Advances in Crop Simulation Modelling

Dear Colleagues,

In contrast to statistical models, process-based crop simulation models consider dynamic interactions between environment, genotype, and management (including but not limited to agricultural water) factors, something that justifies their application in decision making, in the assessment of the impacts of climate change/variability, and management practices on productivity and environmental performance of alternative cropping systems, to promote better and sustainable agriculture. However, application of these models is often hindered by limited input data availability (such as climate, cultivar and soil characteristics, and management practices) for model calibration and testing and extensive computing time.

Although it is well recognized that the choice of model calibration strategy and incomplete/ poor in quality/not easily accessible input data have implications on the overall reliability of the crop model simulations, only few attempts have been made to quantify errors in crop simulation results related to the above-mentioned issues on a variety of spatial scales (from field to large area applications).

In this context, this Special Issue aims to highlight the challenges of producing locally relevant and climate informed results from crop simulation models, promoting this way their effective use, across various time frames (from seasonal to future climate change) for agriculture, under historical and future climate conditions.

Dr. Mavromatis Theodoros
Guest Editor

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