Recently, several EU countries, including the Republic of Ireland, have struggled to meet legally binding commitments to reduce ammonia emissions. Some farmers readily embrace abatement measures, whereas others reject them and it could be argued that farmers’ technology rejection decisions have been studied in less detail within the literature. However, understanding why some farmers reject recommended farming practices holds critical information that helps to inform policy, tailor government support schemes, and reduce pro-innovation biases. This study builds on the Grounded Theory method, data collected from focus group discussions with dairy and beef cattle farmers across eight key farming regions, and inductive thematic analysis. Three main themes, six subthemes and 26 codes were defined. The adoption of recommended bovine farming methods was hindered by affordability, compatibility, usability, availability, and information-related barriers. Participants also expressed frustration with unfair pressure to reduce agricultural emissions along with insufficient recognition of the environmental benefits provided by well-managed grasslands, the contributions cattle production makes to global food production, and the sustainability progress they have already achieved. We recommend that future reforms of the EU Common Agricultural Policy adopt a coordinated policy approach. It should simultaneously target all the nitrogen-related farm environmental issues and fully consider the local farmers perspectives in policy design and implementation. The reforms should include accessible support schemes for small-scale farm owners and more effective efforts to raise awareness about the causes and consequences of farm ammonia3 emissions, available abatement methods, and the correction of misinformation.

Afforestation is the most significant land-based strategy for enhancing carbon sequestration and mitigating climate change globally. However, in many countries, including Ireland, planting targets remain unmet despite the availability of financial incentives. The latest Common Agricultural Policy (CAP) reform introduced SmallScale Afforestation Measures (SSAMs) to encourage tree planting on Irish farms, but barriers and motivations for adoption remain poorly understood. Accordingly, this study examines both external factors (e.g. financial incentives) and intrinsic motives (e.g., beliefs, values, attitudes, and social norms) influencing the adoption of SSAMs, which is crucial for designing more effective and targeted afforestation policies. Using data from a nationally representative survey of 563 Irish farms, the extended Theory of Planned Behaviour was applied to distinguish the influence of different beliefs, subjective norm, and barriers. A Generalised Linear Model with Ordinal Regression was used to analyse farmers' adoption intentions. Results showed over half of farmers were either 'neutral' or 'unwilling' to adopt SSAMs, while 38.9%, 32.0%, and 45.1% were 'willing' to adopt tree planting of 'Any-kind', under CAP Pillar I 'Eco-scheme', and CAP Pillar II 'Agri-Environment-Climate Measures (AECMs)', respectively. Most background factors were not significant to adoption, and while socio-psychological factors emerged as particularly significant. Economic beliefs shaped farmers' attitudes toward AECM adoption, while environmental beliefs were key drivers for Eco-Scheme participation. Findings suggest that adoption can be improved by combining financial incentives with participatory training to engage influential peer groups. Addressing both economic concerns and environmental values is essential for enhancing the effectiveness of afforestation policies and increasing uptake.

Widespread adoption of technologies for greenhouse gas (GHG) emission mitigation is required to meet GHG reduction targets while maintaining levels of food production. In this context, improving our understanding of factors influencing behavioural change, including farmer views towards GHG mitigation, is required. Based on a representative sample of 526 farmers across various farm systems in the Republic of Ireland, this study takes an exploratory research focus and investigates the views of farmers towards their farm level GHG emissions by conducting a typology analysis that groups like-minded farmers together. Farmer views are first assessed on five-point Likert scales using nine statements. Principal component analysis (PCA) is applied to survey responses, revealing three components: one dealing with belief in ability and knowledge to reduce emissions, the second looking at environmental concern towards GHG emissions and the third gauging social mistrust felt by farmers as well as income prioritization. Subsequent cluster analysis shows four distinct farmer groups, which were then labelled based on their profile; Unconcerned, Ill-equipped, Concerned and Mistrusted. Finally, differences in farm and farmer characteristics across groups are examined using a series of statistical tests.

