Analysis of the economic impact of large-scale deployment of biomass resources for energy and materials in the Netherlands : macro-economics biobased synthesis report

The Bio-based Raw Materials Platform (PGG), part of the Energy Transition in The Netherlands, commissioned the Agricultural Economics Research Institute (LEI) and the Copernicus Institute of Utrecht University to conduct research on the macro-economic impact of large scale deployment of biomass for energy and materials in the Netherlands. Two model approaches were applied based on a consistent set of scenario assumptions: a bottom-up study including technoeconomic projections of fossil and bio-based conversion technologies and a topdown study including macro-economic modelling of (global) trade of biomass and fossil resources. The results of the top-down and bottom-up modelling work are reported separately. The results of the synthesis of the modelling work are presented in this report

Recognizing Emotions in a Foreign Language

Expressions of basic emotions (joy, sadness, anger, fear, disgust) can be recognized pan-culturally from the face and it is assumed that these emotions can be recognized from a speaker's voice, regardless of an individual's culture or linguistic ability. Here, we compared how monolingual speakers of Argentine Spanish recognize basic emotions from pseudo-utterances ("nonsense speech") produced in their native language and in three foreign languages (English, German, Arabic). Results indicated that vocal expressions of basic emotions could be decoded in each language condition at accuracy levels exceeding chance, although Spanish listeners performed significantly better overall in their native language ("in-group advantage"). Our findings argue that the ability to understand vocally-expressed emotions in speech is partly independent of linguistic ability and involves universal principles, although this ability is also shaped by linguistic and cultural variables

Decadal changes of the Western Arabian sea ecosystem

Historical data from oceanographic expeditions and remotely sensed data on outgoing longwave radiation, temperature, wind speed and ocean color in the western Arabian Sea (1950–2010) were used to investigate decadal trends in the physical and biochemical properties of the upper 300 m. 72 % of the 29,043 vertical profiles retrieved originated from USA and UK expeditions. Increasing outgoing longwave radiation, surface air temperatures and sea surface temperature were identified on decadal timescales. These were well correlated with decreasing wind speeds associated with a reduced Siberian High atmospheric anomaly. Shoaling of the oxycline and nitracline was observed as well as acidification of the upper 300 m. These physical and chemical changes were accompanied by declining chlorophyll-a concentrations, vertical macrofaunal habitat compression, declining sardine landings and an increase of fish kill incidents along the Omani coast

Deep U band and R imaging of GOODS-South: Observations,data reduction and first results

We present deep imaging in the {\em U} band covering an area of 630 arcmin$^{2}$ centered on the southern field of the Great Observatories Origins Deep Survey (GOODS). The data were obtained with the VIMOS instrument at the ESO Very Large Telescope. The final images reach a magnitude limit $U_{lim} \approx 29.8$ (AB, 1$\sigma$, in a 1\arcsec radius aperture), and have good image quality, with full width at half maximum \approx 0.8\arcsec. They are significantly deeper than previous U--band images available for the GOODS fields, and better match the sensitivity of other multi--wavelength GOODS photometry. The deeper U--band data yield significantly improved photometric redshifts, especially in key redshift ranges such as $2<z<4$, and deeper color--selected galaxy samples, e.g., Lyman--break galaxies at $z\approx 3$. We also present the coaddition of archival ESO VIMOS R band data, with $R_{lim} \approx 29$ (AB, 1$\sigma$, 1\arcsec radius aperture), and image quality \approx 0.75 \arcsec. We discuss the strategies for the observations and data reduction, and present the first results from the analysis of the coadded images.Comment: Accepted for publication ApJS, 54 pages, 27 figures. Released data and full-quality paper version available at http://archive.eso.org/cms/eso-data/data-packages/goods-vimos-imaging-data-release-version-1.

