56 research outputs found

    Assessing the contribution of soil NOx emissions to European atmospheric pollution

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    Atmospheric NOx concentrations are declining steadily due to successful abatement strategies predominantly targeting combustion sources. On the European continent, total NOx emissions fell by 55% between 1990 and 2017, but only modest reductions were achieved from the agricultural sector; with 7.8% from 20 Eastern European countries and 19.1% from 22 Western European countries. Consequently, the share of agricultural NOx emissions for these 42 European countries have increased from 3.6% to 7.2%. These values are highly uncertain due to serious lack of studies from agricultural soils and manure management. The emission factor (EFNO 1.33%), currently used for calculating soil NOx emissions from European agricultural categories 'N applied to soils' and 'manure management' was evaluated here by including recently published data from temperate climate zones. The newly calculated EFNO (average 0.60%, 0.0625th%/0.5475th%, n = 65 studies) is not notably different from the current value, given the large uncertainties associated with the small pool of studies, and therefore continued use of EFNO (1.33%) is recommended until more data become available. An assessment of the contribution of agricultural and non-agricultural NOx sources found that of the 42 European countries, the 8 most populated countries achieved considerable reductions (1990–2017) from categories 'non-agricultural sources' (55%), 'N applied to soils' (43%) and 'manure management' (1.2%), compared to small reductions from the remaining 34 countries. Forests are also large sources of soil NOx. On average, emissions from Eastern European forests were 4 times larger than from 'N applied agricultural soil', whereas Western European NOx emissions from 'N applied agricultural soil' were two times larger than from forest soils. Given that non-agricultural sources of NOx continue to decline, soil related emissions from agriculture, forests and manure management become more important, and require rigorous investigation in order to improve atmospheric pollution forecasts

    Comparison of ammonia emissions related to nitrogen use efficiency of livestock production in Europe

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    The increasing global demand for food and the environmental effects of reactive nitrogen losses in the food production chain, increase the need for efficient use of nitrogen (N). Of N harvested in agricultural plant products, 80% is used to feed livestock. Because the largest atmospheric loss of reactive nitrogen from livestock production systems is ammonia (NH3), the focus of this paper is on N lost as NH3 during the production of animal protein. The focus of this paper is to understand the key factors explaining differences in Nitrogen Use Efficiency (NUE) of animal production among various European countries. Therefore we developed a conceptual framework to describe the NUE defined as the amount of animal-protein N per N in feed and NH3-N losses in the production of milk, beef, pork, chicken meat and eggs in The Netherlands, Switzerland, United Kingdom, Germany, Austria and Denmark. The framework describes how manure management and animal-related parameters (feed, metabolism) relate to NH3 emissions and NUE. The results showed that the animal product with the lowest NUE had the largest NH3 emissions and vice versa, which agrees with the reciprocal relationship between NUE and NH3 within the conceptual framework. Across animal products for the countries considered, about 20% of the N in feed is lost as NH3. The significant smallest proportion (12%) of NH3-N per unit of Nfeed is from chicken production. The proportions for other products are 17%, 19%, 20% and 22% for milk, pork, eggs and beef respectively. These differences were not significantly different due to the differences among countries. For all countries, NUE was lowest for beef and highest for chicken. The production of 1 kg N in beef required about 5 kg N in feed, of which 1 kg N was lost as NH3-N. For the production of 1 kg N in chicken meat, 2 kg N in feed was required and 0.2 kg was lost as NH3. The production of 1 kg N in milk required 4 kg N in feed with 0.6 kg NH3-N loss, the same as pork and eggs, but those needed 3 and 3.5 kg N in feed per kg N in product respectively. Except for beef, the differences among these European countries were mainly caused by differences in manure management practices and their emission factors, rather than by animal-related factors including feed and digestibility influencing the excreted amount of ammoniacal N (TAN). For beef, both aspects caused important differences. Based on the results, we encourage the expression of N losses as per N in feed or per N in product, in addition to per animal place, when comparing production efficiency and NUE. We consider that disaggregating emission factors into a diet/animal effect and a manure management effect would improve the basis for comparing national NH3 emission inventories

    To what extent is climate change adaptation a novel challenge for agricultural modellers?

