farm research studies help to better understand the complexity of the soil-weather-genotype interaction and can be utilized as support decision tools when temporal and spatial data is aggregated at the local- and regional-scales.