Agency: European Commission | Branch: H2020 | Program: RIA | Phase: SFS-07b-2015 | Award Amount: 9.01M | Year: 2016
This aim of IMAGE is to enhance the use of genetic collections and to upgrade animal gene bank management. IMAGE will better exploit DNA information and develop methodologies, biotechnologies, and bioinformatics for rationalising animal genetic resources. It will demonstrate the benefits brought by gene banks to the development of sustainable livestock systems by: enhancing the usefulness of genetic collections to allow the livestock sector to respond to environment and market changes; using latest DNA technology and reproductive physiology for collecting, storing and distributing biological resources; Minimising genetic accidents such as abnormalities or genetic variability tipping points; Developing synergies between ex-situ and in-situ conservation to maximise resources for the future. To this end, the project will involve stakeholders, SME, and academic partners to achieve the following objectives. At the scientific level, the project will: Assess the diversity available in genetic collections; Search for adaptive traits through landscape genetics in local populations; Contribute to elucidate local populations and major genes history; Identify detrimental variants that can contribute to inbreeding depression; Predict cryobank samples reproductive performance; Facilitate the use of collections for genome-assisted breeding. At the technological level, it will develop: Procedures for harmonising gene bank operations and rationalising collections; Conservation and reproductive biotechnologies; A central information system to connect available data on germplasm and genomic collections. At the applied level, it will develop methods and tools for stakeholders to: Restore genetic diversity in livestock populations; Create or reconstruct breeds fitting new environmental constraints and consumer demands; Facilitate cryobanking for local breeds; Define and track breed-based product brands; Implement access and benefit sharing regulations.
Agency: European Commission | Branch: H2020 | Program: RIA | Phase: SFS-01c-2015 | Award Amount: 7.00M | Year: 2016
iSAGE will enhance the sustainability, competitiveness and resilience of the European Sheep and Goat sectors through collaboration between industry and research. iSAGE have a powerful consortium with 18 industry representatives from various EU production systems and socio-economic contexts. The sheep and goat sector will be investigated because it is sensitive to general socio-economic, demographic, and ecological and market challenges; nevertheless, the projects approach and results will be made available and disseminated to other EU livestock industries. Therefore, at the core of iSAGE is a participatory approach centered on a multi-actor internal and external communication (WP) to build the project from the farmer level. This approach will ensure relevant issues are addressed and the project outcomes are applicable in practice and create a farm-level observatory and knowledge exchange network on the sustainability of livestock. This WP will also assist three assessment work packages that will deal with the sustainability assessment of sheep and goat farm systems and related supply chains, with socio-economic demographic and consumer trend analyses, and with the impacts of climate change. Assessment WPs will inform action WPs that will: (1) redesign holistic farming systems to best reconcile the various demands concerning productivity, sustainability and societal values. (2) identify industry solutions that aim to improve sustainability and productivity of sheep and goat systems through breeding, including new phenotypes linked to sustainable animal productivity. iSAGE, together with stakeholders and end-users, will draft a roadmap for further research and policy making. The stakeholder groups will be the key players in disseminating project outputs through case studies and demonstrations to act as a blueprint to other producers across Europe and create networks to assist wider implementation of iSAGE outputs.
Agency: European Commission | Branch: H2020 | Program: CSA | Phase: RUR-10-2016-2017 | Award Amount: 2.00M | Year: 2016
SheepNet is a thematic network project about practice-driven innovation to improve sheep productivity (number of lambs weaned/ewe mated): a critical component of farmers income and therefore of the sustainability and attractiveness of sheep farming. SheepNet will establish durable exchange of scientific and practical knowledge among researchers, farmers and advisors, through a multi-actor and transdisciplinary approach at national and international levels and by the broad involvement of European Agriculture knowledge and Innovation Systems. This will promote the implementation and dissemination of innovative and best technologies and practices for the improvement of sheep productivity. To maximize impact and ensure a wide coverage of different farming systems, SheepNet will bring together six main sheep producing EU countries, plus Turkey, and Australia, New Zealand, and every relevant EU network. The project aims to: produce a scientific, technical and practical knowledge reservoir through a combined top-down and bottom-up approach and the strong involvement of 45 innovative farms; foster cross-fertilization through multi-actors workshops at national and international levels, a broad and interactive participation of the sheep community via social networks and an interactive platform; develop an easily understandable support package of communication and learning material, web-based tools, interactive platform, designed to help both scientists and stakeholders and a strong interactions with the EIP AGRI Service Point will guarantee long-lasting and wide accessibility of the SheepNet results.
David Ritchie Implements Ltd, Sruc and Innovent Technology Ltd | Date: 2016-06-01
The present invention relates to an imaging arrangement for acquiring an image of a quadrupedal livestock animal. The imaging arrangement comprises a stall 32 configured to receive a quadrupedal livestock animal 36 and imaging apparatus 34 configured to acquire at least one image of a body of the quadrupedal livestock animal when the quadrupedal livestock animal is received in the stall 32. The stall 32 comprises first and second opposing sides 38, 40 between which the quadrupedal livestock animal 36 is received during operation of the imaging apparatus 34 such that the first and second opposing sides are on opposite sides of the quadrupedal livestock animal. The first and second opposing sides 38, 40 are configured such that, in use, they are: more widely spaced apart at a first location than the legs of the quadrupedal livestock animal whilst being more narrowly spaced apart than the sides of a trunk of the quadrupedal livestock animal; and more widely spaced apart than the sides of the trunk of the quadrupedal livestock animal at a second location spaced apart from the first location in a dorsal direction of the quadrupedal livestock animal.
