Research context In recent years, much attention has focussed on the development of resources that underpin genomics and bioinformatics in key crop species. This includes funding for assembling complex crop genomes and enhanced understanding of how genes function and interact. Whilst crucial for unlocking potential productivity gains to support food security, there is now a gap in capacity for experimental field trials, undertaken at scale. Such research trial capabilities are core to delivering crop research outputs to plant breeders, growers and the wider agricultural industry. In this proposal we request funds for a high-precision research plot combine capable of harvesting a wide range of crops, including all UK grown cereal and legume species. This will help safeguard the delivery of research field trials at the NIAB-Cambridge site for the next 5-10 years. It will underpin delivery of the diverse breadth of crop research and pre-breeding activities carried out by NIAB, routinely undertaken in partnership with other universities, institutes and industrial partners. Therefore, it will provide a highly accessible route for UK crop researchers to deliver a substantial portfolio of high-quality research to deliver outcomes aligned with BBSRC's high-level themes in 'Tackling Strategic Challenges' ('Bioscience for Sustainable Agriculture and Food') and 'Building strong Foundations' ('Infrastructure' and 'Collaboration, Partnership and Knowledge Exchange'). The equipment requested will be accessible within the context of NIAB's extensive trials facilities and associated crop research capabilities and expertise - so maximising the potential for wide uptake by the crop R&D community. Aims and objectives The aim of this proposal is to purchase a Haldrup C85 plot combine to support UK crop resarch with impact. The combine will be equipped with on-board near-infrared (NIR) spectrometry (the first in use in UK agricultural research), allowing on-combine measurement of grain protein content, an important criteria for determining grain quality and end use. It will also provide automated straw weighing, allowing accurate assessment of crop biomass (an important indicator of the ability of a crop to efficiently convert energy into harvestable product), and include integrated GPS functionalities for guided route planning and GPS data stamping. Potential applications and benefits NIAB delivers in excess of 100,000 arable and forage trial plots per year, of which 15,000 are combinable research plots. These are currently delivered using two Haldrup C85 plot combines, one of which is at the end of its replacement threshold. The requested equipment has new on-combine features not currently in use in the UK crop research sector. It will directly benefit NIAB's research delivery (currently £11 million of BBSRC investment), and catalyse access to high quality research field trials from public- and private-sector partners and collaborators.

Doctoral Training Partnerships: a range of postgraduate training is funded by the Research Councils. For information on current funding routes, see the common terminology at https://www.ukri.org/apply-for-funding/how-we-fund-studentships/. Training grants may be to one organisation or to a consortia of research organisations. This portal will show the lead organisation only.

Doctoral Training Partnerships: a range of postgraduate training is funded by the Research Councils. For information on current funding routes, see the common terminology at https://www.ukri.org/apply-for-funding/how-we-fund-studentships/. Training grants may be to one organisation or to a consortia of research organisations. This portal will show the lead organisation only.

