GIS and crop simulation modelling will see increasing use in climate change research and in applications of research in decision making. Maps are especially valuable for allowing people to understand how climate change impacts, as well as possible adaptations vary, across the landscape.
Examples highlighted in this review illustrate how the combination of GIS and crop models may assist with policy and breeding decisions in relation to climate change. Knowledge surrounding the potential shifts of abiotic and biotic stresses can help guide prioritization and targeting of key traits within crop breeding programmes. Increasingly, options exist to undertake analysis under a range of future climate scenarios that incorporate data from a range of sophisticated GCMs. Such an approach can lead to probabilistic outputs that can be used to guide decisions regarding the likely importance of specific traits in different geographic regions in the future. In combination with secondary data sets (e.g. crop distributions and demographic data) this can provide useful indicators regarding likely focus areas for important traits (e.g. drought and heat stress). Valuable information, particularly from crop models, may also be obtained on the potential value of specific adaptation mechanisms - either in terms of phenology or crop management.
Similarly, for decisions relating to the conservation of plant genetic diversity and plant genetic resources, outputs from a GIS/ modelling-based approach can provide useful insights. The case studies highlighted here illustrate how priority regions, either for in situ conservation of important wild relatives or for prioritized collection efforts for ex situ conservation, can be identified. In both the conservation of plant genetic resources and the priority setting of breeding traits the lead time to obtain the desired results (e.g. a new variety or adequate protection of a priority region) can be considerable. The application of GIS/model-ling technology within a future climate framework as outlined in this review is one way that can guide decision making on an appropriate time frame.
Limitations of the two technologies per se relate to our incomplete knowledge of physiological processes, the availability and accuracy of data, and implementation of the tools through software systems. Both technologies may provide useful insights for future decision making, but it is unlikely that they will capture in totality the full complexity and unpredictability of a rapidly changing climate.
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