Agency: GTR | Branch: BBSRC | Program: | Phase: Research Grant | Award Amount: 378.65K | Year: 2016
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.
Agency: GTR | Branch: ESRC | Program: | Phase: Research Grant | Award Amount: 469.05K | Year: 2016
Brazil is an important player in the international debate about food security, food production and the need to develop production methods that minimise climate impacts, land use changes and loss of tropical forests and biodiversity. These sometimes competing objectives and the damaging role of some forms of livestock production in particular, have led some commentators to suggest that a process of sustainable agricultural intensification is necessary to produce more outputs from less inputs, especially land, which has traditionally been abundant in Brazil. This new sustainable intensification agenda is an important element of a wider green growth debate in the UK and increasingly in Brazil and other emerging economies. This project considers the nexus of trade-offs inherent in the need for Brazil to sustainably intensify agricultural production to avoid local and global external costs in terms of greenhouse gas emissions from livestock production and direct and indirect land use change (by deforestation) and associated loss of biodiversity and ecosystem services. The wider context for this imperative is the increasing global demand for food production and Brazils ambition to maintain its pre-eminent status as a global food commodity exporter, while maintaining domestic food security and social equality. Global climate change creates an additional stressor, with a need for Brazil to understand impacts and to make incremental or transformative adaptations to allow the agricultural systems to be more resilient to climate scenarios. The project adopts a quantitative approach to understanding the interaction between these elements. We identify a number of sustainable intensification measures that can be accommodated within farming systems of different scale across the variety of Brazilian environments (or biomes). These measures include livestock feeding, grassland improvement and housing options. We then develop a numerical optimisation model that describes different production systems and allows us to illustrate the economic and environmental trade-offs in a way that helps to inform the design of policies such as those focused on greenhouse gas emissions from agricultural production and land use change (including deforestation). The project addresses the more general question of where production is appropriate and where it is not, by modelling the responses in each biome. We will also be able to address more contentious issues such as the consequences of reducing global demand (or consumption) for livestock products, which is increasingly discussed in academic and policy circles. The project combines expertise from key agricultural research institutions in Scotland (SRUC) and Brazil (Embrapa), and a key civil society organisation (Imaflora) focussed on agricultural development and sustainability in Brazil. Our proposal builds on an existing collaboration between the scientific team members and an existing model framework that we aim to improve using additional data and qualitative research of smallholder systems. A range of different stakeholders from the public, private and civil society will be involved to help develop our model structure and comment on results. The work builds on existing experience of providing evidence to the Secretary for Agricultural Policy for the Ministry of Agriculture, Livestock and Supply (SPA/MAPA). This advice has been used for policy development for Brazils offer as Nationally Appropriate Mitigation Actions (NAMAs) from the agricultural sector. NAMAs are voluntary emissions reduction commitments to be offered under the United Nations Framework on Climate Change. Finally the project will target significant knowledge exchange to facilitate Brazils ambition to inform the intensification debate in other countries in the global south.
Agency: GTR | Branch: NERC | Program: | Phase: Research Grant | Award Amount: 202.10K | Year: 2017
In the UK, the average herd size and animal to stockman ratio is increasing within the beef and dairy sectors, thus the time devoted to monitoring of individual animals is reducing. In order to optimise the production efficiency of the UK livestock sector, there is a requirement for the development and use of cost-effective animal monitoring solutions to inform on the health and productive status of individual animals. Dystocia is a considerable problem within beef and dairy systems causing the cow considerable pain. Prevalences of up to 22.6% in dairy cattle and 6% in beef cattle have been reported, with as many as 51% or dairy calvings and 34% of beef calvings requiring some level of assistance. The costs associated with mild and severe cases of dystocia in the dairy herds have been estimated as between £110 and £400 due to milk loss, increased days open, increased numbers of services, premature culling of loss of cows and lost calves. Timely intervention on difficult calvings can significantly reduce calf mortality, uterine infections post-partum and calving to conception interval compared with unassisted calving . Thus the development of methods to automatically predict calving onset and identify problematic calvings is important to facilitate timely and appropriate interventions. A number of physiological and behavioural changes occur around calving which offer opportunities for the prediction of calving onset. Despite the possibility of using ability of hormonal changes as indicators to be used for prediction of calving, the variable accuracy of these, figures and need for invasive nature of blood sampling to detect changes in hormones limits its usefulness as a method of automatic dystocia prediction. Reductions in body temperature occur on the day of calving compared with 2-3 days before calving but high variations in temperature change between individual animals and possible impacts of pyrexia limit the predictive power of temperature alone. The n-invasive nature of behavioural observations and the availability of a number of sensors on the market or near to market designed to monitor different elements of cattle behaviour provides opportunities for translation of current behavioural and technology validation research into a multi-sensor platform for the prediction of calving onset and calving difficulties. Lying and standing behaviour, eating and rumination patterns, social behaviour and tail raising events are known to change during the 24 hours prior to calving This study will assist in translating a range of behavioural research and technical knowledge into a potential early warning system for calving and dystocia. It will assess a number of technologies on the market or near-to-market for related and other uses (e.g. detection of oestrus) for their capabilities in the detection of calving and dystocia. The use of combined technologies is likely to result in increased accuracy of decision making algorithms and will therefore provide added value to the end user. The availability of early detection and alerts for parturition/dystocia will enable farmers to intervene in a timely manner to prevent the losses associated with dystocia, thus optimising the economic and production efficiency of their business. The prevention of pain and suffering for both the dam and calf aligns clearly with the BBSRC animal welfare strategies. The development of appropriate early warning systems is key to maximising the sustainability of UK and global agriculture.