The identification of small segments of chromosomes containing genes that control traits can improve the speed, efficiency and effectiveness of plant breeding. One method of identifying such segments is to look for correlations between DNA markers and traits. Typically, a marker is a short length of DNA with a known location on a chromosome. A strong correlation between marker and trait is an indication of a gene with an effect on the trait close to the marker. This process is typically carried out by searching for correlations among the progeny of a cross between two inbred parents. In such controlled crosses other factors affecting the genetic make-up of a population, such as migration, are eliminated. This minimises the occurrence of spurious correlations between markers and traits. This approach has two limitations. Firstly, marker-trait associations in bi-parental populations are not guaranteed to be important among the lines used by breeders. Secondly, correlations occur when marker and gene are quite a distance apart and these have little practical use. Ideally, the presence of a correlation should indicate that the marker is close to the gene. Plant breeders can then select for presence or absence of the marker, rather than selecting for the trait. This can be quicker and cheaper. Methods have been developed to overcome these limitations. One is to repeatedly cross individuals over successive generations before correlating markers and traits. These extra crosses cause thorough shuffling of genes coming from each of the parents, thereby improving the chance that marker-trait associations are only detected if the marker is very close to the gene. Such populations are called 'Advanced Intercrosses'. There is no requirement for an Advanced Intercross to have only two parents. Multiple parents can make the population more representative of the lines used by breeders. Multiparent Advanced Intercross (MAIC) populations take time to set up and more markers are required than usual. However, the cost of DNA markers is falling steadily. It is therefore important that MAIC populations are established now, to exploit cheaper marker systems as they become available. The work described in this proposal sets out to establish this resource for winter wheat. We shall set up two MAIC populations, one based on current elite lines and one based on older lines of historic importance. Within the time available, we shall also derive a set of inbred lines from the first generation of each population. These will be made available to all interested stakeholders. The use of these populations in very fine mapping is limited, but they will still allow location of genes with greater precision than possible with available alternatives. We have also identified two pre-existing highly outcrossed winter wheat populations. These have not been maintained under controlled conditions but are still likely to be of use as MAIC populations. We will generate 1000 lines from each of these populations to create a resource available immediately for very fine mapping. Although the theory behind the use of MAIC populations is understood, we need to confirm that it works in practice. For this purpose, we will use a system of cheap genetic markers called DArT (Digital Array Technology) to genotype (genetically fingerprint) samples of individuals and inbred lines. Using these data, we can check that the shuffling process occurs as expected and that the pre-existing populations can indeed be treated as if they were MAIC populations. Finally, we will use the DArT data to hunt for associations with the genes responsible for male sterility, present in the established outcrossing populations. The location of these genes in roughly known, but we shall refine it to provide a practical demonstration of the power of the MAIC. Locating this gene with greater precision will also help in the design and execution of future genetic experiments.

Microbes in their environment are exposed to changing conditions, which select for the most fit variants. This continual process of adaptation leads to the genetic composition of populations shifting in space and time as the fittest mutations track change. Unfortunately, when selection is imposed by chemicals that are designed to kill microbes, then those that are genetically resistant rise in frequency; this results in the global problem of antimicrobial resistance evolving in the environment. While emerging antimicrobial resistance is widely recognised in bacteria, the emergence of fungi that are resistant to antifungal chemicals is underappreciated yet is compromising our ability to grow blight-free crops and to treat serious human fungal diseases -therefore presenting a classic One Health dilemma. The core focus of our project is Aspergillus species, common environmental moulds to which all humans are exposed due to their ubiquitous presence in the air. Of note, A. fumigatus affects millions of susceptible individuals worldwide (including those with COVID-19) and is increasingly causing disease that is resistant to the frontline azole antifungal drugs that are used to treat it. Crucially, this is the same class of chemicals is used by farmers as fungicides, which is driving a surge in azole-resistant A. fumigatus as this mould comes under selection by these chemicals in its natural environment. However, we currently have very little understanding of the landscape-scale pathways that lead to fungicide chemical residues accumulating to the concentrations that select for, and amplify, resistance in moulds. We understand even less about the consequences combinations of different fungicides on the emergence of resistance, or how interactions with the wider microbial community that may hinder (or help) the emergence of resistance. Our project will examine the nested anthropogenic drivers - agricultural practices and green-waste recycling - with the aim of understanding how they create hotspots of evolution for antifungal resistant pathogens. The moulds on which we will focus are embedded in complex microbial ecosystems and we will determine the impact of scale from country-wide distributions of the fungus, through the ecological succession seen in fungicide-rich mesocosm environments, and down to individual model microcosm models. To do this, we will couple field and laboratory studies with Bayesian-based statistical methods that take into account both evolutionary and ecological complexity within a spatially-explicit framework. In doing so, we will be able to identify, understand and link the key factors that lead to hotspots of fungicide-resistant moulds forming. The variables that we measure - landuse, fungicides, fungal genetics and microbial community ecology - will be integrated into a systems network analysis that links the usage of fungicides in the environment to ecological settings where resistance is selected for. These 'Bayesian probabilistic networks' are a powerful tool which will allow us predict hotspots for fungal drug-resistance, as well as allowing us to model methods to mitigate against this risk by reducing fungicide-inputs into specific 'pinch-points' that we identify. Ultimately, by dissecting the extended (unintentional) consequence of fungicide use as these chemicals drive the evolution of fungal antimicrobial resistance, our project will address this problem within its greater 'One Health' context. Our approach is urgently needed to develop the knowledge-base that is needed to understand the current risk as well as to mitigate the selection-pressure driving future emergence of fungal antimicrobial resistance in the environment.