In any given year, the impact of Teagasc research is a combination of the continuing impact of past research and the new impact of recent research. A new publication Teagasc Research Impact Highlights in 2022 highlights some of these new impacts achieved in 2022.
According to Teagasc’s Director of Research, Professor Pat Dillon: “Teagasc launched its Climate Action Strategy in December 2022 with the objective to reduce greenhouse gas emissions from agriculture by 25% by 2030 and become climate neutral by 2050. Many of the research impacts examples from 2022 will be critical in achieving these greenhouse gas reduction targets. They include research impacts such as reducing the age at slaughter from suckler beef, including a carbon sub index in the EBI for dairy cows and increasing the use of white clover on grassland farms to reduce chemical N use. Many of the other research impacts will significantly increase the sustainability of the Irish agri-food sector”.
The examples selected for the publication are from across Teagasc’s research programme.
They are not an exhaustive account of the new impacts of Teagasc’s research, but they do demonstrate the breadth of research carried out by Teagasc including soils and the environment, animal production, crop production, food processing, economics and social science. A snapshot of the impacts can be seen below.
The Economic Breeding Index (EBI) for dairy cow selection has been proven to reduce the carbon footprint per unit of milk produced. However, because of increased milk production associated with EBI selection, overall greenhouse gas emissions remains static. A sub-index within the EBI was needed that reflected overall emissions associated with individual animals, while also improving herd profit.
Research at Teagasc Moorepark, by Laurence Shalloo and colleagues, led to the development of a sub-index for the EBI and dairy-beef index (DBI) that’s now being used to rank dairy and beef bulls and cows on expected overall carbon emissions, and which reflects emission values on different traits within the two breeding indexes. Because the carbon sub-index is a component of the two breeding indexes, the overall index framework ensures the parents of the next generation improve profit while concurrently reflecting associated emissions.
Soil health is a strategic Teagasc goal for sustainable food systems. Classical methods of soil monitoring involve resource-intensive chemical analysis. Transitioning to spectroscopy and machine learning models can save time and costs while reducing chemical waste. Researchers at Teagasc Johnstown Castle (Karen Daly and colleagues) developed a systematic method for predicting multiple soil attributes without chemical analysis.
Soil samples were scanned using infrared spectroscopy to build a national spectral library, which was combined with laboratory reference data to develop a machine learning model that predicted a range of soil health attributes. Adopting this method saves greatly on time and cost when generating large datasets.
Plant-derived food processing generates enormous amounts of co-products. Most of these are rich sources of essential nutrients, such as proteins. Recovering such high-value proteins in a format usable by the food industry is desirable from an economic, environmental and sustainability perspective.
A patent filed by Teagasc scientists (Carlos Álvarez and colleagues at Teagasc Ashtown) to recover proteins from animal co-products attracted the interest of an Irish company seeking protein extraction technology. Funding from Enterprise Ireland enabled adaptation of the process for canola oil processing using residues from local suppliers, which was successfully scaled up and fully characterised.
This protein extraction technology optimised for canola material is adaptable to many other plant materials. It has short-term relevance at the national level by using unused plant materials to create high-value food ingredients and supplements. Long-term, it could enable international market expansion and adoption in the food industry. The technology also makes crop processing more environmentally-friendly and economically beneficial, positively impacting the food industry overall.
The Terrain-AI project is looking at what happens when land management changes – does the land emit greenhouse gases or absorb them?
The project has research sites across the country covering different soil types, land uses and habitats. Over 40 scientists are working on the project, which is led by the National University of Ireland Maynooth, with Stuart Green and colleagues at Teagasc.
These include geographers, ecologists and computer scientists, all collaborating using a new cloud-based portal that holds all data generated by the sensors, drones, aircraft and satellites that continually monitor the research sites. The scientists can then use models and machine learning methods to understand change and activity regarding emissions and land use.
The project has real-time and continual data collected from each site, creating an ongoing record. It has developed new solutions in land use understanding, such as detecting urban driveways and automatically mapping field boundaries correctly. The biggest impact is creating an evidence base for improving greenhouse gas emissions budgets from Irish land use, ensuring that targets and baselines reflect more closely the reality on the ground.
Professor Dillon concludes: “Teagasc’s vision is to be a globally recognised leader in developing innovative, science-based solutions for the sustainable transformation of Ireland’s land resources into products and services that benefit society. Each year, our research, advisory and educational activities contribute towards the achievement of our vision and make a real and tangible impact on farmers, policy and industry, some of which are highlighted in this publication”.
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