This paper analyses the economic effects of adopting greenhouse gas mitigation measures on Irish dairy and cattle farms. Using an unbalanced panel dataset from 2018 to 2022, we estimate a translog cost function to assess input price elasticities, focusing on grass and crop production for animal feed. Farms are grouped according to soil and climate conditions to capture heterogeneity in environmental performance. Our findings reveal consistent substitution patterns between home-produced forage and purchased feed, with three key insights: (i) substitution is stronger in dairy systems; (ii) farms adopting mitigation measures show greater substitution, likely due to higher investment capacity; and (iii) substitution is more prevalent under poorer environmental conditions, reinforcing the economic and environmental advantages of on-farm forage production. This research contributes to the literature by integrating environmental heterogeneity and mitigation strategies into input substitution analysis. The results underscore the need for aligned policy supports to promote the adoption of sustainable practices, particularly in the context of Ireland's climate targets.

This systematic review of 66 peer-reviewed articles (2007-2024) looks at the factors that influence the adoption of greenhouse gas (GHG) mitigation measures among farmers in the EU, the UK, Switzerland, New Zealand and Australia. The review is motivated by the need to accelerate GHG reductions in EU agriculture, while drawing on evidence from other high-income agricultural systems to broaden the empirical base. While existing research often highlights farm size, education and profitability as key determinants of GHG mitigation technology adoption, our review uses a binomial test to identify which factors reliably predict higher adoption and which exhibit context-specific or inconclusive effects. The results show three clear drivers. First, previous adoption of technology increases the willingness to adopt new, more advanced practices. Second, social influence is crucial, as positive peer networks and community norms can speed up adoption, while insular or conservative communities can slow it down. Third, government support and policies (carefully calibrated subsidies, credit-trading systems and infrastructure) reduce cost barriers and risk and lead to more widespread adoption of emission-reducing measures. Our results also show that demographic and economic factors work differently across contexts. Older farmers are more reluctant, but targeted training and social reinforcement can overcome age-related resistance. While high costs are a major barrier, having off-farm income or larger farm income does not guarantee adoption. Environmental awareness helps adoption, but only when backed up by tangible incentives and accessible information channels. Overall, an integrated approach that combines social, technical, and financial support appears key to accelerating GHG mitigation on farms in the EU and comparable high-income agricultural systems and to delivering agricultural contributions to the EU's 2030 climate targets.Key policy insights Prior experience with lower-risk mitigation or conservation practices strongly predicts uptake of advanced measures; policies should support 'stepping-stone' pathways rather than isolated actions.Strong farmer networks, peer influence and advisory support raise adoption, whereas insular or conservative communities hold it back; investing in farmer groups, advisory systems and peer-learning should be treated as a core climate policy tool.Targeted economic support such as subsidies, credit and infrastructure lowers upfront and ongoing cost barriers for smaller or lessprofitable farms; CAP eco-schemes and national programmes must reach these capital-constrained farms.Older age is a negative factor, yet demonstration-based and participatory training can soften reluctance; age- and skills-appropriate training for older farmers is a promising policy option.Schemes focused on productivity or income support risk sidelining mitigation; instruments with explicit climate conditions and measurable environmental outcomes are more likely to deliver GHG reductions, so aligning incentives with these climate goals is crucial.

Small-Scale Afforestation Measures (SSAMs) recently introduced under the Common Agricultural Policy, aim to help meet Ireland's afforestation goals and achieve net-zero emissions by 2050. However, little is known about farmers' willingness to adopt SSAMs and the factors influencing their intentions. This study explains farmers' intentions to adopt SSAMs using the Theory of Planned Behaviour in Ireland. A quantitative, cross-sectional survey was conducted with a nationally representative sample of Irish farmers (n = 563) through the Teagasc National Farm Survey. Structural Equation Modelling (SEM) was employed to examine the direct and indirect effects of Attitudes (ATT), Subjective Norm (SNs), and Perceived Behavioural Control (PBC) on farmers' intentions to adopt SSAMs. The results revealed that farmers were generally neutral in their willingness to adopt SSAMs, showing a slight preference for planting under the Agri-Climate Rural Environmental scheme. SEM analysis indicated that SNs were the strongest predictor of farmers' intentions, directly (beta = 0.25) and indirectly (beta = 0.39), positively influencing the intention. Among the SN related influences, the perceived financial importance of afforestation promoted by main influential bodies emerged as the most significant factor shaping farmers' intentions. ATT (beta = 0.44) was the second strongest predictor, with farmers holding positive environmental beliefs but slightly negative ATT towards the economic and land permanence aspects of SSAMs. PBC (beta = 0.23) also positively influenced intentions, with farmers reporting low control/confidence due to a lack of technical knowledge, limited access to expert advice, and administrative burdens. This study highlights the importance of social influences and the need for community-based knowledge-sharing to support farmers in adopting SSAMs.

