Recent studies show that current trends in yield improvement will not be sufficient to meet projected global food demand in 2050, and suggest that a further expansion of agricultural area will be required. However, agriculture is the main driver of losses of biodiversity and a major contributor to climate change and pollution, and so further expansion is undesirable. The usual proposed alternativeintensification with increased resource use-also has negative effects. It is therefore imperative to find ways to achieve global food security without expanding crop or pastureland and without increasing greenhouse gas emissions. Some authors have emphasised a role for sustainable intensification in closing global 'yield gaps' between the currently realised and potentially achievable yields. However, in this paper we use a transparent, data-driven model, to show that even if yield gaps are closed, the projected demand will drive further agricultural expansion. There are, however, options for reduction on the demand side that are rarely considered. In the second part of this paper we quantify the potential for demand-side mitigation options, and show that improved diets and decreases in food waste are essential to deliver emissions reductions, and to provide global food security in 2050. Over 35% of the Earth's permanent ice-free land is used for food production and, both historically and at present, this has been the greatest driver of deforestation and biodiversity loss 1. Food demand has increased globally with the increase in global population and its affluence. Globally, the demand for food will undoubtedly increase in the medium-term future. The United Nations' Food and Agriculture Organisation (FAO) has projected that cropland and pasture-based food production will see a 60%

Recent research in fluvial geomorphology has emphasized the spatially distributed feedbacks amongst river channel topography, flow hydraulics and sediment transport. Although understanding of the behaviour of dynamic river channels has been increased markedly through detailed within-channel process studies, less attention has been given to the accurate monitoring and terrain modelling of river channel form using three-dimensional measurements. However, such information is useful in two distinct senses. Firstly, it is one of the necessary boundary conditions for a physically based, deterministic modelling approach in which three-dimensional topography and river discharge drive within-channel flow hydraulics and ultimately spatial patterns of erosion and deposition and therefore channel change. Secondly, research has shown that an alternative means of estimating the medium-term bedload transport rate can be based upon monitoring spatial patterns of erosion and deposition within the river channel. This paper presents a detailed assessment of the distributed monitoring and terrain modelling of river bed topography using a technique that combines rigorous analytical photogrammetry with rapid ground survey. The availability of increasingly sophisticated terrain modelling packages developed for civil engineering application allows the representation of topographic information as a landform surface. Intercomparison of landform surfaces allows visualization and quantification of spatial patterns of erosion and deposition. A detailed assessment is undertaken of the quality of the morphological information acquired. This allow some general comments to be made concerning the use of more traditional methods to monitor and represent small-scale river channel morphology.

Hodge, R.A., Brasington, J., Richards, K.S. (2009). Analysing laser-scanned digital terrain models of gravel bed surfaces: linking morphology to sediment transport processes and hydraulics. Sedimentology, 56(7), 2024-2043. Sponsorship: NERCThe grain-scale topography of a sediment surface is a key component of a fluvial system, affecting aspects including sediment transport, flow resistance and ecology. However, its effect is hard to quantify because of the need for grain-scale elevation data from in situ fluvial gravel surfaces which are difficult to collect. The sediment surface properties are, therefore, commonly estimated as a function of the sediment grain-size distribution; however, because of additional factors, such as grain packing and shape, there is not necessarily a unique relationship between the two. A new methodology has been developed that uses terrestrial laser scanning to collect grain-scale topographic data from in situ fluvial gravel surfaces, from which digital terrain models are created. This paper investigates methods of analysing such digital terrain models, and possible sedimentological interpretations that can be drawn from the analysis. Eleven digital terrain models from exposed gravel surfaces in two contrasting rivers (the River Feshie and Bury Green Brook) were analysed by calculating: the distribution of surface elevations, semivariograms, surface inclinations, surface slopes and aspects and grain orientation. The distribution of surface elevations and surface slope and aspect analysis were found to be most informative. In the River Feshie, grain-size was interpreted as being a dominant control on sediment surface structure and gravel imbrication was identified. In Bury Green Brook, the location of the digital terrain models within the riffle?pool sequence was the dominant control on surface structure and grain orientation. Such digital terrain models therefore provide a new approach to measuring and quantifying the topography of fluvial sediment surfaces.Peer reviewe

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The grain-scale morphology of fluvial sediments is an important control on the character and dynamics of river systems; however current understanding of its role is limited by the difficulties of robustly quantifying field surface morphology. Terrestrial Laser Scanning (TLS) offers a new methodology for the rapid acquisition of high-resolution and high-precision surface elevation data from in situ sediments. To date, most environmental and fluvial applications of TLS have focused on large-scale systems, capturing macroscale morphologies. Application of this new technology at scales necessary to characterize the complexity of grain-scale fluvial sediments therefore requires a robust assessment of the quality and sources of errors in close-range TLS data. This paper describes both laboratory and field experiments designed to evaluate close-range TLS for sedimentological applications and to develop protocols for data acquisition. In the former, controlled experiments comprising high-resolution scans of white, grey and black planes and a sphere were used to quantify the magnitude and source of three-dimensional (3D) point errors resulting from a combination of surface geometry, reflectivity effects and inherent instrument precision. Subsequently, a methodology for the collection and processing of grain-scale TLS data is described through an application to a coarse grained gravel system, the River Feshie (D 50 32 to 63 mm). This stepwise strategy incorporates averaging repeat scans and filtering scan artefact and non-surface points using local 3D search algorithms. The sensitivity of the results to the filter parameter values are assessed by careful internal validation of Digital Terrain Models (DTMs) created from the resulting point cloud data. The transferability of this methodology is assessed through application to a second river, Bury Green Brook, dominated by finer gravel (D 50 18 to 33 mm). The factor limiting the resolution of DTMs created from this second dataset was found to be the relative sizes of the laser footprint and smallest grains. Figure 3. Point density scan data from single scanner positions: (a) River Feshie patch from three locations, patches are 1·14 by 0·9 m, (b) Bury Green Brook patch from two locations, patches are 0·85 by 1 m, (c) combined Bury Green Brook patch data.Figure 4. Stages of processing a River Feshie patch: raw data, after RSEV filter, after cone filter, and after local high point filter. Top row (a, c, e, g) shows a surface linearly interpolated at 1 mm from the relevant point cloud, shaded by elevation. Middle row (b, d, f, h) shows point density, in points cm -2 . Bottom (i) shows orthogonal view of final surface. The outlined areas indicate sub-patches used in determining appropriate filter parameter values for the next processing stage.Figure 6. Distributions of E for sub-patches of a River Feshie patch after applications of different filters using the complete range of parameter values tested: (a) RSEV filter, (b) cone filter, (c) local high point filter. Boxes show inter-...