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Teagasc - The Irish Agriculture and Food Development Authority

Country: Ireland

Teagasc - The Irish Agriculture and Food Development Authority

182 Projects, page 1 of 37
  • Funder: EC Project Code: 841882
    Overall Budget: 196,591 EURFunder Contribution: 196,591 EUR

    Breeding for improved perennial ryegrass (PRG) cultivars to support pastoral based production systems for milk and meat is a critically important goal. However, genetic gains for traits such as forage yield and quality have very much lagged behind genetic gain for agronomic traits in cereals. One reason for this is the long breeding cycle in a typical PRG breeding programme, where a single cycle of selection can take 5-6 years. Genomic selection (GS) is a form of marker assisted selection that simultaneously estimates all loci, haplotype, or marker effects across the entire genome to calculate Genomic Estimated Breeding Values (GEBVs). The main advantage that GS could offer PRG breeding is to enable multiple cycles of selection to be achieved in the same time it takes to do a single cycle of conventional selection, thereby increasing the rate of genetic gain. Improving digestibility of the forage leads to an increase in animal performance, and is therefore an important target trait for forage breeders. Furthermore, it has already been shown that increases in organic matter digestibility can reduce methane emissions. Reducing methane emissions is a key target of the EUs climate and energy policy. In this action I will focus on developing and validating GS equations for feed parameters that are being used as model inputs into the Cornell Net Carbohydrate and Protein System (CNCPS). This CNCPS is currently being adapted to predict nutritional value to the grazing animal in pasture based production systems, and it is envisaged that it will be able to identify feed parameters limiting milk-solid production and thereby direct future forage breeding efforts. The work of this action will lead to a novel and innovative forage breeding programme that can select for multiple feed parameters to develop the ideal forage cultivars for pasture production systems.

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  • Funder: EC Project Code: 101106728
    Funder Contribution: 199,694 EUR

    Plant viruses continue to be one of the main threats to European agriculture, exacerbated by climate change, evolution of pesticide resistance, and loss of crop protection chemistries. Europe has ambitions of creating a more sustainable food system with a goal of reducing pesticide usage by 50% by 2030 as part of the European Farm to Fork Strategy. Achieving these goals requires robust integrated pest-management (IPM) approaches and real-time decision support systems (DSS) for farmers. In disease management there is a strong reliance on rapid, sensitive, and specific diagnostic tools built on a clear understanding of viral diversity. HealthyPlants will use high-throughput sequencing to (i) complete the first systematic survey of viruses of cultivated plants in Ireland and establish a baseline upon which to build improved diagnostic tools, (ii) establish the importance of arable margins and hedgerows as cereal virus reservoirs, and (iii) identify viruses on newly imported crops with potential phytosanitary risks. HealthyPlants will deliver the first database of viral sequences of cultivated plants on the island of Ireland, an open-access platform to support development of robust diagnostics within Europe.

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  • Funder: EC Project Code: 898013
    Overall Budget: 184,591 EURFunder Contribution: 184,591 EUR

    Low pH foods can attenuate the glycemic response to starch-rich foods. It has been demonstrated that lemon juice, due to its low pH (pH≈2.3), inhibited key digestive enzymes thereby interrupting gastric digestion of starch in vitro. This effect can significantly reduce the glycemic response in humans. In particular, adding lemon juice to a starch rich meal reduced the mean blood glucose concentration peak by 30%. Considering the panoply of food options available, it is likely that other combinations have similar effects but no work has been conducted to develop a consolidated knowledge base to exploit this strategy. GlucoMatchMaker will go beyond the state-of-the art by addressing this knowledge gap. The main goal is to develop and test the real-life effectiveness of the first mobile app to guide individuals on how to mix and match starchy foods with other foods/beverages to attenuate glycemic responses. The research work will employ multidisciplinary knowledge and methodologies and is divided into 4 parts (1) Selection and characterization of starch-rich foods, low-pH foods/beverages and of how their combination influences starch digestion in vitro (WP1). (2) Determination of the conditions of effectiveness of these combinations (in silico models) (WP2). (3) Development of the first mobile app that will integrate this knowledge to guide the user on how to mix and match starch-rich foods with others to lower their glycemic impact (WP3). (4) Test the effectiveness of the developed strategy in a real-life context (WP4). This project addresses the United Nations and EU target to reduce premature mortality from non-communicable diseases by one third as part of the 2030 Agenda for Sustainable Development. The research plan was developed in the framework of “H2020 Work Programme - Health, demographic change and wellbeing”, specifically the aim to “translate new knowledge into innovative applications and accelerate large-scale uptake and deployment”.

