A Comparison of Three Systems of Milk Production With Different Land Use Strategies

Under the Luxemburg agreement FAPRI-Ireland (Breen & Hennessey 2003) projects that milk price will decrease by 5.0 to 5.5 c/l because of reductions in support for butter and skimmed milk powder. These changes mean that many dairy farmers need to reappraise their systems of milk production and consider necessary adjustments that will ensure viability in the longer term. The objective of this study was to model three different systems of milk production in scenarios where quota, cow numbers or land was restricted

Optimising Financial Return from Grazing in Temperate Pastures

Key Points The increased interest in pasture-based systems of milk production in recent years has been largely generated through lower product prices and rising costs of production. Pasture based systems of milk production decrease unit production costs, through lower feed and labour expenses, and reduced capital investment. Systems utilising grazed pasture will be optimised in regions where pasture production potential is high, variability in seasonal pasture supply and quality is low, manufacturing milk accounts for a large proportion of total production, and where large areas of land are available at relatively low cost. Pasture based systems may allow greater global sustainability (through reduced use of fuel, herbicides and pesticides), increased product quality, improved animal welfare and increased labour efficiency

Development of an index to rank dairy females on expected lifetime profit

peer-reviewedThe objective of this study was to develop an index to rank dairy females on expected profit for the remainder of their lifetime, taking cognizance of both additive and nonadditive genetic merit, permanent environmental effects, and current states of the animal including the most recent calving date and cow parity. The cow own worth (COW) index is intended to be used for culling the expected least profitable females in a herd, as well as inform purchase and pricing decisions for trading of females. The framework of the COW index consisted of the profit accruing from (1) the current lactation, (2) future lactations, and (3) net replacement cost differential. The COW index was generated from estimated performance values (sum of additive genetic merit, nonadditive genetic merit, and permanent environmental effects) of traits, their respective net margin values, and transition probability matrices for month of calving, survival, and somatic cell count; the transition matrices were to account for predicted change in a cow’s state in the future. Transition matrices were generated from 3,156,109 lactation records from the Irish national database between the years 2010 and 2013. Phenotypic performance records for 162,981 cows in the year 2012 were used to validate the COW index. Genetic and permanent environmental effects (where applicable) were available for these cows from the 2011 national genetic evaluations and used to calculate the COW index and their national breeding index values (includes only additive genetic effects). Cows were stratified per quartile within herd, based on their COW index value and national breeding index value. The correlation between individual animal COW index value and national breeding index value was 0.65. Month of calving of the cow in her current lactation explained 18% of the variation in the COW index, with the parity of the cow explaining an additional 3 percentage units of the variance in the COW index. Females ranking higher on the COW index yielded more milk and milk solids and calved earlier in the calving season than their lower ranking contemporaries. The difference in phenotypic performance between the best and worst quartiles was larger for cows ranked on COW index than cows ranked on the national breeding index. The COW index is useful to rank females before culling or purchasing decisions on expected profit and is complementary to the national breeding index, which identifies the most suitable females for breeding replacements

A Response to the Draft National Mitigation Plan. Teagasc submission to the Department of Communications, Climate Action & theEnvironment

Teagasc SubmissionThis submission details the mitigation potential of agriculture to shortly be published as an update to the Marginal Abatement Cost Curve (MACC) for Agriculture and and describes how the MACC mitigation strategies relate to the measures in the National Mitigation Plan

Scenarios to limit environmental nitrogen losses from dairy expansion

peer-reviewedIncreased global demand for dairy produce and the abolition of EU milk quotas have resulted in expansion in dairy production across Europe and particularly in Ireland. Simultaneously, there is increasing pressure to reduce the impact of nitrogen (N) losses to air and groundwater on the environment. In order to develop grassland management strategies for grazing systems that meet environmental targets and are economically sustainable, it is imperative that individual mitigation measures for N efficiency are assessed at farm system level. To this end, we developed an excel-based N flow model simulating an Irish grass-based dairy farm, to evaluate the effect of farm management on N efficiency, N losses, production and economic performance. The model was applied to assess the effect of different strategies to achieve the increased production goals on N utilization, N loss pathways and economic performance at farm level. The three strategies investigated included increased milk production through increased grass production, through increased concentrate feeding and by applying a high profit grass-based system. Additionally, three mitigation measures; low ammonia emission slurry application, the use of urease and nitrification inhibitors and the combination of both were applied to the three strategies. Absolute N emissions were higher for all intensification scenarios (up to 124 kg N ha−1) compared to the baseline (80 kg N ha−1) due to increased animal numbers and higher feed and/or fertiliser inputs. However, some intensification strategies showed the potential to reduce the emissions per ton milk produced for some of the N-loss pathways. The model showed that the assessed mitigation measures can play an important role in ameliorating the increased emissions associated with intensification, but may not be adequate to entirely offset absolute increases. Further improvements in farm N use efficiency and alternatives to mineral fertilisers will be required to decouple production from reactive N emissions

