Yellow Wheat Blossom Midge (YWBM) is a poorly understood and often under-reported insect pest of wheat, the UK's most widely grown crop. Midge larvae feed on the wheat flower, preventing grain formation and leading to significant yield losses. All wheat varieties are reported to be susceptible to this pest. In some years, the ideal conditions required for adult midges to emerge from dormancy in the soil, mate and lay eggs occur just as the wheat is at its most vulnerable to attack. However, YWBM damage varies from year-to-year and is currently difficult to predict. This project aims to further our knowledge of this pest and its impact on the wheat crop. In related pest midges, adult females produce a volatile sex pheromone which allows adult males to locate females prior to mating. Synthetic versions of these pheromones released from simple traps are widely used in many crops to monitor midge pests and identify when and where control strategies must be applied. By identifying the sex pheromone of YWBM in this project, we will have completed the necessary first step in developing an appropriate monitoring tool for use in UK wheat crops. We have previously identified experimental NIAB wheat lines that showed no YWBM damage in seasons when midge levels were high in adjacent varieties. With help from plant breeding companies, we will test these promising lines more thoroughly. We will grow them in small field plots at several locations across the UK, and measure YWBM levels in resistant NIAB lines and in susceptible commercial varieties. We will collect unripe wheat ears containing live YWBM larvae, and soil samples containing dormant pupae, from these and other sites to provide a source of midges. Young midges will be reared individually at NIAB East Malling until they emerge as adults. NIAB and NRI specialists will collect the volatile chemicals produced by groups of adult males and females. Through electrophysiological experiments at NRI, we will identify which chemicals produced by female midges can be detected by the males as likely components of the sex pheromone. Using chemical analysis and our experience in identifying other midge pheromones, we will begin identification of the YWBM sex pheromone components. If supply of midges and time allows, we will synthesize these likely components for further testing. NIAB will also explore the feasibility of maintaining a laboratory colony of YWBM for future work into the life cycle of this important pest.

Context: Wheat is the UK's major crop, covering 1.6 million hectares. Maintaining wheat yield is a critical component towards achieving economically and environmentally sustainable food security. To meet growing demand, wheat yields must increase; in the UK, this needs to take place against a background of unpredictable climate and reduced inputs. Delivering 'sustainable intensification' requires breeders to improve both yield and yield stability, in the face of unpredictable future environments. After a post-war period of sustained on-farm UK wheat yield increases, a result of both genetic and agronomic improvement, there has been no increasing trend in yield over the last fifteen years. Improved methods to increase the rate of genetic improvement represent a critical component of the solution. For the first time in UK wheat research, this project utilises a powerful combination of newly available approaches and resources, allowing detection of the genetic determinants of yield at high-precision, thus enabling rapid deployment of project outcomes within the six participating industrial partners. Central is the use of our unique Multiparent Advanced Generation Inter-Cross (MAGIC) population, which combines high genetic diversity (originating from eight UK wheat varieties), and high levels of genetic reshuffling ('genetic recombination', captured via multiple rounds of intercrossing, and the generation of the resulting 1,000 progeny lines). Project objectives: MAGIC Yield targets the genetic improvement of grain yield, the principle target for both breeders and farmers. It exploits the powerful union of high-density genetic marker coverage with a MAGIC population that captures high levels of genetic recombination and diversity, to: (1) Identify and characterise the genetic regions in wheat controlling yield, yield components and yield stability, at high precision. (2) Provide a molecular tool-kit with which wheat breeders can use in their breeding programs to deploy and track the regions of the wheat genome found to confer beneficial yield and yield stability. (3) Provide the participating breeders with analysis pipelines and resources with which they can independently carry out analysis of MAGIC datasets, both within and after project duration. (4) Use the novel molecular breeding methodology, Genomic Selection, to allow selection for yield and yield stability in the MAGIC lines, based on molecular data alone. (5) Provide resources centered around the MAGIC population, from which future studies targeting additional components of sustainable wheat production can be undertaken. (6) Develop and enhance interaction between the academic and industrial wheat R&D communities to ensure results and resources are effectively disseminated for the benefit of UK agriculture. Applications and benefits: The ability to apply modern molecular breeding approaches to precisely determine the determinants of yield and yield stability will lead to the development of new wheat varieties with improved performance. Such varieties would be of major benefit to the UK agronomy sector, helping increase wheat yields and protect against current and future threats to production from a changing climate. Promoting the UK's wheat R&D sector will help ensure the competitiveness of the agricultural sector, and support UK-based crop research and innovation. Ultimately, promoting stable and sustainable UK wheat production benefits the consumer in terms of food prices, and minimising the environmental impact of food production.

Despite its importance and growing demand within the UK, and globally, the rate of increase in wheat yields on UK farms have stagnated. To meet global future demand, annual wheat yield increases must grow to at least 1.4% and increasing the rate of genetic improvement using modern approaches is one way to do this. The ability to record vast quantities of genetic and phenotypic information cheaply (e.g. genetic markers and spectral images of field trials - termed in this proposal as Genomics and Phenomics) presents a new opportunity for increasing the rate of genetic improvement. The rate of genetic improvement is affected by (1) the accuracy of selection, (2) breeding cycle time, (3) selection intensity, and (4) the amount of genetic diversity to be selected upon. In the medium to long term, concerns about genetic diversity are being addressed through national and international projects to introgress traits and alleles from landraces and progenitor species. However, the major barrier to the immediate increase in the rate of genetic improvement in wheat is the length of the breeding cycle time. Even at their fastest wheat breeding programs require at least four to six seasons to complete a cycle, principally due to the time required to reduce the number of individuals for selection to a subset that can be intensively phenotyped. Genomic selection (GS) is a new breeding tool that, amongst other advantages, can dramatically reduce breeding cycle time as selection can occur without the need to record phenotypes. In wheat this means breeding cycle time could be reduced to one season, dramatically increasing the rate of genetic improvement. In the extreme, using glasshouses to complete 2 cycles of selection per year, 10 cycles could be undertaken in the 5-year time frame currently taken for a single selection cycle. GS uses a training population that is phenotyped and genotyped to construct a prediction equation. This equation is used to predict the breeding values of unphenotyped individuals, which, in wheat, would allow reduction of the breeding cycle to one season. GS assumes that saturating the genome of all individuals with molecular markers and estimating the effect of these markers (i.e. training the prediction equation) will allow capture of a large proportion of the genetic variation caused by the underlying quantitative trait loci. If the proportion of the captured genetic variation is large and well estimated the prediction equation will be able to make accurate predictions about breeding values. Similarly, in Phenomics the phenotype could be saturated with descriptors, which could lead to a better separation of its environmental and genetic components as well as generating more precise phenotypes. Creation of training populations is a required investment for GS and strategic use of resources to achieve the required size is needed to optimize the cost and benefit of GS. Use of a genotyping and imputation strategy is paramount for reducing costs. Field trials are also costly. Use of novel high-dimensional approaches for capturing extra traits and variables (Phenomics) could enhance the value of field trials generally, as well as enabling more powerful GS. This proposal will use field trials and simulation to design and evaluate Genomics and Phenomics strategies for increasing rates of genetic improvement in wheat. This will include GS training population designs and low cost collection of genotype data, assessment of the properties of high-dimensional environmental descriptors and quantification of their power, assessment of the properties of trait phenotypes collected by high-dimensional data recording devices and quantification of their relationships to standard traits. Results will be generalised to other species with breeding programs similar to those of wheat as well as to other type of experiments and field trials (e.g. National List evaluations).