Multi-scale Modelling of Adapting European Farming Systems

European farming systems are challenged by an increasing global population, income growth, dietary changes and last, but not least, by a changing climate threatening future harvests, especially through increased frequency and severity of extreme events such as drought and heat waves. Therefore, there is a clear need to sustainably intensify and effectively adapt agricultural systems to climate change. Yet, increase in food production and adaptation are just two of many claims on agriculture, which is also supposed to meet growing demands on feed, fibre and fuel and to play a key role in mitigating climate change. The multiple claims on ecosystem services expected from agri-ecological systems call for an integrated assessment and modelling (IAM) of agricultural systems to adequately evaluate the multiple dimensions of the potential impacts as well as promising adaptation and mitigation options. This includes agriculture's responses to global change in the context of other sustainability aspects. Biophysical and socioeconomic analyses need to be integrated across different disciplines and spatiotemporal scales. In recent years the agricultural systems modelling community has made great efforts to use harmonized climate change, socio-economic and agricultural development scenarios and run them through a chain of models, e.g. by selected ensembles of biophysical and economic models at multiple scales, from farm to global. In phase 2 (2015-17) the European MACSUR knowledge hub has put its main focus on the regional (sub-national) level in the EU, with due consideration of the whole farm context. The aim of this paper is to compare three regional cases from the pool of MACSUR case studies across Europe, i.e. North Savo region in Finland, the Mostviertel region in Austria and the Oristanese region in Sardinia (Italy) representing different European farming systems along a north-south climatic gradient in Europe. These case studies represent a sample of some prominent farming systems, though only a fraction of a much larger diversity of farming and environmental conditions prevailing in Europe. We describe how adaptation options are analysed within an integrated set of linked models or model outputs combining information from different spatial scales, i.e. from region-specific crop, animal and farm level models to an analysis at regional and national level changes in agriculture and food production. First results show that adaptation to climate change affects agricultural production and farm income very differently. For some regions, e.g. in Finland there are both negative and positive effects while for the Sardinian case study adaptation to climate change have negative effects on farm income. Biophysical models, especially crop simulation models are first applied to analyse climate change impacts on yield, water use, biomass etc. and provide the outputs (i.e. delta changes) as input to economic models that contain the regional specificities of the case studies. Likewise, biophysical models are applied to analyse effects of various adaptation and mitigation options to provide information on effects of management changes on reducing damage/loss or taking opportunities from climate (adaptation) or reducing greenhouse gas emissions (mitigation). The economic models analyse economic impacts, for example the viability of management changes at farm and regional scales. Farm and regional scale economic models, backed by more detailed data and regional expert knowledge, can supply better representations of developments in each of the regions than this could be done by larger-scale (e.g. EU-wide or global) models. Sector or national economy-wide models are less specific in technical changes in agriculture, productivity changes, or in its use of inputs, due to higher level of aggregation. Nevertheless the market level view offered by sector models put the farm level changes and adaptations in a wider global context. Agricultural markets are highly integrated globally and the analyses for the case study regions also require information on global and European market developments. For example, significant changes in food demand due to changes in tastes and preferences, including aspects of climate change mitigation, may imply major changes for regional production structures. In MACSUR, this information – although not fully implemented in the case studies yet – is provided by the economic agricultural sector model CAPRI. The main strength of CAPRI in this context is that it is a global model with European focus. As such CAPRI can capture global developments and translate them to the regional level in the EU. The coupled analysis using global, EU and national level models side by side with farm level models provides unique results and much more insights on future possibilities and challenges for farmers and the food chain, than separating and restricting the analyses to either low or high aggregation level analyses. Market and policy changes often dominate longer term climate change considerations in the decision making of food chain actors, even if unfavourable weather events have become more common in recent years. Socio-economic scenarios from global to national and regional levels are needed to put adaptation and mitigation strategies in a wider context. Models, especially those that are able to accommodate biophysical, economic and policy changes are needed to show the value added from adaptations to climate change. Benefits and costs of mitigation strategies may be highly dependent on market developments. The current integrated assessment and modelling approach of MACSUR focusses on adaptation scenarios. It will be extended for the analysis and impact of mitigation policies in a later phase

