Modelling uncertainty for decision making on ammonia mitigation with trees in the landscape (MUDMAT).

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In the agricultural landscape there are competing needs of making the best economical use of the land for food production and the use of land to mitigate against Nitrogen pollution from agriculture. Agricultural practises accounts for over 80% of ammonia emissions within the UK with releases from livestock housing and manure management through storage and spreading. Deposition of nitrogen in the form of ammonia can cause eutrophication and acidification effects on semi-natural ecosystems, leading to species composition changes and reduced biodiversity. The Clean Air Strategy 2019 has given significant focus to the impact of ammonia emissions and the subsequent atmospheric nitrogen load on ecosystems together with the particulate form of ammonia affecting human health outcomes. Over 60% of the UK's semi-natural habitats exceed their environmental limit for nitrogen deposition. Mitigation measures have been proposed by Government to support farmers in providing reductions in ammonia emissions. One effective abatement strategy is to plant tree shelter-belts downwind of livestock housing and slurry stores to 'scavenge' ammonia. (10-25% Bealey et al. 2014). Trees are particularly effective scavengers of air pollutants due to their effect on turbulence. Because of their rougher surface and high surface area trees are particularly effective scavengers of air pollutants. Recently a decision support tool has been created to help land managers quickly predict the potential of tree planting to mitigate in ammonia pollution based on simple user input choices. This web-based decision tool can help resolve the competing interests of using agricultural land for food production and pollution mitigation, by allowing the land manager to assess tree planting strategies that maximise ammonia abatement for the minimum use of land. Therefore, the web tool has proved to be very popular with key food producers (e.g. egg industry), and agricultural decision makers notably pollution regulators, conservation bodies and also planners.However there is no quantification of the reliability of the predictions made from the web tool. This is a key omission and hampers its use in decision making. Since models are never perfect representations of the landscape, model predictions are only as good as the quantification of how certain those predictions are. Compounding this, in the web tool, the computationally expensive coupled turbulence-deposition model MODDAAS-THETIS is replaced with a very simple empirical model and there is at present no way of quantifying the reduced accuracy of predictions associated with this simplification.Here we propose to use statistical methods to create a faster but quantifiably traceable emulator of MODDAAS-THETIS which will replace the empirical model in the web tool and will also allow uncertainty of the predictions from MODDAAS-THETIS to be quantified through the emulator.Our key objectives are to:1) Provide a decision tool that will help land managers to make the most economically efficient use of the agricultural land whilst minimising ammonia pollution. 2) Make a step-change improvement in decision making concerning the mitigation of ammonia pollution in the landscape by bringing state-of-the-art statistical methods to a web-based decision analysis system by quantifying the uncertainty of the predictions made. 3) Significantly improve the model behind the web-based tool making it traceable statistically to the underlying process-based model (MODDAAS-THETIS) through emulation and increasing the tree belt planting options available to the decision makers for predicting the mitigation of ammonia in the tool.4) Establish a methodology for emulating the multivariate spatial and temporal output from pollution transport models in the landscape making it possible to quantify uncertainty in the predictions from these computationally-expensive models.