There is little published research on dairy-beef heifer systems or comparisons of heifer and steer dairy-beef production. Furthermore, given its impact on the productivity and economics of dairy-beef systems, any comparison of gender must also consider potential interactions with stocking rate (SR). The objective of this study was to evaluate the variability of physical and economic performance, greenhouse gas emissions, feed-food competition and pasture land-use of dairy-beef production steer and heifer systems at differing stocking rates. Performance data from a two (gender: Steers and Heifers) x two (SR: Low and High) x two (breed-types: Early-maturing (EM) and Late-maturing (LM) factorial experiment was used to parameterize a bio-economic farm systems model. Low SR animals were heavier, had higher fat scores and better conformed at slaughter. High stocking rate resulted in greater carcass output per hectare and subsequently were, on average, 22 % more profitable than their Low SR counterparts. Late-maturing animals were found to be more profitable than early-maturing, and steers were more profitable than heifers. GHG emissions of the eight treatments investigated ranged from 10.7 to 17.7 kgs of carbon dioxide equivalents (kg CO2eq) per kilogram of carcass weight produced, with both High SR and heifer systems having lower GHG emissions per kg of product than their Low SR and steer counterparts. Human edible protein efficiency was only favorable for the steer systems. High SR systems had, on average, lower land use per kg of product than their Low SR counterparts. Results from this study indicate that no single treatment was optimal across the range of performance metrics considered.

The objective of this paper is to analyse the cost structure of pasture-based beef production in Ireland. Specifically, the paper assesses (i) farmers’ capacity to respond to price changes by substituting inputs; (ii) the optimality of the scale of production; and (iii) the optimal utilization of land. As differences in soil quality may alter the size of utilized land and affect farmers’ dependency on purchased or home-produced feed, a short-run translog cost model was estimated separately for three groups of farms with differing soil quality. For empirical implementation, Irish beef farm data from 2000 to 2011, obtained from the Teagasc National Farm Survey (NFS), were used. Results suggest that substitution possibilities are limited in Irish beef farms. The lack or limited substitution possibilities between types of feed suggests that beef farms are vulnerable to feed price increases. We find statistically significant evidence of allocative inefficiency irrespective of the quality of soil, which takes the form of over-utilization of purchased cattle relative to other inputs. Moreover, cost advantages can also be achieved if Irish farmers decrease beef production. Given that increases in methane emissions from higher cattle numbers could jeopardise the achievement of climate neutrality by 2050, the implications of results are of interest to agricultural and broader policy makers, industry stakeholders and society as a whole.

Objective: The objective of this study was to quantify the sustainability of representative dairy-beef farms in Ireland (AVE) and to compare these with dairy-beef farms participating in a farm improvement program (IMP) and research (RES) systems. The study aimed to determine the differences in technical performance and key sustainability indicators among these farm categories. Material and Methods: Within each farm category, dairy-beef systems differing in sire breed (early maturing, late maturing, and Holstein-Friesian), animal sex (steer and heifer), finishing age (ranging from 18 to 30 mo of age), and production system (finishing from grazing or indoor-based systems) were modeled using the Grange Results and Discussion: The average finishing age was 25.4, 23.8, and 20.6 mo on AVE, IMP, and RES, respectively. Results highlighted a wide range in net margins (from 185 to 806 per hectare; 1 = $1.05) for the systems modeled. Sex had the largest effect on profitability (steer greater than heifer), followed by finishing system (finishing from grazing systems greater than indoor systems) and breed type (late maturing greatest and Holstein-Friesian least). Greenhouse gas emissions of the 3 oxide equivalents (CO2eq) per kilogram of carcass weight ducers of human-edible protein, and all farms were net consumers of human-edible energy. economic, environment, labor, feed-food competition, and land-use perspective because none of the 3 farm categories investigated were without fault from a sustainable dairy- beef production perspective.

