Analysis of Land Suitability for Woodland Expansion in Scotland: update 2020

The Scottish Government has a statutory commitment to achieve ‘Net Zero’ greenhouse gas emissions (GHG) by 2045. One land use change that will help to meet this target is to increase woodland planting, and the Climate Change Plan includes commitments to incrementally increase the annual woodland creation target from 10,000 to 15,000 ha per year by 2024/25.In 2011 the Woodland Expansion Advisory Group (WEAG) provided detailed analysis of the land area that might be suitable for planting new woodlands. This report summarises the results of an initial re-analysis of the opportunities and constraints for woodland expansion in Scotland, using a GIS spatial analysi

Peatland restoration and potential emissions savings on agricultural land: an evidence assessment

Peatland restoration has a significant role in tackling the global climate emergency and helping Scotland meet its ambitious climate change targets. Globally, peatlands are the largest natural terrestrial carbon store, containing about 25% of global soil carbon. However, they have been damaged by overexploitation. The Scottish Government has committed to restoring 250,000 hectares of peatland in Scotland by 2030. About a quarter of Scotland’s area is covered in peat, storing over 3 billion tonnes of carbon. Peat also provides a range of other co-benefits. Changing some current uses of peatland, particularly for agriculture, may lead to significant savings in greenhouse gas (GHG) emissions and offer some of the highest per hectare emissions savings. This report assesses the current evidence for the potential for emissions savings from re-wetting peatland currently used for agriculture in Scotland and explores alternative uses that might provide an economic return

Monitoring soil health in Scotland by land use category – a scoping study

Monitoring of soil health in a changing climate is a priority issue for Scottish Government. In 2020, CXC published a baseline report that pulled together existing research on the vulnerability of Scottish soils to climate change. This scoping study takes the thirteen potential indicators that were previously identified and considers their strategic relevance to monitoring soil health in the context of existing land use Scotland. Ten pre-defined land use categories were considered. We have also considered how soil monitoring might be managed to inform our understanding of cross-cutting issues such as biodiversity and climate change

Potential carbon loss from Scottish peatlands under climate change

Open Access via Springer Compact Agreement. Acknowledgements: This work was possible thanks to a Studentship from the Macaulay Development Trust.Peer reviewedPublisher PD

ECOSSE: Estimating Carbon in Organic Soils - Sequestration and Emissions: Final Report

Background Climate change, caused by greenhouse gas ( GHG) emissions, is one of the most serious threats facing our planet, and is of concern at both UK and devolved administration levels. Accurate predictions for the effects of changes in climate and land use on GHG emissions are vital for informing land use policy. Models which are currently used to predict differences in soil carbon (C) and nitrogen (N) caused by these changes, have been derived from those based on mineral soils or deep peat. None of these models is entirely satisfactory for describing what happens to organic soils following land-use change. Reports of Scottish GHG emissions have revealed that approximately 15% of Scotland's total emissions come from land use changes on Scotland's high carbon soils; the figure is much lower for Wales. It is therefore important to reduce the major uncertainty in assessing the carbon store and flux from land use change on organic soils, especially those which are too shallow to be deep peats but still contain a large reserve of C. In order to predict the response of organic soils to external change we need to develop a model that reflects more accurately the conditions of these soils. The development of a model for organic soils will help to provide more accurate values of net change to soil C and N in response to changes in land use and climate and may be used to inform reporting to UKGHG inventories. Whilst a few models have been developed to describe deep peat formation and turnover, none have so far been developed suitable for examining the impacts of land-use and climate change on the types of organic soils often subject to land-use change in Scotland and Wales. Organic soils subject to land-use change are often (but not exclusively) characterised by a shallower organic horizon than deep peats (e.g. organo-mineral soils such as peaty podzols and peaty gleys). The main aim of the model developed in this project was to simulate the impacts of land-use and climate change in these types of soils. The model is, a) be driven by commonly available meteorological data and soil descriptions, b) able to simulate and predict C and N turnover in organic soils, c) able to predict the impacts of land-use change and climate change on C and N stores in organic soils in Scotland and Wales. In addition to developing the model, we have undertaken a number of other modelling exercises, literature searches, desk studies, data base exercises, and experimentation to answer a range of other questions associated with the responses of organic soils in Scotland and Wales to climate and land-use change. Aims of the ECOSSE project The aims of the study were: To develop a new model of C and N dynamics that reflects conditions in organic soils in Scotland and Wales and predicts their likely responses to external factors To identify the extent of soils that can be considered organic in Scotland and Wales and provide an estimate of the carbon contained within them To predict the contribution of CO 2, nitrous oxide and methane emissions from organic soils in Scotland and Wales, and provide advice on how changes in land use and climate will affect the C and N balance In order to fulfil these aims, the project was broken down into modules based on these objectives and the report uses that structure. The first aim is covered by module 2, the second aim by module 1, and the third aim by modules 3 to 8. Many of the modules are inter-linked. Objectives of the ECOSSE project The main objectives of the project were to: Describe the distribution of organic soils in Scotland and Wales and provide an estimate of the C contained in them Develop a model to simulate C and N cycling in organic soils and provide predictions as to how they will respond to land-use, management and climate change using elements of existing peat, mineral and forest soil models Provide predictive statements on the effects of land-use and climate change on organic soils and the relationships to GHG emissions, including CO 2, nitrous oxide and methane. Provide predictions on the effects of land use change and climate change on the release of Dissolved Organic Matter from organic soils Provide estimates of C loss from scenarios of accelerated erosion of organic soils Suggest best options for mitigating C and N loss from organic soils Provide guidelines on the likely effects of changing land-use from grazing or semi-natural vegetation to forestry on C and N in organic soils Use the land-use change data derived from the Countryside Surveys of Scotland and Wales to provide predictive estimates for changes to C and N balance in organic soils over time

