We advocate a hybrid approach for obtaining input data for model calibration and validation. Results showed that barriers to effective simulations exist because, in most instances, the input data, like climate, soil, farm management practices, and cultivar characteristics, were generally incomplete, poor in quality, and not easily accessible or usable. This review looked at the barriers to crop simulation, relevant sources from which input data for crop models can be sourced, and proposed a framework for collecting input data. To date, no review has looked at factors inhibiting the effective use of crop simulation models and complementary sources for input data in South Africa. Even when a suitable choice of a crop simulation model is selected, data limitations hamper some of the modelsвЂ™ effective role for projections. In some cases, available input data may not be in the quantity and quality needed to drive most crop models. A constant challenge to crop model simulation, especially for future crop performance projections and impact studies under varied conditions, is the unavailability of reliable historical data for model calibrations. A broad scope of crop models with varying demands on data inputs is being used for several purposes, such as possible adaptation strategies to control climate change impacts on future crop production, management decisions, and adaptation policies.

Apsim crop model