We propose to use the largely undomesticated red clover forage crop as a model for unravelling a key domestication trait. Forage legumes have superior feeding value for ruminant animals, and their nitrogen fixing capability enables them to provide useful ecosystem services in terms of improvement of soil fertility. Despite these properties their use in livestock agriculture declined particularly in Europe in the 70's and 80's, chiefly due to the availability of cheap chemically produced nitrogen fertilizer. The drive towards more sustainable agriculture, particularly less use of fertilizer manufactured from fossil fuels has halted the decline, and there is increasing interest in these legume crops, particularly in mixtures with forage grasses. There is thus an urgent need to accelerate their genetic improvement, which has stalled in later years due to lack of investment. This proposal aims to use recently developed genomics resources for red clover, second only to alfalfa in importance in temperate agriculture, to assess the genetic and phenotypic diversity of a European-wide collection of germplasm. One of the most fundamental requirements for genetic improvement programmes is to have access to genetic variation within your germplasm. There are suggestions that most recent European breeding populations have a relatively narrow base. With a very recent history of breeding, the largely undomesticated red clover crop is an ideal candidate to provide a comprehensive assessment of the role of domestication in changing the genome landscape during a crop improvement programme. In other words we will aim to characterise the genomic impact of domestication in a crop improvement programme by using red clover as a model. We will use a collection of populations from a range of habitats from throughout Europe together with elite breeding material. We will use this diversity panel to assess the genome-wide nucleotide diversity and use this information to tell us which regions of the genome have been subject to selective pressures either as a result of breeding or environmental adaptation. The focus will be on a key domestication trait, namely prostrate versus erect growth habit, which has a profound effect on grazing tolerance and persistency in forage crops. Plants with more prostrate growth habits are likely to be more tolerant to grazing and be more persistent. On the other hand, there is a yield penalty associated with prostrateness. Unravelling the genetic architecture is thus of major importance for genetic improvement, and will also give us novel insight into this fundamental trait in plants. We will use two types of plant material for this: A diversity panel consisting of ecotypes and natural populations with varying degrees of prostrate growth habit, and compare with elite breeding populations, all of which are erect. Secondly, we will generate populations segregating for this trait by crossing an erect female parent from elite material with five pollen donors taken from the prostrate natural populations. Phenotypic analysis of agronomic and growth traits in these populations will be accompanied by chemical analysis of various forage quality traits, and by obtaining genome-wide SNP polymorphism data. This will be achieved by restriction associated DNA (RAD) marker analysis in the mapping populations, as well as the diversity panel. In combination with the improved genome sequence assembly, this will enable us to identify and map genomic regions under selection, and allow identification of some of the genes governing this trait. This will provide novel insight into the architecture of domestication traits. The partnership with Germinal Holdings Ltd gives us a pipeline into the breeding programme, which will ensure that the genomic data and knowledge we obtain will benefit genetic improvement of red clover.

Magnesium (Mg) plays an important role in many basic processes in living cells, and is therefore essential for animal health. Low Mg status (hypomagnesaemia) gives rise to conditions called tetany, or staggers, in ruminants like cattle and sheep. These conditions are remarkably widespread among ruminants in Europe, often with high fatality rates. Avoiding hypomagnesaemia is therefore important for animal welfare and farm business profitability. Typically the condition is managed by use of feed mixes or dietary supplements or by directly administering Mg to animals as a medicine. However, these approaches are costly and can be inefficient and ineffective. Another approach is to ensure that the grass grazed by animals in the field or eaten as hay or silage provides a good source of Mg. This approach is under-developed in UK agriculture, as is forage fertilizer management. To develop these approaches requires three advances. First, we must understand the natural variation in the capacity of the soil to supply Mg, caused by differences across the country in the properties of the soil and the composition of the rocks from which they are derived. Second, we must understand how farmers currently use Mg in a range of enterprises as supplements, additives, fertilizers and veterinary interventions. Third, we must understand the mechanisms by which Mg is transferred from soil to plant to animal and how these can be exploited. For example, forage grass varieties that accumulate more Mg were selected by plant breeders in the 1970/80s. For example, a variety of Italian ryegrass (Lolium multiflorum 'Magnet/RVP2067') which accumulates more Mg was selected in UK breeding programmes. This variety performed consistently across sites and was shown to improve ruminant Mg status in feeding studies. However, this trait has not since been pursued in modern hybrid or perennial ryegrass varieties now favoured by the sector. We aim to develop novel and resilient nutrient management strategies for Mg in the UK ruminant sectors. The primary nutritional focus of this project is Mg due to its strategic importance to the UK ruminant sector. However, new data, knowledge and management and communication tools arising from this project will apply to other nutrients/elements which are important for animal health. The project will therefore have wider potential to make animal production more efficient and resilient and will improve our wider understanding of landscape-scale processes. This project will draw on a range of scientific disciplines including soil chemistry, geology, statistics economics and plant sciences. The four primary objectives are: (1) to use varied soil data to map the regions of England, N. Ireland and Wales where the soil supply of Mg is likely to be insufficient (2) to develop new understanding of Mg transfers on the farm and how these are managed, (3) to develop genetic markers and crop management strategies to increase leaf Mg concentration in modern forage grasses, and (4) to integrate these streams of knowledge and information into a decision tool that allows the farmer to improve forage nutrient management at farm scale and also help advisors or policy makers to examine management options at regional scale. The tool will take into account the economic impact of nutrient management scenarios based upon delivery via mineral supplements or improved grazing management via enhanced nutritional forage profiles given local and regional soil conditions. Decision support will be delivered via a user-friendly web/smart-phone interface. User-defined inputs will include spatial data (soil characteristics, climate, etc.), choice of grass variety, fertiliser-management, supplement use, and economic costs. Outputs will enable the economic benefits of the various Mg nutrition options to be compared and communicated, at farm-to-regional scales.

