Water and energy footprint of irrigated agriculture in the Mediterranean region

Irrigated agriculture constitutes the largest consumer of freshwater in the Mediterranean region and provides a major source of income and employment for rural livelihoods. However, increasing droughts and water scarcity have highlighted concerns regarding the environmental sustainability of agriculture in the region. An integrated assessment combining a gridded water balance model with a geodatabase and GIS has been developed and used to assess the water demand and energy footprint of irrigated production in the region. Modelled outputs were linked with crop yield and water resources data to estimate water (m3 kg−1) and energy (CO2 kg−1) productivity and identify vulnerable areas or 'hotspots'. For a selected key crops in the region, irrigation accounts for 61 km3 yr−1 of water abstraction and 1.78 Gt CO2 emissions yr−1, with most emissions from sunflower (73 kg CO2/t) and cotton (60 kg CO2/t) production. Wheat is a major strategic crop in the region and was estimated to have a water productivity of 1000 t Mm−3 and emissions of 31 kg CO2/t. Irrigation modernization would save around 8 km3 of water but would correspondingly increase CO2 emissions by around +135%. Shifting from rain-fed to irrigated production would increase irrigation demand to 166 km3 yr−1 (+137%) whilst CO2 emissions would rise by +270%. The study has major policy implications for understanding the water–energy–food nexus in the region and the trade-offs between strategies to save water, reduce CO2 emissions and/or intensify food production

Climate change impacts on water for irrigated horticulture in the Vale of Evesham. Final Report

This project has undertaken a scoping review and assessment of the impacts of climate change on irrigated horticulture in the Vale of Evesham, an area of intense irrigated production located within the Environment Agency’s Warwickshire Avon CAMS Catchment. The research was based on a combination of methodologies including desk-based review of published and grey literature, computer agroclimatic and water balance modelling, GIS mapping, meetings with key informants and a stakeholder workshop. Future climate datasets were derived from the latest UK Climate Impacts Programme (UKICIP02) climatology, using selected emission scenarios for the 2020s, 2050s and 2080s. These scenarios were then used to model and map the future agroclimatic conditions under which agriculture might operate and the consequent impacts on irrigation need (depths of water applied) and volumetric demand. This was complimented by a postal survey to abstractors and a stakeholder workshop, to identify, review and assess farmer adaptation options and responses. The key findings arising from the research, implications for water resource management and recommendations for further work are summarised below. Using a geographical information system (GIS), a series of agroclimate maps have been produced, for the baseline and selected UKCIP02 scenario. The maps show major changes in agroclimate within the catchment over the next 50 years. The driest agroclimate zones are currently located around Worcester, Evesham, Tewkesbury and Gloucester, corresponding to areas where horticultural production and irrigation demand are most concentrated. By the 2020s, all agroclimate zones are predicted to increase in aridity. By the 2050s the entire catchment is predicted to have a drier agroclimate than is currently experienced anywhere in the driest parts of the catchment. This will have major impacts on the pattern of land use and irrigation water demand. Cont/d

Biodegradable polyesters reinforced with triclosan loaded polylactide micro/nanofibers: Properties, release and biocompatibility

Mechanical properties and drug release behavior were studied for three biodegradable polyester matrices (polycaprolactone, poly(nonamethylene azelate) and the copolymer derived from 1,9-nonanediol and an equimolar mixture of azelaic and pimelic acids) reinforced with polylactide (PLA) fibers. Electrospinning was used to produce suitable mats constituted by fibers of different diameters (i.e. from micro- to nanoscale) and a homogeneous dispersion of a representative hydrophobic drug (i.e. triclosan). Fabrics were prepared by a molding process, which allowed cold crystallization of PLA micro/nanofibers and hot crystallization of the polyester matrices. The orientation of PLA molecules during electrospinning favored the crystallization process, which was slightly enhanced when the diameter decreased. Incorporation of PLA micro/nanofibers led to a significant increase in the elastic modulus and tensile strength, and in general to a decrease in the strain at break. The brittle fracture was clearer when high molecular weight samples with high plastic deformation were employed. Large differences in the release behavior were detected depending on the loading process, fiber diameter size and hydrophobicity of the polyester matrix. The release of samples with the drug only loaded into the reinforcing fibers was initially fast and then became slow and sustained, resulting in longer lasting antimicrobial activity. Biocompatibility of all samples studied was demonstrated by adhesion and proliferation assays using HEp-2 cell cultures

Active galactic nuclei synapses: X-ray versus optical classifications using artificial neural networks

(Abridged) Many classes of active galactic nuclei (AGN) have been defined entirely throughout optical wavelengths while the X-ray spectra have been very useful to investigate their inner regions. However, optical and X-ray results show many discrepancies that have not been fully understood yet. The aim of this paper is to study the "synapses" between the X-ray and optical classifications. For the first time, the new EFLUXER task allowed us to analyse broad band X-ray spectra of emission line nuclei (ELN) without any prior spectral fitting using artificial neural networks (ANNs). Our sample comprises 162 XMM-Newton/pn spectra of 90 local ELN in the Palomar sample. It includes starbursts (SB), transition objects (T2), LINERs (L1.8 and L2), and Seyferts (S1, S1.8, and S2). The ANNs are 90% efficient at classifying the trained classes S1, S1.8, and SB. The S1 and S1.8 classes show a wide range of S1- and S1.8-like components. We suggest that this is related to a large degree of obscuration at X-rays. The S1, S1.8, S2, L1.8, L2/T2/SB-AGN (SB with indications of AGN), and SB classes have similar average X-ray spectra within each class, but these average spectra can be distinguished from class to class. The S2 (L1.8) class is linked to the S1.8 (S1) class with larger SB-like component than the S1.8 (S1) class. The L2, T2, and SB-AGN classes conform a class in the X-rays similar to the S2 class albeit with larger fractions of SB-like component. This SB-like component is the contribution of the star-formation in the host galaxy, which is large when the AGN is weak. An AGN-like component seems to be present in the vast majority of the ELN, attending to the non-negligible fraction of S1-like or S1.8-like component. This trained ANN could be used to infer optical properties from X-ray spectra in surveys like eRosita.Comment: 15 pages, 7 figures, accepted for publication in A&A. Appendix B only in the full version of the paper here: https://dl.dropboxusercontent.com/u/3484086/AGNSynapsis_OGM_online.pd

TREX-DM: a low background Micromegas-based TPC for low mass WIMP detection

Dark Matter experiments are recently focusing their detection techniques in low-mass WIMPs, which requires the use of light elements and low energy threshold. In this context, we present the TREX-DM experiment, a low background Micromegas-based TPC for low-mass WIMP detection. Its main goal is the operation of an active detection mass $\sim$0.300 kg, with an energy threshold below 0.4 keVee and fully built with previously selected radiopure materials. This article describes the actual setup, the first results of the comissioning in Ar+2\%iC$_4$H$_{10}$ at 1.2 bar and the future updates for a possible physics run at the Canfranc Underground Laboratory in 2016. A first background model is also presented, based on Geant4 simulations and a muon/electron discrimination method. In a conservative scenario, TREX-DM could be sensitive to DAMA/LIBRA and other hints of positive WIMPs signals, with some space for improvement with a neutron/electron discrimination method or the use of other light gases.Comment: Proceedings of the 7th Symposium on Large TPCs for Low-Energy Rare Event Detectio