Research Impact Highlights - Environment
Reducing carbon footprint of beef
Donall Fahy and Padraig French (AGRIP)
Reducing the age at which beef cattle are slaughtered in Ireland is a key strategy to reduce methane production from the national herd. Suckler-bred progeny are slaughtered on average at 26.6 and 27.4 months for heifers and steers, respectively, four to five months longer than achieved in research environments.
Established in collaboration with Teagasc and Dawn Meats in 2015, Newford farm operates in a commercial environment. A key focus is achieving a reduction in slaughter age on a pasture-based diet while maintaining carcass weight. 2021 spring-born heifers were slaughtered at 17.9 months and steers at 21 months. Newford’s carbon footprint is 14% lower than the national average, reducing labour, winter forage and housing requirements, and reducing bought-in supplement onto the farm. Performance was driven by three key technologies: genetics, winter feeding management, and grassland management.
Newford farm is used extensively by the Teagasc advisory and education teams to communicate, in a demonstrable environment, key technologies underpinning suckler beef production. This will give confidence to farmers that reducing age at slaughter can also deliver increased profitability, and Newford has demonstrated the technologies to achieve higher profit and lower carbon emissions.
14% lower carbon footprint than the national average
[pic credit] Andrew Downes
Improving water quality
Per-Erik Mellander, Jason Galloway, Daniel Hawtree (CELUP)
Phosphorus is needed for food production, yet it affects water quality through both point-source and diffuse pollution, point-sources being easier to control. Finding ways to reduce diffuse pollution is more challenging, requires knowledge on how phosphorus moves to water, but lacks standardised evaluation.
The Agricultural Catchments Programme, established to monitor the effectiveness of EU Directive ‘Good Agricultural Practice for Protection of Waters’, has collected over ten years of data on phosphorus concentration and streamflow from six different catchments. This was used to introduce a method describing phosphorus mobilisation and delivery with one index number each. Estimating these numbers over several years provided insights to diffuse phosphorus pollution over time.
Environmental quality standards were often exceeded in three investigated catchments due to different risks of phosphorus loss; the new screening method is able to identify dominant risk types. Characterising risks in this way allows how changes to land management and climate will impact water quality to be assessed ahead of time. Such information is useful for supporting sustainable land management, and to guide policy development to mitigate water quality risk, allowing for investigations into how climate and land use changes affect phosphorus loss risks. This is needed to meet requirements under the Water Framework Directives.
Green chemistry for soil analysis
Karen Daly, Felipe Bachion de Santana and Giulia Bondi (CELUP)
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 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. The Signpost Programme at Teagasc collected soil samples from over 100 participating farms, and baseline soil health data were generated using spectral models.
Analysis using classical methods, performed by an external commercial lab and Teagasc soil labs, have an estimated total cost of €110,540. Using spectroscopy, costs were estimated at €3,420, including selecting 2% of samples for commercial analysis for validation. National monitoring for soil health could be expensive and spectral models have commercialisation potential. An Invention Disclosure is currently being drafted by Teagasc for potentially licensing spectral libraries and models.
Impact Pathway: Technology Development and Adoption; Capacity Building.
[pic credit] Andrew Downes
ROADRUNNER – Farm roadway runoff assessment
Owen Fenton, Karen Daly and Patrick Tuohy (CELUP)
Waters, soils and sediments on farm internal roadways can become soiled by livestock excrement, leading to increased nutrient concentrations in farm roadway runoff, which can enter waterways, negatively impacting water quality.
The ROADRUNNER project quantified the scale of this problem, highlighting that nitrogen and phosphorus concentrations in soiled runoff waters are up to 10 times higher than expected. Phosphorus concentrations trapped in roadway sediments can remain stored in the ground for long periods, released into waterways during rainfall, causing year-round waterway pollution.
The project found that soiled waters have the highest risk of entering waterways from open ditches connected to farmyards. There are typically three to four such areas on any given farm, which lead directly to rivers. The project co-developed the Farm Roadway Visual Assessment Booklet with farmers and Teagasc’s Agricultural Sustainability Support and Advisory Programme, used by farmers and advisors to pinpoint water connectivity areas on farms.
The project identified key intervention points including: 100-metre radius around the farmyard, underpasses and associated waiting areas, water troughs along roadways, junctions or anywhere that impedes cow flow. Low-cost diversion bars placed 25 metres apart were trialled to protect waters from runoff.
Other contributors: University of Limerick.
Funding: Department of Agriculture, Food and the Marine and Environmental Protection Agency co-funded.
Impact pathway: Technology Development and Adoption; Capacity Building; Policy Influencing.
[pic credit] Teagasc
Identifying solutions to sustainability in the European beef sector
Maeve Henchion, Richard Lynch (REDP)
With a significant proportion of Irish and European farmers dependent on direct supports for viability, and concerns around greenhouse gases and animal welfare, the beef sector faces sustainability challenges, responses to which could improve through collective knowledge exchange.
The BovINE project drew on farmers and researchers across Europe, identifying near- or practice-ready solutions for increased farming system sustainability, collating this onto a shared learning platform, with content freely available to industry.
The BovINE platform helps actors within Europe’s beef sector support demand-driven innovation. Its implementation has captured solutions covering 340 topics across four themes – Socioeconomic Resilience, Animal Health & Welfare, Production Efficiency & Meat Quality, and Environmental Sustainability. Results were communicated to the 1,724 external contacts registered to the BovINE network - and through events and media - and are being used by farmers, farm advisors and educators across Europe and further afield to inspire and support innovation implementation.
The potential contribution of these solutions to the EU’s Green Deal and Farm to Fork objectives has been shared with policy-makers. A key message is that farmers are innovators in their own right, already addressing sustainability challenges.
Other contributors: Irish Farmers’ Association and all BovINE project partners.
Funding: Horizon Europe.
Impact Pathway: Technology Development & Adoption; Capacity Building; Policy Influencing.
[pic credit] Teagasc
New insights to climate change decision-making
Stuart Green, Reamonn Fealy, Gary Lanigan, Cathal Buckley, Jesko Zimmermann, Mohana Logakrishnan, Jennifer Floody and Kevin Carolan, and Hugh Fitzpatrick (REDP)
Led by National University of Ireland Maynooth, the Terrain-AI project is a direct response to the challenge of understanding the impact of human activity on land use and climate change. The project answers questions about 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, including geographers, ecologists and computer scientists, 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.
Contact: firstname.lastname@example.org and email@example.com
Other contributors: National University of Ireland Maynooth.
Funding: Science Foundation Ireland and Microsoft.
Impact Pathway: Capacity Building.
[pic credit] George Burba/shutterstock.com