Assessing Variability Among Potato Model Using Common Datasets.

Crop models significantly differ in the methods in which soil, plant, and atmospheric
components are mathematically expressed in source code. These differences can profoundly
influence the simulated response of crops to genetic, environment, and management inputs.
Inter-comparison studies, where models are evaluated against common datasets, can help identify knowledge gaps and modeling methodologies to improve predictions for addressing agricultural systems issues including climate change impacts and adaptation approaches. An international team is conducting studies to quantify prediction uncertainties and sensitivities to climatic factors among nine different potato models. Results include comparison of non-calibrated and calibrated simulations in different production zones including Bolivia, Burundi, Denmark, and the United States. Annual and inter-annual agronomic responses including yield, water use efficiency, and nitrogen uptake are summarized.