39 research outputs found

    Inventory of ammonia emissions from UK agriculture 2009

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    The National Ammonia Reduction Strategy Evaluation System (NARSES) model (spreadsheet version) was used to estimate ammonia (NH3) emissions from UK agriculture for the year 2009. Year-specific livestock numbers and fertiliser N use were added for 2009 and revised for previous years. The estimate for 2009 was 231.8 kt NH3, representing a 2.3 kt increase from the previously submitted estimate for 2008. Backward and forward projections using the 2009 model structure gave estimates of 317, 245 and 244 kt NH3 for the years 1990, 2010 and 2020, respectively. This inventory reports emission from livestock agriculture and from nitrogen fertilisers applied to agricultural land. There are a number of other minor sources reported as ‘agriculture’ in the total UK emission inventory, including horses not kept on agricultural holdings, emissions from composting and domestic fertiliser use

    Simulating grazing beef and sheep systems

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    CONTEXT Ruminant livestock make an important contribution to global food security by converting feed that is unsuitable for human consumption into high value food protein, demand for which is currently increasing at an unprecedented rate because of increasing global population and income levels. Factors affecting production efficiency, product quality, and consumer acceptability, such as animal fertility, health and welfare, will ultimately define the sustainability of ruminant production systems. These more complex systems can be developed and analysed by using models that can predict system responses to environment and management. OBJECTIVE We present a framework that dynamically models, using a process-based and mechanistic approach, animal and grass growth, nutrient cycling and water redistribution in a soil profile taking into account the effects of animal genotype, climate, feed quality and quantity on livestock production, greenhouse gas emissions, water use and quality, and nutrient cycling in a grazing system. METHODS A component to estimate ruminant animal growth was developed and integrated with the existing components of the SPACSYS model. Intake of herbage and/or concentrates and partitioning of the energy and protein contained in consumed herbage and/or concentrates were simulated in the component. Simulated animal growth was validated using liveweight data from over 200 finishing beef cattle and 900 lambs collected from the North Wyke Farm Platform (NWFP) in southwest England, UK, between 2011 and 2018. Annual nitrous oxide (N2O), ammonia, methane and carbon dioxide emissions from individual fields were simulated based on previous validated parameters. RESULTS AND CONCLUSIONS A series of statistical indicators demonstrated that the model could simulate liveweight gain of beef cattle and lamb. Simulated nitrogen (N) cycling estimated N input of 190 to 260 kg ha−1, of which 37–61% was removed from the fields either as silage or animal intake, 15–26% was lost through surface runoff or lateral drainage and 1.14% was emitted to the atmosphere as N2O. About 13% of the manure N applied to the NWFP and excreta N deposited at grazing was lost via ammonia volatilisation. SIGNIFICANCE The extended model has the potential to investigate the responses of the system on and consequences of a range of agronomic management and grazing strategies. However, modelling of multi-species swards needs to be validated including the dynamics of individual species in the swards, preferential selection by grazing animals and the impact on animal growth and nutrient flows

    A methodology to link national and local information for spatial targeting of ammonia mitigation efforts

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    The effects of atmospheric nitrogen (N) deposition are evident in terrestrial ecosystems worldwide, with eutrophication and acidification leading to significant changes in species composition. Substantial reductions in N deposition from nitrogen oxides emissions have been achieved in recent decades. By contrast, ammonia (NH3) emissions from agriculture have not decreased substantially and are typically highly spatially variable, making efficient mitigation challenging. One solution is to target NH3 mitigation measures spatially in source landscapes to maximize the benefits for nature conservation. The paper develops an approach to link national scale data and detailed local data to help identify suitable measures for spatial targeting of local sources near designated Special Areas of Conservation (SACs). The methodology combines high-resolution national data on emissions, deposition and source attribution with local data on agricultural management and site conditions. Application of the methodology for the full set of 240 SACs in England found that agriculture contributes ∼45% of total N deposition. Activities associated with cattle farming represented 54% of agricultural NH3 emissions within 2 km of the SACs, making them a major contributor to local N deposition, followed by mineral fertilizer application (21%). Incorporation of local information on agricultural management practices at seven example SACs provided the means to correct outcomes compared with national-scale emission factors. The outcomes show how national scale datasets can provide information on N deposition threats at landscape to national scales, while local-scale information helps to understand the feasibility of mitigation measures, including the impact of detailed spatial targeting on N deposition rates to designated sites

    AROMA - Agri-Environment Reduction Options for Mitigating Ammonia: assessment of the effects of RDPE environmental land management schemes on air quality

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    Measures available under agri-environment and grant schemes were assessed for their ammonia mitigation potential

    Application of Bayesian statistics to estimate nitrous oxide emission factors of the nitrogen fertilisers in UK grasslands

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    Trapezoidal integration by linear interpolation of data points is by far the most commonly used method of cumulative flux calculations of nitrous oxide (N2O) in studies that use flux chambers; however, this method is incapable of providing accurate uncertainty estimates. A Bayesian approach was used to calculate N2O emission factors (EFs) and their associated uncertainties from flux chamber measurements made after the application of nitrogen fertilisers, in the form of ammonium nitrate (AN), urea (Ur) and urea treated with Agrotain® urease inhibitor (UI) at four grassland sites in the UK. The comparison between the cumulative fluxes estimated using the Bayesian and linear interpolation methods were broadly similar (R2=0.79); however, the Bayesian method was capable of providing realistic uncertainties when a limited number of data points is available. The study reports mean EF values (and 95% confidence intervals) of 0.60 ± 0.63, 0.29 ± 0.22 and 0.26 ± 0.17% of applied N emitted as N2O for the AN, Ur and UI treatments, respectively. There was no significant difference between N2O emissions from the Ur and UI treatments. In the case of the automatic chamber data collected at one site in this study, the data did not fit the log-normal model, implying that more complex models may be needed, particularly for measurement data with high temporal resolutioninfo:eu-repo/semantics/publishedVersio

    Identification of potential “Remedies” for Air Pollution (nitrogen) Impacts on Designated Sites (RAPIDS)

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    Atmospheric nitrogen (N) deposition is a significant threat to semi-natural habitats and species in the UK, resulting in on-going erosion of habitat quality and declines in many species of high conservation value. The project focused on impacts and remedies for designated conservation sites, especially Natura 2000 sites protected under the EU Habitats Directive. However, the approach and certainly the measures could be equally applied to other areas of high conservation value. Evidence was drawn together to develop a framework for identifying key N threats at individual sites as a basis to target mitigation options in the context of potential legislative, voluntary and financial instruments
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