Agency: GTR | Branch: BBSRC | Program: | Phase: Research Grant | Award Amount: 281.67K | Year: 2017
The challenges are to improve the quality and efficiency of primary lamb production and build new objective data feedback from meat processor to farmer on carcass quality and animal health phenotypes. Only around half of UK-produced lambs meet the target EUROP conformation and fat classification specification resulting in waste at the farm and processor levels. Farmers are not rewarded for producing higher quality lamb carcasses, for example with higher saleable meat yield, or a greater proportion of higher value cuts such as the loin. Crucially, genetic improvement is undertaken in purebred flocks with a disconnect between their crossbred progeny performance and carcass data from the abattoir, which could potentially inform selection decisions further up the breeding pyramid in the purebred sector. Additionally, improving carcass quality and yield in lambs must be undertaken without compromising their own health and welfare, and that of their purebred sires and dams (as it is likely that these two groups of traits are antagonistic at a genetic level). It is also important because some 1.75 million ewes (52.5%) that were mated to Texel sires were mated to sires from which replacements were retained for breeding (2012 figures). This livestock genomics project addresses key issues in primary livestock production by collecting, analysing, and exploiting state-of-the art genomic and new phenotypic data from meat sheep on hard to measure (HTM) traits. Building on the existing infrastructure of phenotype farms, data collection protocols and success of the current Agritech Catalyst project Using genomic technologies to reduce mastitis in meat sheep (Innovate Project no. 131791), we will extend the range of HTM phenotypes to include abattoir-derived disease and condemnation data with new carcass and meat quality data in pure and crossbred Texel lambs. We will use Computer Tomography (CT) and newly-created Visual Image Assessment (VIA) and carcass condemnation data to exploit the information to generate wealth using single nucleotide (SNP) genomic technology. By combining HTM disease and meat quality traits in tandem, we can ensure that genomic selection for carcass merit in crossbred Texel lambs does not take place by compromising disease status in the breeding ewe population. The aim is to use genomic technology to sell genomically-enhanced rams for exploitation and put in place the provision of a new genomic service for the future sheep breeding programmes in the wider industry. This will help the UK to become a world leader in agricultural technology, innovation and sustainability by exploiting new opportunities to develop and adopt new genomic technology, to increase productivity, and contribute to global food security. The project will use new technology and data capture systems to drive new information-led breeding structures on novel, economically-important traits for sustainable breeding of meat sheep. The results of the project will enable farmers to have clearer market (price) signals that adequately reflect the commercial value of their lambs produced, so that a higher proportion of lamb carcasses better meet the required specifications for lamb carcass quality and health. In this way, the UKs food security for lamb meat production will be enhanced, as will export-driven demand for high quality meat. The project will pioneer the use of visual imaging technology for sheep meat in the UK, and through benchmarking it alongside estimates of carcass composition (using CT), the commercial partner, Anglo Beef Processors (ABP) gain commercial intelligence that, ultimately, will be manifested throughout the sheep meat sector.
Agency: GTR | Branch: BBSRC | Program: | Phase: Research Grant | Award Amount: 306.31K | Year: 2016
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.
Agency: GTR | Branch: BBSRC | Program: | Phase: Research Grant | Award Amount: 203.27K | Year: 2016
Tail biting in pigs is a serious and unpredictable animal welfare problem for farmers worldwide. It results in losses for farmers of £10.4M a year in the UK alone, mainly from carcass condemnation. Before damaging tail biting begins, pigs hold their tails down. This project will develop a system to detect these tail posture changes using a 3D video system giving farmers advance warning of tail biting in time to intervene. We will 1) Collect continuous 3D video from pigs at a high risk of tail biting to capture the changes in tail posture pre-tail biting, 2) Provide a detailed behaviour analysis of tail posture changes and 3) Develop software algorithms to automate this. The project partners provide expertise in pig behaviour (SRUC), a route to market and algorithms for automated 3D video analysis (Innovent Technology Ltd), pork supply chain knowledge (Sainsburys) video expertise and access to a network of expertise in engineering and precision agriculture (Agri-EPI centre).