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Recent research has demonstrated significant demand for foods from Europe’s mountain areas; the production of these foods delivers significant positive externalities, despite producers facing greater constraints than their lowland equivalents. Existing markets often fail to account for these factors due to a lack of clear definition of mountain products. This research investigated the current and potential future role of food labelling and certification to support mountain food supply chains and sustainable mountain development, using expert/stakeholder interviews, spatial analysis, and email survey. Results demonstrate that existing EU Geographical Indication schemes are important for marketing mountain foods; however, they are less suitable for small-scale producers. National schemes for certifying mountain products have limited effectiveness, although considerable scope for enhancement exists. Recent EU legislation defining mountain products represents a considerable opportunity; however, challenges and potential trade-offs remain regarding the development of criteria on the location of supply chain stages and environmental factors, certification and control methods, and definition of mountain areas. © 2015, Armand Colin. All rights reserved. Source


Fitton N.,University of Aberdeen | Datta A.,University of Aberdeen | Datta A.,Tata Energy Research Institute | Smith K.,ADAS | And 5 more authors.
Nutrient Cycling in Agroecosystems | Year: 2014

Biogeochemical models such as DailyDayCent (DDC) are increasingly used to help quantify the emissions of green-house gasses across different ecosystems and climates. For this use they require parameterisation to represent a heterogeneous region or are site specific and scaled upwards. This requires information on inputs such as climate, soil, land-use and land management. However, each input has an associated uncertainty, which propagates through the model to create an uncertainty in the modelled outputs. To have confidence in model projections, an assessment of how the uncertainty in inputs propagated through the model and its impact on modelled outputs is required. To achieve this, we used a pre-defined uncertainty range of key inputs; temperature, precipitation, clay content, bulk density and soil pH, and performed a sensitivity and uncertainty analysis, using Monte Carlo simulations. This allowed the effect of measurement uncertainty on the modelled annual N2O emissions and crop yields at the Grange field experimental site to be quantified. Overall the range of model estimates simulated was relatively high and while the model was sensitive to each input parameter, uncertainty was driven by the sensitivity to soil pH. This decreased as the N fertiliser application rate increased, as at lower N application rates the model becomes more sensitive to other drivers of N mineralisation such as soil and climate inputs. Therefore, while our results indicate that DDC can provide a good estimate of annual N2O emissions and crop yields under UK conditions, reducing the uncertainty in the input parameters will lead to more accurate simulations. © 2014 Springer Science+Business Media Dordrecht. Source


Fitton N.,University of Aberdeen | Datta A.,University of Aberdeen | Datta A.,The Energy And Resources Institute Darbari Seth Block | Smith K.,ADAS | And 5 more authors.
Nutrient Cycling in Agroecosystems | Year: 2014

Biogeochemical models such as Daily- DayCent (DDC) are increasingly used to help quantify the emissions of green-house gasses across different ecosystems and climates. For this use they require parameterisation to represent a heterogeneous region or are site specific and scaled upwards. This requires information on inputs such as climate, soil, land-use and land management. However, each input has an associated uncertainty, which propagates through the model to create an uncertainty in the modelled outputs. To have confidence in model projections, an assessment of how the uncertainty in inputs propagated through the model and its impact on modelled outputs is required. To achieve this, we used a pre-defined uncertainty range of key inputs; temperature, precipitation, clay content, bulk density and soil pH, and performed a sensitivity and uncertainty analysis, using Monte Carlo simulations. This allowed the effect of measurement uncertainty on the modelled annual N2O emissions and crop yields at the Grange field experimental site to be quantified. Overall the range of model estimates simulated was relatively high and while the model was sensitive to each input parameter, uncertainty was driven by the sensitivity to soil pH. This decreased as the N fertiliser application rate increased, as at lower N application rates the model becomes more sensitive to other drivers ofNmineralisation such as soil and climate inputs. Therefore, while our results indicate that DDC can provide a good estimate of annual N2O emissions and crop yields under UK conditions, reducing the uncertainty in the input parameters will lead to more accurate simulations. © Springer Science+Business Media Dordrecht 2014. Source


Fitton N.,University of Aberdeen | Datta A.,University of Aberdeen | Datta A.,Tata Energy Research Institute | Smith K.,ADAS | And 5 more authors.
Nutrient Cycling in Agroecosystems | Year: 2014

Biogeochemical models such as Daily- DayCent (DDC) are increasingly used to help quantify the emissions of green-house gasses across different ecosystems and climates. For this use they require parameterisation to represent a heterogeneous region or are site specific and scaled upwards. This requires information on inputs such as climate, soil, land-use and land management. However, each input has an associated uncertainty, which propagates through the model to create an uncertainty in the modelled outputs. To have confidence in model projections, an assessment of how the uncertainty in inputs propagated through the model and its impact on modelled outputs is required. To achieve this, we used a pre-defined uncertainty range of key inputs; temperature, precipitation, clay content, bulk density and soil pH, and performed a sensitivity and uncertainty analysis, using Monte Carlo simulations. This allowed the effect of measurement uncertainty on the modelled annual N2O emissions and crop yields at the Grange field experimental site to be quantified. Overall the range of model estimates simulated was relatively high and while the model was sensitive to each input parameter, uncertainty was driven by the sensitivity to soil pH. This decreased as the N fertiliser application rate increased, as at lower N application rates the model becomes more sensitive to other drivers ofNmineralisation such as soil and climate inputs. Therefore, while our results indicate that DDC can provide a good estimate of annual N2O emissions and crop yields under UK conditions, reducing the uncertainty in the input parameters will lead to more accurate simulations. © Springer Science+Business Media Dordrecht 2014. Source


Fitton N.,University of Aberdeen | Datta A.,University of Aberdeen | Datta A.,Tata Energy Research Institute | Hastings A.,University of Aberdeen | And 9 more authors.
Environmental Research Letters | Year: 2014

The United Kingdom currently reports nitrous oxide emissions from agriculture using the IPCC default Tier 1 methodology. However Tier 1 estimates have a large degree of uncertainty as they do not account for spatial variations in emissions. Therefore biogeochemical models such as DailyDayCent (DDC) are increasingly being used to provide a spatially disaggregated assessment of annual emissions. Prior to use, an assessment of the ability of the model to predict annual emissions should be undertaken, coupled with an analysis of how model inputs influence model outputs, and whether the modelled estimates are more robust that those derived from the Tier 1 methodology. The aims of the study were (a) to evaluate if the DailyDayCent model can accurately estimate annual N2O emissions across nine different experimental sites, (b) to examine its sensitivity to different soil and climate inputs across a number of experimental sites and (c) to examine the influence of uncertainty in the measured inputs on modelled N2O emissions. DailyDayCent performed well across the range of cropland and grassland sites, particularly for fertilized fields indicating that it is robust for UK conditions. The sensitivity of the model varied across the sites and also between fertilizer/manure treatments. Overall our results showed that there was a stronger correlation between the sensitivity of N2O emissions to changes in soil pH and clay content than the remaining input parameters used in this study. The lower the initial site values for soil pH and clay content, the more sensitive DDC was to changes from their initial value. When we compared modelled estimates with Tier 1 estimates for each site, we found that DailyDayCent provided a more accurate representation of the rate of annual emissions. © 2014 IOP Publishing Ltd. Source

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