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  • Funder: EC Project Code: 101105558
    Funder Contribution: 199,694 EUR

    Today, there is a considerable desire to consume sustainable products due to healthy and ethical reasons, environmental sustainability, and issues related to greenhouse gas emissions. Ireland now specializes in producing beef, lamb, and dairy—which are the most emission-intensive foods—but plans to cut greenhouse emissions (50%) by 2030 and reach net zero emissions by 2050. Cheese manufacturing consumes significant quantities of energy (4.9-8.9 MJ/kg) and the study on the novel dairy-free cheeses has gained high interest due to the new opportunities offered by the worldwide market where it is predicted to rise to 3.90 billion USD by 2024. The dairy-free industry faces major challenges to formulate dairy-free cheeses with excellent nutritional properties, functionality, flavour, and texture—compared with the dairy-based cheeses. This study proposes to evaluate and manipulate interactions of plant proteins/carbohydrates/relevant ingredients through food material science, aiming to design an innovative dairy-free cheese with similar physicochemical, rheological, and sensory properties to the dairy-based cheese. This project will be carried out during a 2-year period at Teagasc with a 3-month secondment in University College Cork. Firstly, the physico-chemical interactions, structures and functional properties of materials will be studied and the most appropriate materials and their ranges will be determined (WP1). After producing some prototypes of dairy-free cheeses, the properties of them will be determined (WP2), and then, formulation(s) will be optimized (WP3). Finally, the quality of the optimized sample will be compared with commercially available cheeses (WP4). Results of this study will contribute to the industrial/sustainable production of dairy-free cheeses; thus, it will provide food security, sustainable lifestyles, and consumption patterns in alignment with current Teagasc goals, Horizon 2022, EU, and Irish government research initiatives.

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  • Funder: EC Project Code: 797162
    Overall Budget: 175,866 EURFunder Contribution: 175,866 EUR

    Potato breeding is a 10-year process that involves combining over 40 characteristics to produce varieties that have improved sustainability, utilisation and consumer characteristics. Genomic and marker assisted selection (GS and MAS) can make breeding faster/more efficient, increasing the rate of genetic gain and the ability to combine multiple traits. Strategies for GS to date have tended to employ many thousands of markers; however, the economic burden of deploying such approaches on thousands of plants annually in a breeding programme may restrict the adoption of GS. The objective of this action is to develop a novel, low cost, genome-scanning marker platform for use in simultaneous GS and MAS for multiple traits in potato breeding, by combining existing knowledge on the patterns of linkage disequilibrium in potato with recent advances in genotyping-by-sequencing approaches based on massively multiplex PCR. The marker system will target clusters of SNPs whose aggregate profile over distances covered by paired-end next generation sequencing (NGS) reads will allow them to be used as haplotags. Unlike bi-allelic SNPs, haplotags can discriminate multiple allelic variants in tetraploid potato. Approximately 400 loci at placed at 1Mb spacing throughout the euchromatic portion of the genome, and a further 100 SNPs linked to specific traits, will be targeted and assayed using an approach called GT-Seq, which uses combinatorial barcoding to allow multiplexing of thousands of samples in a single NGS run. The sequencing platform will be utilised in conjunction with 2 training populations to develop genomic prediction equations for yield and fry colour of potato. Subsequently, it will be deployed for combined GS and MAS on 1000 plants from the second field generation of a commercial potato breeding programme. The research and training activities in the fellowship proposal will equip a young postdoctoral researcher with the skills of a "next generation plant breeder".

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