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High spatial-scale data used to improve implementation efficiency of agri-environmental measures. Targeted implementation about 50 % more effective for aquatic N pollution than GHG emissions. A targeted reduction of 25 % in aquatic N pollution co-reduced GHG emissions by 59 %. Requirements for substantial reductions in environmental pollution such as nitrogen (N) loading to aquatic environments, and in greenhouse gase (GHG) emissions impose challenges for agriculturally-intensive regions in Europe. Here we use afforestation to illustrate how high-spatial resolution data can be used to improve the efficiency of implementation of an environmental measure. Since afforestation of agricultural land has the potential to reduce both aquatic N load and GHG emissions, targeting the reduction of one pollutant will also affect the non-targeted pollutant. We developed a method to use nationally-available, high-resolution data to minimise the agricultural area selected for potential afforestation, for a given reduction of N load or GHG emissions, and assess the co-reduction in the non-targeted pollutant. To illustrate the effect of imposing policy restrictions on the implementation of measures, two restrictions were investigated; limitations on the maximum proportion of each farm that could be afforested and threshold proportions of the farm area, above which the whole farm must be afforested. For N load, both the N leaching below the root zone and the efficiency of denitrification between the bottom of the root zone and the recipient aquatic ecosystem were significant factors determining the selection of fields for afforestation. Since N leaching was the only location-dependent GHG emission source and these were only a minor contributor to the total GHG emissions, the selection of land for afforestation to reduce GHG emissions depended more on farm-scale than field-scale characteristics. In a case study area, the availability of high-resolution data allowed the use of a targeted afforestation selection method that significantly reduced the land area required, relative to a non-targeted approach. With the targeted approach, reducing N load or GHG emissions by 25 % of the maximum potential reduction required the afforestation of 14 % and 18 %, respectively, of the case study area. This represented reductions in area of 42 % and 24 % compared to the untargeted approach. Measures targeting one pollutant also substantially reduced the non-targeted pollutant. We conclude that the implementation efficiency of some environmental policy interventions in agriculture depends on the availability of high-resolution agricultural and landscape data, and adequate methods to utilize these data. [ABSTRACT FROM AUTHOR]

Abstract Spatially detailed information on agricultural nitrogen (N) budgets is relevant to identify regions where there is a need for a reduction in inputs in view of various forms of N pollution. However, at the scale of the European Union, there is a lack of consistent, reliable, high spatial resolution data necessary for the calculation of regional N losses. To gain insight in the reduction in uncertainty achieved by using higher spatial resolution input data. This was done by comparing spatially disaggregated agricultural N budgets for Denmark for the period 2000–2010, generated by two versions of the European scale model Integrator, a version using high spatial resolution national data for Denmark (Integrator-DK) and a version using available data at the EU scale (Integrator-EU). Results showed that the national N fluxes in the N budgets calculated by the two versions of the model were within 1–5% for N inputs by fertilizer and manure excretion, but inputs by N fixation and N mineralisation differed by 50–100% and N uptake also differed by ca 25%, causing a difference in N leaching and runoff of nearly 50%. Comparison with an independently derived Danish national budget appeared generally to be better with Integrator-EU results in 2000 but with Integrator-DK results in 2010. However, the spatial distribution of manure distribution and N losses from Integrator-DK were closer to observed distributions than those from Integrator-EU. We conclude that close attention to local agronomic practices is needed when using a leaching fraction approach and that for effective support of environmental policymaking, Member States need to collect or submit high spatial resolution agricultural data to Eurostat. Graphical abstract Unlabelled Image Highlights • Spatially disaggregated agronomic nitrogen (N) budgets for Denmark are compared. • Effects of using national rather than EU input data are analysed. • Detail of data causes large differences in N excretion and N losses to air and water. • Results obtained with detailed Danish input data were closer to observed distributions. • Good policy support requires high spatial resolution input data. [ABSTRACT FROM AUTHOR]

