Changing climate, variable yields, and resource constraints are challenging UK agriculture and global food security. Our goal is to enhance sustainable and efficient production for two crops of major importance in the UK, wheat and potatoes. We will work with farmers and end users in the application and deployment of novel crop sensing and diagnostic technologies. We will develop a tool that can predict and diagnose crop response to water and nutrient related limits. In turn, this knowledge will guide crop management and help to inform stakeholders from across the arable supply chain about the best approaches for land management towards more sustainable and efficient production, using precision agriculture. We will test remote sensing technologies and couple them with methods for analysing crop and soil processes. We will deploy sensors at farm sites on fixed towers and unmanned aerial vehicles (UAV), and compare these against satellite sensors with global coverage. We will evaluate whether changes in leaf temperature, fluorescence and reflectance are related to yield reductions, comparing sensor data against field measurements of plant growth, yield and ecophysiology, and plant and soil temperature and moisture. We will understand how, and under what circumstances, sensors and platforms can be employed to determine and diagnose crop yield limits. Links to simulation process modelling will provide a rich set of diagnostics related to the plant-soil system, and forecast its sensitivity to management changes. Data assimilation approaches will allow model updates and improvements based on field observations and sensor output, to generate more reliable, near real-time and robust analyses. Our technologies will underpin a crop diagnostic system, indicating crop water and nutrient status, quantifying reductions to yield, that can be used at sub-field to farm scale, with a clear quantification of reliability. Working with farmers, our technologies will be combined to generate a decision support tool, with capacity for (i) immediate (near-real time) mapping of crop stress, and its likely impact on crop yield and (ii) providing detailed spatial information on optimal management interventions to support decision making for sustainable high yield. This work directly addresses a priority of the UK research councils to support UK farming with high quality and practical research to support consistent high returns from crop production against a background of changing climate and increasingly competitive global markets. Our deliverables will provide advanced diagnostics for farmers, and guide cost effective strategies for water and nutrient management for consistent yield.

As the world needs to feed more and more people, the ethical and sustainable production of food becomes of greater importance. In order to produce animal products ethically, animal welfare standards should be kept as high as possible during the production process. Because of this, legislation exists that requires food producers to adhere to minimum standards in terms of animal care. Within these constraints, there are varying methods of rearing to satisfy consumers' demands for high welfare products, for example organic, free range and cage egg production. Objectively assessing which practice benefits the animals' welfare the most, however, is difficult, because while some animals may thrive in one environment, others appear to suffer. But even for the animals that do not show obvious problems, some environments may be more stressful than others. Current methods of animal welfare assessment either depend critically on the animal's immediate state (and would not pick up on problems the previous day, for example), or are difficult and time-consuming to administer, which is not practical in a commercial setting. Here, we propose to use what we know of the clinical and pre-clinical study of depression and chronic stress to look for markers of long-term poor welfare in chickens. It is well known from rat models of depression and chronic stress that chronic stress leads to a reduction in the survival of newly generated nerve cells in a brain structure called the hippocampus. The exact role of these new cells in the development of depression is still heavily debated, but the reduction in new cells can be used as a marker for whether the animals have undergone chronic stress or not. We propose to develop a rapid and objective method to measure this incorporation of new neurons in the hippocampus by measuring the total amount of a gene or its protein product (called doublecortin) that is produced only by these new neurons. We will first verify that this method gives the same results as the more laborious method of counting new cells on microscope slides. We will do this in a well-established model of chronic stress, using rats, for which we know chronic stress will lead to a reduction in the number of cells expressing doublecortin. We will then apply this idea to chickens, and compare the amount of doublecortin between chickens housed under laboratory conditions that are stressful or not. Finally, we will compare doublecortin amounts in the hippocampus of chickens collected from different commercial housing environments (e.g. layers in cages vs. free range), and between chickens that are considered to be in good and in poor physical shape, using current criteria of welfare assessment. We will do this for the two most relevant types of chickens: egg-producers (laying hens) and meat-producers (broilers), which are held in different types of facilities. This way we will be able to inform our industrial partners (egg and meat producing companies) about which of their production methods provides the highest welfare for their animals. Our novel method will provide a fast and reliable method to assess welfare in chickens under different housing conditions, and as such allow producers to optimise animal welfare within their facilities, and for regulators to monitor welfare. If this technique proves useful, it can then later be expanded to other farm animals, such as turkeys, sheep, cattle and pigs.