An Analysis of the Cost of the Abatement of Ammonia Emissions in Irish Agriculture to 2030

peer-reviewedThis analysis quantifies the potential to abate national ammonia (NH3) emissions up to 2030. This report is an updated marginal abatement cost curve (MACC) analysis where Teagasc has quantified the abatement potential of a range of ammonia mitigation measures, as well as their associated costs/benefits (see Lanigan et al. 2015 for previous analysis). The objective of this analysis is to quantify the extent and costs associated with meeting future ammonia emission targets that were negotiated as part of the amended Clean Air Policy Package. The requirement to reduce ammonia emissions is urgent, both in terms of compliance with the National Emissions Ceilings Directive (NECD), and as a principal loss pathway for agricultural nitrogen (N). Improvement of N efficiency is a key focus for improving farm efficiency and sustainability as well as reducing the ammonia, nitrate and greenhouse gas (GHG) footprint of agriculture. This is particularly relevant in the context of the national strategies on the development of the agri-food sector: Food Wise 2025, Ag-food strategy 2030 and Ag-Climatise (currently under development) and the newly unveiled EU Farm to Fork Strategy, which is a part of the European Green Deal. Under the baseline scenario (S1), agricultural ammonia emissions are projected to increase by 9% (without any mitigation) by 2030 relative to 2005 levels. While these increases are small in comparison to the targeted increase in agricultural output, they will provide a major challenge to meeting emissions targets, particularly as agriculture comprises over 99% of national emissions. The analysis presented in this report seeks to quantify the ammonia mitigation potential under likely uptake pathways. This is not an exhaustive analysis of all mitigation measures, but represents an assessment of best available techniques, based on scientific, peer-reviewed research carried out by Teagasc and associated national and international research partners. Indeed, any future changes in the sector or in the national emission inventory calculations will require further analysis of the applicability of ammonia mitigation techniques, particularly in terms of housing and storage but also in the context of other reactive N1 emissions. It should also be noted that some mitigation measures, particularly those related to nitrogen application to soils, could result in either higher greenhouse gas emissions or higher nitrate leaching. Compared to a future where no mitigation measures are deployed to address emissions, by 2030 the average technical abatement2 potential was estimated to be approximately 15.26 kt NH3 at a net cost of €10.86 million per annum. However, it should be noted that the net cost (€10.86 million) is comprised of 6 measures that are cost negative (-€22.21 million) and 7 measures that are cost positive (€33.07) and that some of the cost negative measures are predicated on efficiency gains driven by best management practice adoption (e.g. liming and clover measures with associate chemical N reductions). Amongst the thirteen mitigation measures selected for this analysis, 80% of the mitigation potential can be achieved by the full implementation of the mitigation pathways for protected urea and low emission slurry spreading (LESS) techniques for bovines. It should be stressed that this is an assessment of the maximum abatement potential and realising this level of abatement in practice will be extremely challenging. Any increase in agricultural activity beyond the baseline scenario will increase absolute emissions. The level of mitigation achievable is based on the draft AgClimatise measures any delay or reduction in the uptake of these measures will reduce the mitigation achieved. It must also be ensured that all mitigation measures should, where possible, be synergistic with reductions in greenhouse gas emissions and N loss to water

2022 Review of Data-Driven Plasma Science

Data-driven science and technology offer transformative tools and methods to science. This review article highlights the latest development and progress in the interdisciplinary field of data-driven plasma science (DDPS), i.e., plasma science whose progress is driven strongly by data and data analyses. Plasma is considered to be the most ubiquitous form of observable matter in the universe. Data associated with plasmas can, therefore, cover extremely large spatial and temporal scales, and often provide essential information for other scientific disciplines. Thanks to the latest technological developments, plasma experiments, observations, and computation now produce a large amount of data that can no longer be analyzed or interpreted manually. This trend now necessitates a highly sophisticated use of high-performance computers for data analyses, making artificial intelligence and machine learning vital components of DDPS. This article contains seven primary sections, in addition to the introduction and summary. Following an overview of fundamental data-driven science, five other sections cover widely studied topics of plasma science and technologies, i.e., basic plasma physics and laboratory experiments, magnetic confinement fusion, inertial confinement fusion and high-energy-density physics, space and astronomical plasmas, and plasma technologies for industrial and other applications. The final section before the summary discusses plasma-related databases that could significantly contribute to DDPS. Each primary section starts with a brief introduction to the topic, discusses the state-of-the-art developments in the use of data and/or data-scientific approaches, and presents the summary and outlook. Despite the recent impressive signs of progress, the DDPS is still in its infancy. This article attempts to offer a broad perspective on the development of this field and identify where further innovations are required