Simulating maize yield at county scale in southern Ontario using the decision support system for agrotechnology transfer model

The objectives of this study were to evaluate the ability of the decision support system for agrotechnology transfer (DSSAT) CERES-Maize model to simulate the response to applied nitrogen and soil water storage for maize (Zea mays L.) yields in Woodslee, Ontario. A second objective was to evaluate the CERES-Maize module for maize yield in five southern Ontario counties. The calibrated CERES-Maize module was used in 117 maize yield simulations involving combinations of 45 regional soil datasets and 35 weather datasets covering the five counties. The model evaluation showed a good agreement between the simulated and measured grain yields (i.e., index of agreement, d ≥ 0.96; modeling efficiency, EF ≥ 0.83; normalized root-mean-square error, nRMSE ≤ 15%). The model showed a large deviation using the default soil parameters from 0 to 0.4 m. A sensitivity analysis was made for three soil water parameters, and the calibrated soil parameters showed moderate to good agreements for total soil water storage in the 0–1.1 m soil profile. The model resulted in moderate to good agreement between the simulated and the measured above-ground biomass across growing seasons. There were significant yield differences across the soil types. Drought periods in August 2010 resulted in lower yields in 2010 compared with 2011 and 2012. The simulated average maize yields at each county matched well with the measured data for 2010–2012 except for lower estimated yields in Lambton county in 2010. We concluded that DSSAT CERES-Maize can adequately simulate regional maize yields using the CERES-Maize module calibrated to regional soil and daily weather databases.