There is ever-increasing financial pressure in the UK beef sector due to volatile feed prices, consumer requirements for cheaper produce and competitive beef imports from abroad. Optimising animal productivity is critical to maintaining a competitive and sustainable UK beef industry with production efficiency the greatest single opportunity to reduce primary production costs. At present, there is considerable inefficiency in the UK beef sector which increases variable farm costs, reduces the yearly capacity of beef finishing units, and reduces profitability due to sub-optimal marketing of animals. These inefficiencies are estimated to reduce overall profitability of the UK beef production industry by approximately £500M per year (Morrisons estimate based on their experience from their integrated beef supply chain). The reduced revenue associated with these inefficiencies arise for three main reasons: (1) retaining cattle on-farm beyond the optimum point of marketability leading to extra feed, bedding and fixed costs; (2) reductions in sale revenue due to these over-finished cattle being out of desired specification (i.e. too fat) and (3) loss of productivity and efficiency due to poor animal health. From a biological perspective it has been shown that there is a large between-animal variation in feed efficiency; early results from the TSB-funded Net Feed Efficiency project (BIG/NFE), have suggested a variance of up to 30 percent in feed efficiency of groups of growing and finishing cattle of the same age/breed/sex. The differences between groups of more divergent genotypes might reasonably be expected to be greater still. Profitability and efficiency are also inhibited by illness, with efficiency often dropping well in advance of any clinical signs of illness. In practice, it is difficult for farmers to measure the performance efficiency of individual animals. Currently, animal growth and performance is determined through visual assessment or by weighing the animals. However, growth rates alone are not a measure of efficiency; in order to calculate efficiency of individual animals an accurate measurement of feed input is also required. The project addresses some of the key challenges facing the sustainable intensification of beef. The overall aim is to develop a state-of-the art solution for beef farmers to optimise the efficiency of their businesses. At the core of the project is the development of a near infra-red (NIR) system to characterise feed (dry matter content, nutritional composition) as it exits a feeder wagon. Also pivotal to the project is the development of animal-mounted sensors to measure feeding behaviour (eating and rumination patterns). The bulk feed characteristics will be integrated with the feeding behaviour data with a target of providing a robust, accurate and innovative method of determining individual animal feed intake. The final solution will be a cloud-based decision support platform integrating individual animal feed intake and behaviour data, with measures of animal performance e.g. growth rates. This will provide the support tools necessary to quantify performance and efficiency of individual animals and improve the sustainability of the production process. It is anticipated that by closely monitoring individual animals using the system proposed in this project, the finishing period of the animal will be reduced on average by 14 days, while animals performing poorly due to illness will be flagged up to the farmer allowing for earlier intervention.