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    Modelling is key to adapting agriculture to climate change (CC), facilitating evaluation of the impacts and efficacy of adaptation measures, and the design of optimal strategies. Although there are many challenges to modelling agricultural CC adaptation, it is unclear whether these are novel or, whether adaptation merely adds new motivations to old challenges. Here, qualitative analysis of modellers’ views revealed three categories of challenge: Content, Use, and Capacity. Triangulation of findings with reviews of agricultural modelling and Climate Change Risk Assessment was then used to highlight challenges specific to modelling adaptation. These were refined through literature review, focussing attention on how the progressive nature of CC affects the role and impact of modelling. Specific challenges identified were: Scope of adaptations modelled, Information on future adaptation, Collaboration to tackle novel challenges, Optimisation under progressive change with thresholds, and Responsibility given the sensitivity of future outcomes to initial choices under progressive change

    A dynamic model of herbivore-plant interactions on grasslands

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    A model of plant and large herbivore dynamics is presented in which individual animal intake is limited by either diet digestibility (DIG) or forage availability and in which grazing can result in sward heterogeneity. The model is used to illustrate the interaction between animal density, plant dynamics, sward heterogeneity and diet selection strategy and how this subsequently feeds back to affect animal intake and sward heterogeneity. Two contrasting diet selection strategies were simulated; selection for maximum instantaneous intake of mass (IR) and selection for DIG.\ud \ud For both diet selection strategies, individual animal intake initially increased with increasing animal density, as intake was limited by DIG and the greater frequency of defoliation maintained the sward in a more juvenile and digestible state. As animal density increased further, a point was reached at which intake became limited by forage availability and individual animal intake fell thereafter. The DIG strategy resulted in a higher daily intake than the IR strategy when grazing pressure was low. Here, intake was limited by digestibility and the DIG strategy led by repeated defoliation, to the maintenance of a small proportion of the sward in a state of high digestibility. The model was further used to show that selection for a strategy of long-term optimisation of sward structure through modulation of bite depth is unlikely. The model highlights the potential plasticity of the grazing process and the need to include plant dynamics in models of large herbivore-plant interactions

    Modelling the interactions between regional farming structure, nitrogen losses and environmental regulation

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    Changes in the structure of agriculture are known to affect emissions of environmental pollutants from agriculture. Such changes are often driven by structural changes in agricultural production, so structural changes are likely to have indirect effects on emissions. In a pilot study, we consider how linking two complementary simulation models might be used to explore these effects. The agent-based AgriPoliS model was used to simulate the structural dynamics of agricultural production. The results from AgriPoliS were passed via a number of intermediate models to the Farm-N model, which was used to estimate the nitrogen surplus and losses from each farm for each year. The modelling complex was exercised by simulating the effects of two plausible policy scenarios for each of 14 years. The initial sizes and types of farms were based on statistics from a region in Denmark and the farms were randomly distributed within this region. The reference scenario (REF) implemented the current area-based Common Agricultural Policy payments for Denmark. The 1 LU scenario applied the additional constraint that a minimum area of 1 ha land had to be available for the application of the manure produced by one livestock unit. Substantial changes in the structure of agricultural production were shown for both scenarios. The effect on the regional nitrogen surpluses was predicted to differ between scenarios and the contribution of the different farm types to change with time. Predicted ammonia emission changed with time and differed between the scenarios, whereas the Danish fertiliser and manure legislation meant that nitrate leaching remained fairly stable. The implementation of additional environmental legislation significantly changed the trajectory of structural adjustment processes. Results emphasize the complex interplay between structural changes, losses of nitrogen, and environmental regulation. It is concluded that the effects of structural change on environmental emissions can be usefully explored by linking agent-based models of farmers' investment decisions with other models describing nutrient losses from the farm and that such modelling can play a useful role in designing effective environmental policies for agriculture. However, the approach demands the availability or collection of many region-specific data and this could create a barrier to its use.Emissions, Farm structure, Nitrogen surplus, Nitrogen losses, Structural change, Agent-based modelling
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