A comparision of GHG emissions from UK field crop production under selected arable systems with reference to disease control

Crop disease not only threatens global food security by reducing crop production at a time of growing demand, but also contributes to greenhouse gas (GHG) emissions by reducing efficiency of N fertiliser use and farm operations and by driving land use change. GHG emissions associated with adoption of reduced tillage, organic and integrated systems of field crop production across the UK and selected regions are compared with emissions from conventional arable farming to assess their potential for climate change mitigation. The reduced tillage system demonstrated a modest (<20%) reduction in emissions in all cases, although in practice it may not be suitable for all soils and it is likely to cause problems with control of diseases spread on crop debris. There were substantial increases in GHG emissions associated with the organic and integrated systems at national level, principally due to soil organic carbon losses from land use change. At a regional level the integrated system shows the potential to deliver significant emission reductions. These results indicate that the conventional crop production system, coupled to reduced tillage cultivation where appropriate, is generally the best for producing high yields to minimise greenhouse gas emissions and contribute to global food security, although there may be scope for use of the integrated system on a regional basis. The control of crop disease will continue to have an essential role in both maintaining productivity and decreasing GHG emissions.Peer reviewe

The Cosmological Constant

This is a review of the physics and cosmology of the cosmological constant. Focusing on recent developments, I present a pedagogical overview of cosmology in the presence of a cosmological constant, observational constraints on its magnitude, and the physics of a small (and potentially nonzero) vacuum energy.Comment: 50 pages. Submitted to Living Reviews in Relativity (http://www.livingreviews.org/), December 199

Multidimensional characterization of global food supply from 1961 to 2013

Food systems are increasingly globalized and interdependent, and diets around the world are changing. Characterization of national food supplies and how they have changed can inform food policies that ensure national food security, support access to healthy diets and enhance environmental sustainability. Here we analysed data for 171 countries on the availability of 18 food groups from the United Nations Food and Agriculture Organization to identify and track multidimensional food supply patterns from 1961 to 2013. Four predominant food-group combinations were identified that explained almost 90% of the cross-country variance in food supply: animal source and sugar, vegetable, starchy root and fruit, and seafood and oilcrops. South Korea, China and Taiwan experienced the largest changes in food supply over the past five decades, with animal source foods and sugar, vegetables and seafood and oilcrops all becoming more abundant components of the food supply. In contrast, in many Western countries the supply of animal source foods and sugar declined. Meanwhile, there was remarkably little change in the food supply in countries in the sub-Saharan Africa region. These changes led to a partial global convergence in the national supply of animal source foods and sugar, and a divergence in those of vegetables and of seafood and oilcrops. Our analysis generated a novel characterization of food supply that highlights the interdependence of multiple food types in national food systems. A better understanding of how these patterns have evolved and will continue to change is needed to support the delivery of healthy and sustainable food system policies

Mapping and linking supply- and demand-side measures in climate-smart agriculture. A review

Climate change and food security are two of humanity’s greatest challenges and are highly interlinked. On the one hand, climate change puts pressure on food security. On the other hand, farming significantly contributes to anthropogenic greenhouse gas emissions. This calls for climate-smart agriculture—agriculture that helps to mitigate and adapt to climate change. Climate-smart agriculture measures are diverse and include emission reductions, sink enhancements, and fossil fuel offsets for mitigation. Adaptation measures include technological advancements, adaptive farming practices, and financial management. Here, we review the potentials and trade-offs of climate-smart agricultural measures by producers and consumers. Our two main findings are as follows: (1) The benefits of measures are often site-dependent and differ according to agricultural practices (e.g., fertilizer use), environmental conditions (e.g., carbon sequestration potential), or the production and consumption of specific products (e.g., rice and meat). (2) Climate-smart agricultural measures on the supply side are likely to be insufficient or ineffective if not accompanied by changes in consumer behavior, as climate-smart agriculture will affect the supply of agricultural commodities and require changes on the demand side in response. Such linkages between demand and supply require simultaneous policy and market incentives. It, therefore, requires interdisciplinary cooperation to meet the twin challenge of climate change and food security. The link to consumer behavior is often neglected in research but regarded as an essential component of climate-smart agriculture. We argue for not solely focusing research and implementation on one-sided measures but designing good, site-specific combinations of both demand- and supply-side measures to use the potential of agriculture more effectively to mitigate and adapt to climate change

Brane effective actions, kappa-symmetry and applications

This is a review on brane effective actions, their symmetries and some of their applications. Its first part covers the Green–Schwarz formulation of single M- and D-brane effective actions focusing on kinematical aspects: the identification of their degrees of freedom, the importance of world volume diffeomorphisms and kappa symmetry to achieve manifest spacetime covariance and supersymmetry, and the explicit construction of such actions in arbitrary on-shell supergravity backgrounds. Its second part deals with applications. First, the use of kappa symmetry to determine supersymmetric world volume solitons. This includes their explicit construction in flat and curved backgrounds, their interpretation as Bogomol’nyi–Prasad–Sommerfield (BPS) states carrying (topological) charges in the supersymmetry algebra and the connection between supersymmetry and Hamiltonian BPS bounds. When available, I emphasise the use of these solitons as constituents in microscopic models of black holes. Second, the use of probe approximations to infer about the non-trivial dynamics of strongly-coupled gauge theories using the anti de Sitter/conformal field theory (AdS/CFT) correspondence. This includes expectation values of Wilson loop operators, spectrum information and the general use of D-brane probes to approximate the dynamics of systems with small number of degrees of freedom interacting with larger systems allowing a dual gravitational description. Its final part briefly discusses effective actions for N D-branes and M2-branes. This includes both Super-Yang-Mills theories, their higher-order corrections and partial results in covariantising these couplings to curved backgrounds, and the more recent supersymmetric Chern–Simons matter theories describing M2-branes using field theory, brane constructions and 3-algebra considerations

Evolving autonomous learning in cognitive networks

Abstract There are two common approaches for optimizing the performance of a machine: genetic algorithms and machine learning. A genetic algorithm is applied over many generations whereas machine learning works by applying feedback until the system meets a performance threshold. These methods have been previously combined, particularly in artificial neural networks using an external objective feedback mechanism. We adapt this approach to Markov Brains, which are evolvable networks of probabilistic and deterministic logic gates. Prior to this work MB could only adapt from one generation to the other, so we introduce feedback gates which augment their ability to learn during their lifetime. We show that Markov Brains can incorporate these feedback gates in such a way that they do not rely on an external objective feedback signal, but instead can generate internal feedback that is then used to learn. This results in a more biologically accurate model of the evolution of learning, which will enable us to study the interplay between evolution and learning and could be another step towards autonomously learning machines