Chun Zhao, Brad Joern, James J. Camberato and Philip Hess, Agronomy, Purdue University, West Lafayette, IN
The year to year variability in optimum fertilizer nitrogen (N) rate for corn grown on the same field clearly indicates that weather drives soil and fertilizer N transformations and crop N availability. To better predict in-season optimum N rates in the field, we developed an N model that couples soil surface and subsurface N mineralization algorithms with soil and fertilizer N transformation and loss processes. Processes considered in the model include soil N mineralization, nitrification, denitrification, NH3 volatilization, and nitrate leaching, and the model is driven by temperature, soil moisture and pH. Readily available data including soil texture, pH and organic matter, daily air temperature and precipitation/irrigation, fertilizer N source, placement and timing, and crop planting/emergence date are used as model inputs. Through simple regression analyses from existing N response studies we found that yearly plant N uptake simulated from this model was highly correlated to yield data under field conditions (R2 > 0.95 for any site year, R2 > 0.80 for combined site years). Thus, we believe that this model has the potential to improve the prediction of optimum in-season fertilizer N rates compared to traditional fertilizer N recommendation strategies.