This paper aims to define a high spatial resolution model for estimating nitrous oxide (N2O) emissions from agricultural soils in Ireland. In 2020, N2O emissions from the management of agricultural soils represented 10% of the total national agricultural Greenhouse Gas (GHG) emissions. The high spatial resolution model employed here takes account of environmental factors that influence N2O emissions at a more disaggregated spatial scale than the Intergovernmental Panel on Climate Change (IPCC) national inventory-reporting framework. Activity data from the EU Farm Accountancy Data Network (for Ireland) is used in conjunction with biophysical and climatic data in a geographic information system (GIS) framework to estimate N2O emissions at a sub-national level. Results indicate that N2O emissions are 5% lower by applying this high spatial resolution modelling approach at the farm level compared to the baseline model (Tier 2). Nevertheless, 25% of the farms in the sample analysed had an overestimation in their N2O emissions of 20%, and another 25% of the farm sample had an average underestimation in their N2O emissions by 19%, a consequence of the variation of environmental factors among farms. Using a data panel regression, results confirm that the environmental factors examined are statistically significant within the model proposed, and with a simulation, we assessed the switching of 20% of CAN fertiliser for protected urea. According to the high spatial model, this mitigation measure can reduce N2O emissions from inorganic fertilisers by up to 15%. However, the reduction is conditioned by local factors and environmental conditions.

CONTEXT: Agriculture and food systems contribute significantly to climate change. Greenhouse gas (GHG) emissions intensity from beef production are high when compared to other livestock production systems and, therefore, mitigation of these emissions is urgently required. In many countries dairy-beef is making a large and growing contribution to total beef output thereby reducing net emissions given the lower emissions intensity of beef originating from the dairy herd when compared to specialized beef-cow systems. GHG emissions from dairy beef systems can be further reduced by adopting best practice and mitigation technologies. OBJECTIVES: The objectives of this study were to (1) evaluate a range of management practices to reduce GHG emissions for pasture-based beef cattle production systems, (2) model the individual and combined impacts of these management practices on GHG emissions from dairy-beef systems, and (3) identify any trade-offs between GHG emissions mitigation, farm profitability, food security and land use.METHODS: A farm level bioeconomic systems model was modified to evaluate spring-born, steer production systems finishing cattle at differing slaughter ages and from contrasting forage-based finishing diets (grazed grass or grass silage, each supplemented with concentrates). Mitigation measures included earlier slaughter age, optimal slurry management, urease inhibitors for nitrogen (N) fertilizers, replacing cereals with 'by-products' in concentrate feed rations and incorporating clover in grassland pastures.RESULTS AND CONCLUSIONS: Combining mitigation strategies reduced dairy-beef systems GHG emissions intensity by an average of 21%. Incorporating clover in grassland pastures was found to be the most profitable stand-alone mitigation strategy increasing net margin by an average of 18%. Substituting by-products for barley in a concentrate ration converted all systems into net producers of human edible protein; otherwise, steer systems finishing at pasture during the third grazing season were the only net producers of human-edible protein. However, finishing at pasture during the third grazing season increased GHG emissions per animal and per kilogram of beef carcass.SIGNIFICANCE: Within a grass-based dairy-beef system, such as that modelled in this study, a number of complementary GHG emissions mitigation strategies can be implemented, without making substantive changes to the production system, while simultaneously improving farm profitability.

