Improving resource use efficiency

The impact of global climate change on agricultural production is both direct, as a result of increasing temperatures and more erratic rainfall (Parry et al., 2005), and also indirect from the pressure to produce additional yield and bioenergy while minimising carbon inputs into crop production (Powlson et al., 2005). There is therefore an urgent need to breed new crop varieties that have both improved yield (Reynolds et al., 2005a; Foulkes et al., 2007; Parry et al., 2007) and improved nutrient (Lea & Azevedo, 2006; Hirel et al., 2007) and water use efficiency (Richards, 2000; White et al., 2004). In recent years, there has been an explosion in the amount of genomic data available. Genome sequences for important crops, pests and pathogens are available (e.g. rice, Sakata et al., 2001; Agrobacterium, Wood et al., 2002; Phytoplasma, Oshima et al., 2004; Aspergillus niger, Galagan et al., 2005). In addition, extensive libraries of expressed sequence tags are available for other major crops pests and pathogens. Furthermore, transformation technologies are robust and can readily transfer genes to elite cultivars of major crops (e.g. maize, Ishida et al., 1996; rice, Hiei et al., 1994; and wheat Pastori et al., 2001; Shewry & Jones, 2005). However, the exploitation of genome data is still a major challenge to plant biologists and has proven more problematic than anticipated due to the complexity of agronomic performance (Miflin, 2000; White et al., 2004). Nonetheless, interventions in breeding based on understanding of the genetic and physiological basis of crop performance have the potential to accelerate genetic gains for yield and resource use efficiency. Metabolomic and transcriptomic data can reveal new candidate genes and in combination with the systematic exploitation of genetic resources (Reynolds et al., 2007) such approaches will provide sources of superior alleles for breeders. While on its own manipulation of individual genes (Secchi et al., 2007) is unlikely to improve crop performance, it can enable the validation of candidate genes’ contributions to component traits and these can be combined through gene pyramiding (Meiyalaghan et al., 2006). Another research area likely to have a large impact is understanding the basis of the genotype × environment (G × E) responses at the whole plant level (Reynolds et al., 2005b; Raven et al., 2005; Trethowan et al., 2005) and identification of quantitative trait loci (QTL) and molecular markers associated with G × E (Lebreton et al., 1995; Price et al., 2002; Nigam et al., 2005; Habash et al., 2007). Markers associated with yield stability – at least across a predefined range of target environments – will enable cultivars to be better targeted to agroecosystems thereby increasing resource use efficiency on a large scale.