Highly sophisticated computer models are fast and cheap in application for estimating the impacts of climate change on crop growth. Nevertheless, the models are still based on results from experiments and may carry their restrictions also to the models. To assess the degree of sustainability of particular agricultural production system in relation to environmental factors as well as to develop adoption measures, there is a need to understand quantitatively the processes determining crop production and how these are influenced by climate characteristics, environmental conditions and management practices. Field studies and long-term experimentations are one way to get this required information. Since the field studies, replicated across locations and years, are laborious and resource consuming, it takes considerable time to generate outputs for use in decision making. Additionally, they are site-specific in nature and variability in environmental conditions makes them difficult to duplicate in other places. On the other hand, crop models offer a cheaper and quicker complimentary approach and can easily evaluate a number of alternative strategies and risks in agricultural decision making. The simulation models can be used to indicate future trends and prescribe appropriate actions, such as suitable crops, best soil and water management practices, and changes in agronomic practices that maximize profit and minimize negative environmental impacts.
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