The Irish Climate Action Plan published in 2019 outlines the significant role that agriculture will have to take to achieve a national reduction in greenhouse gas emissions in Ireland. Recent growth in the agricultural sector, especially of the bovine population, however, has led to a continuous increase of agricultural greenhouse gas emissions. For the agricultural sector to meet its potential, greenhouse gas emission targets could be challenging. The agricultural sector model CAPRI is used to investigate the impact of a 20 per cent EU-wide agricultural greenhouse gas mitigation target on the Irish agriculture sector. Three scenarios, allowing the endogenous implementation of mitigation technologies, show the possible impact range of such a policy target. The research indicates that the Irish agriculture sector can achieve the set mitigation target by adapting livestock production systems, resulting in agricultural efficiency gains, and by implementing specific mitigation technologies. Without a mandatory mitigation target but with subsidies granted, changes in the level of agricultural greenhouse gas emissions are marginal, and voluntary adoption is limited. Subsidising the implementation of mitigation technologies can buffer the impact that a mitigation target will have on the agriculture sector in Ireland while achieving the set greenhouse gas emissions reduction. Total agricultural income increases if a mandatory target is set due to strong structural changes. As the analysis shows, the emission reduction is partly achieved through a reduction in total production and strong competitors outside of the EU appear to fill the occurring supply gap. This could potentially lead to a carbon leakage effect with production and emissions shifting towards strong ruminant-based producing countries.Key policy insights Mitigation targets in the agricultural sector should be mandatory to achieve a sufficient reduction in greenhouse gas emissions.Subsidy payments targeted towards the implementation of mitigation technologies buffer the economic impact that a mitigation target will have on the productivity of the agriculture sector and enhance the scale of implemented mitigation technologies.Total agricultural income increases if a mandatory target is set due to strong structural changes.Mitigation targets can divert agricultural trade flows and potentially lead to carbon leakage.

Growing awareness of global challenges and increasing pressures on the farming sector, including the urgent requirement to rapidly cut greenhouse gases (GHG) emissions, emphasize the need for sustainable production, which is particularly relevant for dairy production systems. Comparing dairy production systems across the three sustainability dimensions is a considerable challenge, notably due to the heterogeneity of production conditions in Europe. To overcome this, we developed an ex post multicriteria assessment tool that adopts a holistic approach across the three sustainability dimensions. This tool is based on the DEXi framework, which associates a hierarchical decision model with an expert perspective and follows a tree shaped structure; thus, we called it the DEXi-Dairy tool. For each dimension of sustainability, qualitative attributes were defined and organized in themes, sub-themes, and indicators. Their choice was guided by three objectives: (i) better describe main challenges faced by European dairy production systems, (ii) point out synergies and trade-offs across sustainability dimensions, and (iii) contribute to the identification of GHG mitigation strategies at the farm level. Qualitative scales for each theme, sub-theme, and indicator were defined together with weighting factors used to aggregate each level of the tree. Based on selected indicators, a list of farm data requirements was developed to populate the sustainability tree. The model was then tested on seven case study farms distributed across Europe. DEXi-Dairy presents a qualitative method that allows for the comparison of different inputs and the evaluation of the three sustainability dimensions in an integrated manner. By assessing synergies and trade-offs across sustainability dimensions, DEXi-Dairy is able to reflect the heterogeneity of dairy production systems. Results indicate that, while trade-offs occasionally exist among respective selected sub-themes, certain farming systems tend to achieve a higher sustainability score than others and hence could serve as benchmarks for further analyses.

CONTEXT.Global demand for grass-based beef systems is increasing with a growing proportion of beef output in many countries originating from the dairy herd. Concurrently, concerns around the environmental impact of food, particularly that derived from ruminants, and food security is growing in prominence. Therefore, a compre-hensive analysis of the sustainability of dairy-beef systems is required.OBJECTIVES: The objectives of this study were to (1) augment an existing farm-level bioeconomic model to permit greenhouse (GHG) emissions and feed-food analysis of dairy-beef systems and (2) use this model to asses the performance of grass-based dairy-beef steer production systems.METHODS: The developed farm-level model was used to evaluate production systems finishing males as steers on three contrasting soil types and differing with respect to sire breed (early-maturing, late-maturing and dairy), age at slaughter (20, 24 and 28 months of age) and finishing diet (grazed grass or grass silage, each supplemented with concentrates).

