user2016

Genetic resources consist of genes and genotypes with patterns reflecting their dynamic adaption to changing environmental conditions. Detailed understanding of these patterns will significantly enhance the potential of developing crops with adaptive traits to climate change. Genetic resources have contributed in the past to about 50 percent increase in crop yields through genetic improvements, further improvement and development of climate change resilient crops will largely depend on these natural resources. However, the datasets associated with these resources are very large and consist mostly of records of single observations or/and continuous functions with limited information on key variables. Analysis of such complex and large datasets requires new mathematical conceptual frameworks, and a flexible evolving platform for a timely and continuous utilization of these resources to accelerate the identification of genetic material or genes that could be used for improving the resilience of food crops to climate change. In this global collaborative research and during the development of the theoretical framework, numerous modelling routines have been tested, including linear and nonlinear approaches on the R platform. The results were validated and used for the identification of sources of important traits such as drought, salinity and heat tolerance. This paper presents the conceptual framework with applications in R used in the identification of crop germplasm with climate change adaptive traits. The paper addresses the dynamics as well as the specificity of genetic resources data, which consists not only of records of mostly single observations but also functional data.