Abstracts of the International Workshop on Applied Mathematics and Omics Technologies for Discovering Biodiversity and Genetic Resources for Climate Change Mitigation and Adaptation to Sustainable Agriculture in Drylands - Damania, Ardeshir B.; Dayanandan S; Bari, A. (Rabat Institute, 2014)
Innovative approaches using applied mathematics for agricultural genebank mining for climate change resistant traits improve the effectiveness of breeding programs and of food security for dry areas. Dry areas represent more than 40% of global land cover and are home to over 2.5billion people.Crop cultivars with improved tolerance to heat and drought, and resistance to emerging virulent pests and diseases are urgently needed to sustain and increase agricultural productivity under the current changing climatic conditions in drylands.Identifying stress tolerance and resistance traits from more than seven million accessions held in the world’s 1,700 major agricultural genebanks, is not a trivial exercise. Today, developing innovative approaches to mining these agricultural gene banks is more important than ever.The latest innovations include methods that use applied mathematics – including the Bayes-Laplace inverse and the Stone-Weierstrass approximation theorems in combination with phenomics and genomics – for rapid and cost-effective identi?cation of crop plants with climate change adaptive genetic traits. Applying these approaches as a standard practice for genebank mining in the world's breeding programs will will improve the effectiveness of these organizations help accelerate crop improvement, to maintain biodiversity, food security, and improve livelihoods of communities living on teh world's marginal lands and dryland areas.