The European Union Common Agricultural Policy reforms since the early 2000s allowed for the implementation of different types of agricultural subsidies, such as direct support in the form of decoupled and coupled payments, or agri-environmental payments. As a result, there are significant differences in agricultural subsidies granted in each member state of the European Union. However, there is limited cross-country comparative empirical evidence regarding the effects of the implementation of different levels and types of subsidies on farm efficiency. Using farm level data for beef farms in Ireland, France, Great Britain, and Germany between 2005 and 2012, we implement the stochastic metafrontier proposed by Huang et al. (2014) and attempt to correct for endogeneity applying the method in Shee and Stefanou (2015). Using these approaches, we contribute to the literature by consistently comparing cross-country farm performance, as well as exploring the disaggregated effects of different types of subsidies on farm level technical efficiency and on the technology gaps. Our estimates show that although beef farms included operate on average close to the global frontier, there is scope to improve farm level managerial performance. However, they do operate close to the global frontier, although not on it. We also find evidence that implementing full decoupling benefited efficiency, whereas implementing partial decoupling might have hindered technical efficiency improvements for beef farms, as well as technology catch up.

Beef quality assurance initiatives have been developed to assure consumers of the quality and safety of supplied beef, as well as the environmental-orientation of farm production practices. However, the potential economic benefits of quality schemes to European beef cattle farmers have been overlooked. This paper uses farm-level data to identify the drivers of Irish farmers' participation in Bord Bia's Beef and Lamb Quality Assurance Scheme (BLQAS) in 2012, and assesses the impact of participation on farm gross margins. After controlling for potential self-selection bias, we cannot find reliable evidence that the gross margins of participants in beef quality assurance schemes have been affected by their decision to participate. Consequently, lack of financial incentives can be a barrier to farmer participation in beef quality assurance schemes [EconLit citations: L25, M21, Q12].

Agricultural Greenhouse Gas (GHG) emissions in Ireland are projected to increase up to 21 Mt CO2eq by 2030 mainly driven by increased dairy cow numbers and increased nitrogen fertiliser use. In response to the growing public awareness of the GHG emissions' environmental impact, the Irish government published the Climate Action Plan in 2019, which identifies the agricultural sector's leading role in reducing GHG emission and increasing carbon removals to achieve the national GHG emission targets by 2030. Marginal Abatement Cost Curves (MACCs) on Irish GHG emissions have projected the total technically feasible mitigation potential for the Irish agriculture, forestry and land use (AFOLU) sector to be sufficient enough to achieve the set targets by 2030. Although these mitigation measures are available and when implemented, would mostly lead to a win-win situation, the voluntary adaptation rate by farmers is low. This study addresses the most significant determinants of voluntary adoption of mitigation measures by systematically examining existing literature on how and to what extent non-price determinants affectthe voluntary adoption rate of technically feasible mitigation measures in the Irish afolu sector. The main identified nonprice determining factors were the degree of farmers' awareness regarding man-made GHG emissions, receiving agrienvironmental advice, implementation costs, profitability and size of farms, land quality and the type of farm enterprise. Integrating the gained results in the former macc analysis enabled us to adopt the implementation rates of the cost-efficient afolu mitigation measures accordingly. The non-price determinants impact the voluntary uptake rate of AFOLU mitigation measures to the extent that the adjusted total Irish AFOLU abatement potential is 47% lower than technically feasible. Considering that 51.6% of the total estimated AFOLU abatement potential in 2030 is offset through Irish forestry, which at current afforestation rate will turn into a net carbon source by 2035, a significant gap occurs to any potential Irish and EU GHG reduction targets. To substantially help bring the nexus between agricultural development and GHG emission targets in Ireland closer together, policy measures, that differentiate between the different type of AFOLU mitigation measures, need to be implemented to enhance the uptake rate of cost-beneficial and cost-effective measures. This would have the potential to reduce the level of agricultural GHG emissions by 2030 in a way that it would converge towards possible EU and Irish GHG emission reduction targets.