Livestock production systems can be major sources of trace gases including ammonia (NH 3 ), the greenhouse gases methane (CH 4 ) and nitrous oxide (N 2 O), and odorous compounds such as hydrogen sulphide (H 2 S). Short-term campaigns have indicated that acidification of livestock slurry during in-house storage can reduce NH 3 emissions, and also may influence other emissions. In this study, emissions of NH 3 were quantified by measuring continuously during three complete finishing pig production periods of about 10 weeks each, and emissions of CH 4 and H 2 S less frequently. Emissions were determined from sections with 30–32 pigs with or without daily adjustment of slurry pH to below 6. Ammonia losses from reference sections with untreated slurry were between 9.5 and 12.4% of N excreted, and from sections with acidified slurry between 3.1 and 6.2%. Acidification reduced total emissions of NH 3 by 66 and 71% in spring and autumn experiments, and by 44% in the summer experiment. Regression models were used to investigate sources and controls of NH 3 emissions. There was a strong relationship between NH 3 emissions and ventilation rate during spring and autumn, but less so during summer where ventilation rates were generally high. It was concluded that the contribution from floors to NH 3 emissions was <50%. There was some evidence for reduced CH 4 emissions from acidified slurry, but CH 4 emissions were generally low and apparently dominated by enteric fermentation. No effect on N 2 O emissions was observed. The effect of acidification on emissions of H 2 S differed between experiments. Implications of slurry acidification for subsequent field application, including N and S availability, and soil pH, are discussed. [ABSTRACT FROM AUTHOR]

Abstract: A procedure for the assessment of emissions of nitrogen (N) species (ammonia, nitrous oxide, nitric oxide, di-nitrogen) from the manure management system is developed, which treats N pools and flows including emissions strictly according to conservation of mass criteria. As all relevant flows in the husbandry of mammals are depicted, the methodology is considered a Tier 3 approach in IPCC terminology or a detailed methodology in UN ECE terminology. The importance of accounting for all N species is illustrated by comparing emission estimates obtained using this approach with those obtained from the application the present detailed/Tier 2 methodology. [Copyright &y& Elsevier]

Identifies proteins that interact with the CD2 cytoplasmic region and demonstrates that there is a direct link through human CD2AP (CMS) and CIN85 with the actin capping protein, CAPZ. Effect of overexpression of the C-terminal region of CD2AP on antigen-specific interleukin-2 production; Importance of the interactions of the N-terminal SH3 domain of CD2AP in regulating T cell signaling.

Soil organic carbon (SOC) is a significant component of the global carbon (C) cycle. Changes in SOC storage affect atmospheric CO2 concentrations on decadal to centennial timescales. The C-TOOL model was developed to simulate farm- and regional-scale effects of management on medium- to long-term SOC storage in the profile of well-drained agricultural mineral soils. C-TOOL uses three SOC pools for both the topsoil (0–25 cm) and the subsoil (25–100 cm), and applies temperature-dependent first order kinetics to regulate C turnover. C-TOOL also enables the simulation of 14 C turnover. The simple model structure facilitates calibration and requires few inputs (mean monthly air temperature, soil clay content, soil C/N ratio and C in organic inputs). The model was parameterised using data from 19 treatments drawn from seven long-term field experiments in the United Kingdom, Sweden and Denmark. It was found that the initial SOC content had to be optimised for each experiment, but also that one set of values for other model parameters could be applied at all sites. With this set of parameters, C-TOOL can be applied more widely to evaluate effects of management options on SOC storage in temperate agricultural soils. C-TOOL simulates observed losses of SOC in soils under intensive agricultural use and the gain in SOC derived from large inputs of animal manure and inclusion of perennial grassland. The model simulates changes in SOC for the entire profile, but lack of data on subsoil SOC storage hampers a proper model evaluation. Experimental verification of management effects on subsoil C storage, subsoil C inputs from roots, and vertical transport of C in the soil profile remains prioritised research areas. [ABSTRACT FROM AUTHOR]