The potential for modelling peatland habitat condition in Scotland using long-term MODIS data

Funding: All James Hutton Institute authors are supported by the Scottish Government’s Rural and Environment Research and Analysis Directorate under the current Strategic Research Programme (2016-2021). Sally Johnson, Patricia Bruneau and Louise Ross did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors for this project. The peat spatial extent model was created in part within a UK Government – Department for Business, Energy and Industrial Strategy-funded project (TRN860/07/2014, Scoping the use of the methodology set out in Chapters 2 and 3 of the ‘2013 Supplement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories: Wetlands in the UK GHG Inventory: Land Use, Land Use Change and Forestry (LULUCF)), with further updates created within the Strategic Research Programme (2016-2021) funding.Peer reviewedPostprin

Neural Network Analysis to Evaluate Ozone Damage to Vegetation Under Different Climatic Conditions

Tropospheric ozone (O-3) is probably the air pollutant most damaging to vegetation. Understanding how plants respond to O(3)pollution under different climate conditions is of central importance for predicting the interactions between climate change, ozone impact and vegetation. This work analyses the effect of O(3)fluxes on net ecosystem productivity (NEP), measured directly at the ecosystem level with the eddy covariance (EC) technique. The relationship was explored with artificial neural networks (ANNs), which were used to model NEP using environmental and phenological variables as inputs in addition to stomatal O(3)uptake in Spring and Summer, when O(3)pollution is expected to be highest. A sensitivity analysis allowed us to isolate the effect of O-3, visualize the shape of the O-3-NEP functional relationship and explore how climatic variables affect NEP response to O-3. This approach has been applied to eleven ecosystems covering a range of climatic areas. The analysis highlighted that O(3)effects over NEP are highly non-linear and site-specific. A significant but small NEP reduction was found during Spring in a Scottish shrubland (-0.67%), in two Italian forests (up to -1.37%) and during Summer in a Californian orange orchard (-1.25%). Although the overall seasonal effect of O(3)on NEP was not found to be negative for the other sites, with episodic O(3)detrimental effect still identified. These episodes were correlated with meteorological variables showing that O(3)damage depends on weather conditions. By identifying O(3)damage under field conditions and the environmental factors influencing to that damage, this work provides an insight into O(3)pollution, climate and weather conditions.Peer reviewe

Measuring the vulnerability of Scottish soils to a changing climate

The second Scottish Climate Change Adaptation Programme (SCCAP) identifies soil health as a priority research area to support sustainable soil management and ecosystem services. This follows concerns over a perceived lack of data or gaps in understanding that have been raised in both independent assessments of the first SCCAP by the Committee on Climate Change. The aim of this study is to summarise previous work on Scottish soils, explore existing datasets, and identify those metrics which could support the monitoring of Scotland’s soil health and measure the vulnerability of Scottish soils to climate change in future

A method for automatic segmentation and splitting of hyperspectral images of raspberry plants collected in field conditions

Abstract Hyperspectral imaging is a technology that can be used to monitor plant responses to stress. Hyperspectral images have a full spectrum for each pixel in the image, 400–2500 nm in this case, giving detailed information about the spectral reflectance of the plant. Although this technology has been used in laboratory-based controlled lighting conditions for early detection of plant disease, the transfer of such technology to imaging plants in field conditions presents a number of challenges. These include problems caused by varying light levels and difficulties of separating the target plant from its background. Here we present an automated method that has been developed to segment raspberry plants from the background using a selected spectral ratio combined with edge detection. Graph theory was used to minimise a cost function to detect the continuous boundary between uninteresting plants and the area of interest. The method includes automatic detection of a known reflectance tile which was kept constantly within the field of view for all image scans. A method to split images containing rows of multiple raspberry plants into individual plants was also developed. Validation was carried out by comparison of plant height and density measurements with manually scored values. A reasonable correlation was found between these manual scores and measurements taken from the images (r2 = 0.75 for plant height). These preliminary steps are an essential requirement before detailed spectral analysis of the plants can be achieved

Automated Soil Physical Parameter Assessment Using Smartphone and Digital Camera Imagery

Here we present work on using different types of soil profile imagery (topsoil profiles captured with a smartphone camera and full-profile images captured with a conventional digital camera) to estimate the structure, texture and drainage of the soil. The method is adapted from earlier work on developing smartphone apps for estimating topsoil organic matter content in Scotland and uses an existing visual soil structure assessment approach. Colour and image texture information was extracted from the imagery. This information was linked, using geolocation information derived from the smartphone GPS system or from field notes, with existing collections of topography, land cover, soil and climate data for Scotland. A neural network model was developed that was capable of estimating soil structure (on a five-point scale), soil texture (sand, silt, clay), bulk density, pH and drainage category using this information. The model is sufficiently accurate to provide estimates of these parameters from soils in the field. We discuss potential improvements to the approach and plans to integrate the model into a set of smartphone apps for estimating health and fertility indicators for Scottish soils