Maintaining or increasing agricultural food production and security is a priority in order to meet the needs of a growing population. This challenge is put into further focus by climate change and the need to reduce the environmental footprint of agriculture. There is thus an urgent need to increase the speed of improvement of crop varieties in terms of yield and increased efficiency of use of resources, such as fertiliser and water. Genetic improvement of these traits in crop plants has been achieved by plant breeding on the basis of selection and crossing of phenotypically superior plants. In the last 20 years or so molecular markers have been used in some breeding programmes, but largely on an ad hoc basis for improvement of a few target traits. The advent of more affordable high throughput (next generation) sequencing and genotyping in the last five years has made it possible to make use of molecular markers in a more comprehensive way than hitherto. We refer to genomic selection (GS) which represents a novel way to improve the phenotype of complex agronomic and biological traits governed by many genes each with a small effect. GS is already beginning to transform the breeding of livestock such as cattle and pigs, but has yet to make an impact at a practical level for crop plants. GS is selection based on the collective composition of molecular markers densely covering the entire genome. The proposed collaboration between the Institute of Biological, Environmental and Rural Sciences (IBERS) and the Computer Science Department at Aberystwyth University gives us an opportunity to test GS empirically and theoretically. IBERS is the only university department in the UK with plant breeding programmes, and we will use this unique position by exploiting our perennial ryegrass breeding programme. It is based on repeated cycles of recurrent selection and crossing and is well suited for GS, as we have comprehensive phenotypic data for the current generation and earlier generations of this successful scheme. We will use the current generation of motherplants as a "training population" by genotyping it with over 3000 molecular markers covering the entire genome. The aim is that at least one molecular marker is close to a genomic region influencing the phenotype of interest (quantitative trait locus or QTL). The phenotypic data already available from the breeding programme will be combined with the genotype data to generate complex prediction models using established statistical methods, but also state-of-the-art machine learning techniques developed at the Computer Science Department, for the calculation of a genomic estimated breeding value (GEBV), and to test the performance of the models in the breeding programme. The computational models are then used to calculate the GEBV in a validation population, which is different from the training population, using only genotypic data. The resulting GEBV will be used to select individuals for progeny production based on genotype only. Given a dense coverage of the genome, the combined effect of many QTL for the same trait can be improved measurably by incorporating the effect of all alleles simultaneously. This approach will be particularly advantageous in perennial crops, such as ryegrass and other forages, as the need for lengthy plot trials can be reduced. However, this is not the only benefit of GS. The genomic and statistical resources and models developed here will provide us with a platform for discovery of genes and facilitate the unravelling of the architecture of complex traits of agronomic and biological importance.

Abstracts are not currently available in GtR for all funded research. This is normally because the abstract was not required at the time of proposal submission, but may be because it included sensitive information such as personal details.

Abstracts are not currently available in GtR for all funded research. This is normally because the abstract was not required at the time of proposal submission, but may be because it included sensitive information such as personal details.