Defining the genetic basis of barley metabolite content to improve nutrient use efficiency, crop quality and resilience with reduced inputs

Barley is a crop of great importance with respect to both spring malting barley for the renowned Scotch whisky industry, and winter barley for animal feeds. Only high-quality barley from a limited number of varieties is taken forward to malting and distilling, therefore greatly influencing market value.

Barley is regarded as a high-input cereal and therefore contributes significantly to the overall carbon footprint of whiskies and beers. Optimum nitrogen (N) levels promote the enzymatic breakdown of starch in raw grains to sugars during malting. The current solution is the addition of N fertilisers which enhance yield and assure quality. However, the energy costs of producing N fertiliser and an unbalanced N cycle in soils which produces ghg emissions in the form NOx contribute most to the unfavourable environmental footprint. Improving N recovery and utilisation will reduce the need for inputs and reduce pollution (key to the green recovery). The aim of the PhD will be to assess and improve our understanding of the genetic and metabolic basis of high nitrogen use efficiency and photosynthetic capacity in barley, whilst producing grains with high distilling quality.

Key to barley quality is the capacity to maintain carbon assimilation and export to developing grains under a range of conditions. This requires constant metabolic adjustment in response to environmental variation. A first step towards breeding metabolically resilient barley will be to define the genetic architecture underpinning the optimisation of metabolism. A further objective is to link an understanding of metabolic resilience to key yield and quality traits. To achieve this, barley populations will be screened for high photosynthetic rates and efficient grain import, under reduced N inputs and with alternative fertilisers (e.g. municipal compost, distillery co- products). This data will be used in a genome wide association study (GWAS) to identify Quantitative Trait Loci (QTL) and candidate...