Managing Global Resources for a Secure Future

Crop yield is one of the most used outputs in agricultural research, especially in impact studies at both global and local level. The “in-silico” evaluation of impacts on crop yield is generally done with crop models (CM). Soil is one of the inputs of CM but in most studies at both national and global level soil is treated as a “black box” with no clear indication on how it has been parameterized. Since the soil parameterization is CM-specific previous studies quantifying multiple CM-weather-scaling-soil types did not address how much influence the choice of a wrong soil parameterization affected simulated output. They also studied only changes in simulated yield or few outputs. In this study we aimed at quantifying, for a given CM, the impact of soil parameterization through different sensitivity tests. In addition we studied the impacts on simulated yield, nitrogen balance and water use simulated outputs. The study was done at a National level, considering the whole spring barley growing area of Scotland using the Decision Support System for Agrotechnology Transfer (DSSAT). Results showed that, some DSSAT soil parameters like runoff and drainage coefficient, often ignored and not reported in simulation studies, if wrongly set caused 25% reduction in simulated yield, 70% reduction in simulated nitrate leaching and 50% reduction in simulated drainage. The sensitivity test to environmental conditions (water, nitrogen, CO₂, and temperature) indicated that the DSSAT was sensitive to changes in conditions and that overall the model was able to represent observed yield response to nitrogen fertilizer. However, the choice of the single gridded weather significantly affected the model’s simulated response to N. The correct representation of soil properties in crop models is important for impact studies and feedback from experts in soil science and experienced modellers can point out the main issues with the parameters chosen and help improving soil characterization.