Multi-scale Modelling of Adapting European Farming Systems

European farming systems are challenged by an increasing global population, income growth, dietary changes and last, but not least, by a changing climate threatening future harvests, especially through increased frequency and severity of extreme events such as drought and heat waves. Therefore, there is a clear need to sustainably intensify and effectively adapt agricultural systems to climate change. Yet, increase in food production and adaptation are just two of many claims on agriculture, which is also supposed to meet growing demands on feed, fibre and fuel and to play a key role in mitigating climate change. The multiple claims on ecosystem services expected from agri-ecological systems call for an integrated assessment and modelling (IAM) of agricultural systems to adequately evaluate the multiple dimensions of the potential impacts as well as promising adaptation and mitigation options. This includes agriculture's responses to global change in the context of other sustainability aspects. Biophysical and socioeconomic analyses need to be integrated across different disciplines and spatiotemporal scales. In recent years the agricultural systems modelling community has made great efforts to use harmonized climate change, socio-economic and agricultural development scenarios and run them through a chain of models, e.g. by selected ensembles of biophysical and economic models at multiple scales, from farm to global. In phase 2 (2015-17) the European MACSUR knowledge hub has put its main focus on the regional (sub-national) level in the EU, with due consideration of the whole farm context. The aim of this paper is to compare three regional cases from the pool of MACSUR case studies across Europe, i.e. North Savo region in Finland, the Mostviertel region in Austria and the Oristanese region in Sardinia (Italy) representing different European farming systems along a north-south climatic gradient in Europe. These case studies represent a sample of some prominent farming systems, though only a fraction of a much larger diversity of farming and environmental conditions prevailing in Europe. We describe how adaptation options are analysed within an integrated set of linked models or model outputs combining information from different spatial scales, i.e. from region-specific crop, animal and farm level models to an analysis at regional and national level changes in agriculture and food production. First results show that adaptation to climate change affects agricultural production and farm income very differently. For some regions, e.g. in Finland there are both negative and positive effects while for the Sardinian case study adaptation to climate change have negative effects on farm income. Biophysical models, especially crop simulation models are first applied to analyse climate change impacts on yield, water use, biomass etc. and provide the outputs (i.e. delta changes) as input to economic models that contain the regional specificities of the case studies. Likewise, biophysical models are applied to analyse effects of various adaptation and mitigation options to provide information on effects of management changes on reducing damage/loss or taking opportunities from climate (adaptation) or reducing greenhouse gas emissions (mitigation). The economic models analyse economic impacts, for example the viability of management changes at farm and regional scales. Farm and regional scale economic models, backed by more detailed data and regional expert knowledge, can supply better representations of developments in each of the regions than this could be done by larger-scale (e.g. EU-wide or global) models. Sector or national economy-wide models are less specific in technical changes in agriculture, productivity changes, or in its use of inputs, due to higher level of aggregation. Nevertheless the market level view offered by sector models put the farm level changes and adaptations in a wider global context. Agricultural markets are highly integrated globally and the analyses for the case study regions also require information on global and European market developments. For example, significant changes in food demand due to changes in tastes and preferences, including aspects of climate change mitigation, may imply major changes for regional production structures. In MACSUR, this information – although not fully implemented in the case studies yet – is provided by the economic agricultural sector model CAPRI. The main strength of CAPRI in this context is that it is a global model with European focus. As such CAPRI can capture global developments and translate them to the regional level in the EU. The coupled analysis using global, EU and national level models side by side with farm level models provides unique results and much more insights on future possibilities and challenges for farmers and the food chain, than separating and restricting the analyses to either low or high aggregation level analyses. Market and policy changes often dominate longer term climate change considerations in the decision making of food chain actors, even if unfavourable weather events have become more common in recent years. Socio-economic scenarios from global to national and regional levels are needed to put adaptation and mitigation strategies in a wider context. Models, especially those that are able to accommodate biophysical, economic and policy changes are needed to show the value added from adaptations to climate change. Benefits and costs of mitigation strategies may be highly dependent on market developments. The current integrated assessment and modelling approach of MACSUR focusses on adaptation scenarios. It will be extended for the analysis and impact of mitigation policies in a later phase

The HELLAS2XMM survey: IV. Optical identifications and the evolution of the accretion luminosity in the Universe