Ruminant production systems are important producers of food, support rural communities and culture, and help to maintain a range of ecosystem services including the sequestering of carbon in grassland soils. However, these systems also contribute significantly to climate change through greenhouse gas (GHG) emissions, while intensification of production has driven biodiversity and nutrient loss, and soil degradation. Modeling can offer insights into the complexity underlying the relationships between climate change, management and policy choices, food production, and the maintenance of ecosystem services. This paper 1) provides an overview of how ruminant systems modeling supports the efforts of stakeholders and policymakers to predict, mitigate and adapt to climate change and 2) provides ideas for enhancing modeling to fulfil this role. Many grassland models can predict plant growth, yield and GHG emissions from mono-specific swards, but modeling multi-species swards, grassland quality and the impact of management changes requires further development. Current livestock models provide a good basis for predicting animal production; linking these with models of animal health and disease is a priority. Farm-scale modeling provides tools for policymakers to predict the emissions of GHG and other pollutants from livestock farms, and to support the management decisions of farmers from environmental and economic standpoints. Other models focus on how policy and associated management changes affect a range of economic and environmental variables at regional, national and European scales. Models at larger scales generally utilise more empirical approaches than those applied at animal, field and farm-scales and include assumptions which may not be valid under climate change conditions. It is therefore important to continue to develop more realistic representations of processes in regional and global models, using the understanding gained from finer-scale modeling. An iterative process of model development, in which lessons learnt from mechanistic models are applied to develop ‘smart’ empirical modeling, may overcome the trade-off between complexity and usability. Developing the modeling capacity to tackle the complex challenges related to climate change, is reliant on closer links between modelers and experimental researchers, and also requires knowledge-sharing and increasing technical compatibility across modeling disciplines. Stakeholder engagement throughout the process of model development and application is vital for the creation of relevant models, and important in reducing problems related to the interpretation of modeling outcomes. Enabling modeling to meet the demands of policymakers and other stakeholders under climate change will require collaboration within adequately-resourced, long-term inter-disciplinary research networks. [ABSTRACT FROM AUTHOR]

Livestock manure is a major contributor to ammonia and greenhouse gas emissions and treatment technologies such as slurry acidification can be used to reduce both. In this study, life cycle assessment was used to compare impact potentials of slurry acidification at either the pig housing or the field application stage with conventional slurry management. Furthermore, the effects of differences in environmental regulations concerning nitrogen application limits were analysed. The impact categories analysed were terrestrial eutrophication potential, climate change potential, marine eutrophication potential and toxicity potential. Slurry acidification reduced the terrestrial eutrophication potential by 71% for in-house acidification and by 30% for field acidification. Changes in regulatory plant-available nitrogen application limits resulted in changes in climate change potential and marine eutrophication potential, with lower limits favouring in-house acidification. Acidification can substantially reduce the environmental impacts of animal slurry, but the effect depends on the context of the regulatory regime. [ABSTRACT FROM AUTHOR]

• Spatial disaggregation of GNBs. • Animal manure as the main driver in agricultural pollution hotspots. • Identification of large temporal changes in GNB (±55–200 kg N ha−1), and high NUE values (>90%). • The accuracy of the estimates depend on the overlap between agri-environmental and socioeconomic conditions. • The municipality scale as the optimal resolution to identify pollution hotspots. Gross nitrogen balances (GNB) at the national level are a broad indicator of reactive nitrogen (N) pollution but the identification of pollution hotspots is necessary for designing cost-effective abatement policies. This requires a spatial disaggregation of GNBs to finer resolutions, but key inputs are often only available at high spatial scales. Modelling offers a method to provide these inputs but introduces additional error. Here we develop methods to estimate the main inputs (manure, synthetic fertiliser) and outputs (roughage feed and crop products) for mainland Portugal for the NUTS2, NUTS3 and municipality levels for the years 1989, 1999 and 2009. Our results show that despite the declining of the mainland GNBs over this period (47 to 38 kg N ha-1),the range of GNBs at progressively finer resolutions increased from 26 to 95 at the NUTS3 to −50–963 kg N ha−1 at the municipality levels. The increased concentration of livestock in some areas appears to be leading to an inefficient use of manure for crop production whereas there appears to be a depletion of soil N stocks in other areas. A comparison of our results with those from Denmark leads us to conclude that themunicipality level is the most suitable to identify hotspots, even thougherrors can arise when there is a poor correspondence between agrienvironmental conditions and the socioeconomic administrative boundaries at which statistical data are often available. [ABSTRACT FROM AUTHOR]