Campylobacter is the largest cause of recognised bacterial gastroenteritis in the developed world. The 2009 reporting rates for Great Britain show more than 64 000 cases, an increase of 30% in Scotland and 14% in England & Wales on the previous year, that has continued into 2010. Because there is substantial under-reporting of campylobacteriosis, the actual number of cases in 2009 is likely to be closer to 450 000. Further, about 10% of reported cases are hospitalised. This rise is all the more disappointing because rates of infection with Campylobacter had been falling between 2000 and 2005. Molecular strain typing, by us and others, has identified that poultry is significantly the most important source of this infection with the most common types found in human beings also being the most common in chickens. Studies on retail poultry show a prevalence of Campylobacter in this meat of over 65% with the main routes of infection being eating improperly cooked meat or cross-contamination to uncooked foods. To reduce this burden of human disease, action must be taken to reduce Campylobacter loads in poultry and The Food Standards Agency, Defra and BBSRC have all identified this as a major priority. The FSA is considering targets for the reduction in levels of Campylobacter in raw chicken at retail, to be achieved by April 2015. The target will be set and achieved through stakeholder engagement and partnership working. Interventions in the poultry industry abroad have resulted in dramatic decreases in human infection rates. For example, in Iceland where freezing of positive carcasses is used, in New Zealand where interventions and regulations were introduced and in the USA where improved hygiene and the use of chlorine washes for carcasses has been implemented. However, UK industry has largely been unable to achieve reductions. Although strategies such as poultry vaccination are attractive in the longer term, more immediately it will be through informed biosecurity interventions on broiler farms that control is likely to be most readily achieved. Indeed UK producers widely recognise that where robust biosecurity remains unbreached, as for the valuable (grand)parent birds that are used to produce the eggs that hatch into broilers, then Campylobacter colonisation is uncommon. It is in the high throughput broiler production that colonisation regularly occurs and where novel biosecurity controls, as proposed here, could play an important role. Our previous studies of the sources of Campylobacter infection in humans not only identified the principal source as broiler chickens, it also identified that the distribution of Campylobacter strains found in humans and in the reservoirs of chicken, cattle, sheep, wild birds, pigs etc, were quite distinct with some strains common to several hosts. This proposal seeks to better understand the relative importance of the potential sources of Campylobacter in broilers by using a modelling approach. The hypothesis is that some Campylobacter strains and some Campylobacter reservoirs are much more important than others in this process and that it is only by quantitating their relative importance and their interaction with each other that it will be possible to robustly identify the sources of Campylobacter in the broiler house and hence introduce effective measures to prevent the colonisation of these birds during production. The findings will enable policy to be developed (e.g. code of practice) to define which control measures are most effective in keeping broiler houses Campylobacter free. This will strongly influence industry through improved farming practice.

By 2050, the human population will grow to over 9 billion people, and in the same time frame, global meat production is set to increase by 73%. There is a need to increase the efficiency and sustainability of animal production, reduce waste in the food chain and ensure safe and nutritious diets in order to address this challenge. Rumen microbes confers a unique ability to convert human inedible high-fibre forage into nutrients the animal can absorb to produce high-quality proteins as meat and milk. However, intensive food production puts a strain on the environment, and there is a need to produce more food ethically and in a way that does not harm the environment. The project addresses these challenges by unravelling the functional and genomic architecture of the ruminal microbiome affecting performance traits of cattle. This information will be used to identify fundamental associations between the microbiome or its genes with animal performance traits and methane emissions. In this study we will sequence all microbial genomes - the metagenome - to describe the composition of the microbial community and its functional genes. The analysis will be based on a unique dataset of 288 experimental beef cattle, with rumen DNA samples and a large array of performance information (e.g. feed conversion efficiency, growth, body composition and meat quality) available. These data are structured by breeds and sire progeny groups to estimate the animal host genetic effects on the microbiome and microbial genes. The experimental data have been the basis of numerous publications in which it was shown that at the animal performance level, and for methane emissions, there are large differences between breeds, sire progeny groups and diets. Preliminary analysis for 8 of these animals suggests that there is a link between the abundance of the microbial community or microbial genes and animal performance traits and methane emissions. However, to understand the function and genomic architecture of the ruminal microbiome, analysis of the full sample set is necessary. Algorithms will be developed to predict animal performance, e.g. feed conversion efficiency and methane emissions from the abundance of the microbial community and genes. These high value, but costly-to-measure traits could then be predicted by analysing the rumen microbiome (sampled via stomach tube on live animals or in the abattoir). However, to verify the associations between the rumen microbiome and performance traits, we need basic knowledge about the functional and genomic architecture of the microbiome. Additionally, microbial biomarkers to predict e.g. feed conversion efficiency could be identified. Due to the unique structure of the data in sire progeny groups and diets, we will be able to predict the host genetic and nutritional effect on the microbial community and microbial genes. This structure can also be used in the network analysis to identify animal genetic effects on the functional and genomic architecture of the microbiome. The project will provide unprecedented new knowledge of the genomic and functional architecture of the microbiome and its impact on performance traits and methane emissions as well as the interaction with animal genetics and nutrition. We will compare the functional and genetic architecture of the microbiome in beef cattle with that of other species to provide insights about the microbiome of different species, in particular humans. By understanding host genetic effects on the rumen microbiota and associations with body composition, we expect to provide new insights for human personalised medicine approaches to reduce obesity.