Static (i.e. texture and soil depth) and dynamic (i.e. soil water, temperature) factors play a role in determining field or subfield economically optimal N rates (EONR). We used 50 nitrogen (N) trials from Argentina at contrasting landscape positions and soil types, various soil-crop measurements from 2012 to 2017, and statistical techniques to address the following objectives: a) characterize corn yield and EONR variability across a multi-landscape-year study in central west Buenos Aires, Argentina, b) quantify the relative importance of the dynamic versus static factors, and c) develop predictive models to assist site-specific N management in that region. Results indicated that EONR in this region varies with a coefficient of variation of 67% (range: 0 to 260 kg N ha-1). Yield levels varied less than the EONR with a coefficient of variation of 27% (range: 3.8 to 17 Mg ha-1). Dynamic factors explained about 47% of the spatial and temporal variability in the EONR and static variables explained 20% of the observed variation. Multi-regression analysis considering both static and dynamic factors captured between 60 and 71% of variability in EONR and corn yield. Model performance was better for yield (MAE, mean absolute error, ~1 Mg ha-1) than for EONR (MAE of 39 kg N ha-1). The number of rain events greater than 20 mm accumulated from planting to flowering and from planting to harvest and the amount of residue were the most important predictors of the variability (among ~ 60 variables explored). This analysis advances our understanding on the critical factors influencing EONR and yield to support development of decision N management tools to aid precision agriculture goals and strength current N guidelines.