We present results from the photometric and spectroscopic identification of 122 X-ray sources recently discovered by XMM-Newton in the 2-10 keV band (the HELLAS2XMM 1dF sample). Their flux cover the range 8E-15-4E-13 cgs and the total area surveyed is 0.9 deg2. About 20% of the hard X-ray selected sources have an X-ray to optical flux ratio (X/O) ten times or more higher than that of optically selected AGN. Unlike the faint sources found in the ultra-deep Chandra and XMM-Newton surveys, which reach X-ray (and optical) fluxes more than one order of magnitude lower than the HELLAS2XMM survey sources, many of the extreme X/O sources in our sample have R<=25 and are therefore accessible to optical spectroscopy. We report the identification of 13 sources with X/O>10: 8 are narrow line QSO (i.e. QSO2), four are broad line QSO. We use a combined sample of 317 hard X-ray selected sources (HELLAS2XMM 1dF, CDFN 1Msec, SSA13 and Lockman Hole flux limited samples), 221 with measured z, to evaluate the cosmological evolution of the hard X-ray source's number and luminosity densities. Looking backward in time, the low luminosity sources (logL(2-10keV) = 43-44 erg/s) increase in number at a rate different than the high luminosity sources (logL(2-10keV)>44.5 erg/s), reaching a maximum around z=1 and then levelling off beyond z=2. This translates into an accretion driven luminosity density which is dominated by sources with logL(2-10keV) < 44.5 erg/s up to at least z=1, while the contribution of the same sources and of those with logL(2-10keV)>44.5 erg/s appear to be comparable between z=2 and 4.Comment: v2, minor changes, A&A in pres

Cross validation of bi-modal health-related stress assessment

This study explores the feasibility of objective and ubiquitous stress assessment. 25 post-traumatic stress disorder patients participated in a controlled storytelling (ST) study and an ecologically valid reliving (RL) study. The two studies were meant to represent an early and a late therapy session, and each consisted of a "happy" and a "stress triggering" part. Two instruments were chosen to assess the stress level of the patients at various point in time during therapy: (i) speech, used as an objective and ubiquitous stress indicator and (ii) the subjective unit of distress (SUD), a clinically validated Likert scale. In total, 13 statistical parameters were derived from each of five speech features: amplitude, zero-crossings, power, high-frequency power, and pitch. To model the emotional state of the patients, 28 parameters were selected from this set by means of a linear regression model and, subsequently, compressed into 11 principal components. The SUD and speech model were cross-validated, using 3 machine learning algorithms. Between 90% (2 SUD levels) and 39% (10 SUD levels) correct classification was achieved. The two sessions could be discriminated in 89% (for ST) and 77% (for RL) of the cases. This report fills a gap between laboratory and clinical studies, and its results emphasize the usefulness of Computer Aided Diagnostics (CAD) for mental health care

Role of iron, light, and silicate in controlling algal biomass in subantarctic waters SE of New Zealand

Phytoplankton processes in subantarctic (SA) waters southeast of New Zealand were studied during austral autumn and spring 1997. Chlorophyll a (0.2–0.3 μg L−1) and primary production (350–650 mg C m−2 d−1) were dominated by cells 1 nmol kg−1, there was little evidence of Fe-stressed algal populations, and Fυ/Fm approached 0.60 at the STC. In addition to these trends, waters of SA origin were occasionally observed within the STC and north of the STC, and thus survey data were interpreted with caution. In vitro Fe enrichment incubations in SA waters resulted in a switch from flavodoxin expression to that of ferredoxin, indicating the alleviation of Fe stress. In another 6-day experiment, iron-mediated increases in chlorophyll a (in particular, increases in large diatoms) were of similar magnitude to those observed in a concurrent Si/Fe enrichment; ambient silicate levels were 4 μM. A concurrent in vitro Fe enrichment, at irradiance levels comparable to the calculated mean levels experienced by cells in situ, resulted in relatively small increases (approximately twofold) in chlorophyll a. Thus, in spring, irradiance and Fe may both control diatom growth. In contrast, during summer, as mean irradiance increases and silicate levels decrease, Fe limitation, Fe/Si colimitation, or silicate limitation may determine diatom growth