Abstract
Water and nutrient-use efficiencies are becoming increasingly important for several economic and environmental reasons: • As pressure increases on global food prices and supply, a balance is needed between food production and the maintenance of healthy and diverse ecosystems. • Water is the most important factor limiting crop production, and water availability during the UK growing season is predicted to decline. Water abstraction for field crop production needs to be balanced with maintaining surface water flows and ground water levels. • Nitrogen inputs are a major source of CO2 emissions • Leaching of nitrogen and phosphorus from soils results in poor water quality. • There have been steep increases in the prices of nitrogen and phosphorus fertilizers, and supplies of phosphorus are non-renewable. Existing elite crop varieties have been developed without regard for water and nutrient use efficiencies, and there is now a need to develop new varieties that can maintain productivity with lower inputs. Such varieties will allow more sustainable agriculture. In this project we will use a new technique known as genome wide association mapping (GWAM) to seach for plant genes that are assocated with increased water and nutrient use efficiency. The technique has become possible due to technological advances in genotyping that have allowed the generation of large data sets that describe, in great detail, the genetic differences between hundreds of different accessions of the model plant Arabidopsis thaliana. By measuring traits in these accessions, and then searching, using statistical methods, for associations between particular genetic loci (positions of genes on chromosome), it will be possible to identify loci that control the traits of interest. Once loci are identified, varients at the orthologous loci can be evaluated in crop plants, and specific gene varients tested in crops to see if they confer increased resouce use efficiency. In this 6 month project we will collect together and organise existing data sets, generate programs and protocols for data analysis, and perform GWAM to identify candiate genes. The approach will be useful to others as a general approach to identifying genes that control any plant trait.
United Kingdom