Sensitivity of 4m Maize Model to the Inaccuracy of Weather and Soil Input Data

The accuracy of a crop model is judged mostly by how precise it is in estimating the production. The preciseness of a crop model is determined on one hand by the authenticity of the algorithms describing the processes of the real world, while on the other hand by the quality of its input data. The goal of this study was to test how sensitive crop models are to errors occurring in the measurement of key weather and soil inputs. The key weather and soil related input data for most crop models are air temperature, solar radiation, precipitation, saturated water content, drained upper limit and wilting point. The level of error in air temperature, radiation, and precipitation measurements were reported to be ±0.2 C, ±2% and ±3% respectively by the Hungarian Meteorological Service. An other study reported 0.008 cm/cm level of error in determining the drained upper limit. The question we asked was: ‘To what extent do the yield and biomass change due to this level of inaccuracies in weather and soil inputs?’ The uncertainty caused by the errors of the measured weather elements was found to be 4.7 and 6.9% for the calculated biomass and yield, respectively. The soil parameter errors caused smaller uncertainties in the simulation results. On an average we got 2.3% uncertainty for the biomass and 3.2% uncertainty for the yield. The effect of weather and soil data uncertainties can both strengthen and weaken each other. In certain cases the uncertainty of the simulation could be over 15% due to errors in weather and soil data.