Territoires

Early neoclassical analyses predicted that poor countries would grow faster than wealthy countries, because of technological advances and diminishing returns to capital in the latter. The reverse has occurred: poor countries are falling back rather than catching up. We suggest here that deficient institutions underlie this divergence. Employing various indicators of institutional quality, including the rule of law, the pervasiveness of corruption, and the risk of expropriation and contract repudiation, we show that the ability of poor countries to catch up is determined in large part by the institutional environment in which economic activity in these countries takes place. (JEL O00, O10)

Over 10 years have passed since the first paper on the tourism-led growth hypothesis (TLGH) was published in 2002. Since then, a wave of studies has appeared trying to understand the temporal relationship between tourism and economic growth. Hence, it is possible to provide an assessment in terms of econometric methods used and main empirical findings achieved so far. This paper presents an exhaustive review of approximately 100 peer-reviewed published papers on the TLGH. An overview on the economic theoretical framework behind the TLGH is also provided. Notably, the results present an increasing diversification in the econometric modelling used. With a few exceptions, the empirical findings suggest that overall international tourism drives economic growth.

For agronomic, environmental, and economic reasons, the need for spatialized information about agricultural practices is expected to rapidly increase. In this context, we reviewed the literature on remote sensing for mapping cropping practices. The reviewed studies were grouped into three categories of practices: crop succession (crop rotation and fallowing), cropping pattern (single tree crop planting pattern, sequential cropping, and intercropping/agroforestry), and cropping techniques (irrigation, soil tillage, harvest and post-harvest practices, crop varieties, and agro-ecological infrastructures). We observed that the majority of the studies were exploratory investigations, tested on a local scale with a high dependence on ground data, and used only one type of remote sensing sensor. Furthermore, to be correctly implemented, most of the methods relied heavily on local knowledge on the management practices, the environment, and the biological material. These limitations point to future research directions, such as the use of land stratification, multi-sensor data combination, and expert knowledge-driven methods. Finally, the new spatial technologies, and particularly the Sentinel constellation, are expected to improve the monitoring of cropping practices in the challenging context of food security and better management of agro-environmental issues.

BACKGROUND: Landscape attributes influence spatial variations in disease risk or incidence. We present a review of the key findings from eight case studies that we conducted in Europe and West Africa on the impact of land changes on emerging or re-emerging vector-borne diseases and/or zoonoses. The case studies concern West Nile virus transmission in Senegal, tick-borne encephalitis incidence in Latvia, sandfly abundance in the French Pyrenees, Rift Valley Fever in the Ferlo (Senegal), West Nile Fever and the risk of malaria re-emergence in the Camargue, and rodent-borne Puumala hantavirus and Lyme borreliosis in Belgium. RESULTS: We identified general principles governing landscape epidemiology in these diverse disease systems and geographic regions. We formulated ten propositions that are related to landscape attributes, spatial patterns and habitat connectivity, pathways of pathogen transmission between vectors and hosts, scale issues, land use and ownership, and human behaviour associated with transmission cycles. CONCLUSIONS: A static view of the "pathogenecity" of landscapes overlays maps of the spatial distribution of vectors and their habitats, animal hosts carrying specific pathogens and their habitat, and susceptible human hosts and their land use. A more dynamic view emphasizing the spatial and temporal interactions between these agents at multiple scales is more appropriate. We also highlight the complementarity of the modelling approaches used in our case studies. Integrated analyses at the landscape scale allows a better understanding of interactions between changes in ecosystems and climate, land use and human behaviour, and the ecology of vectors and animal hosts of infectious agents.

The convergence of new EO data flows, new methodological developments and cloud computing infrastructure calls for a paradigm shift in operational agriculture monitoring. The Copernicus Sentinel-2 mission providing a systematic 5-day revisit cycle and free data access opens a completely new avenue for near real-time crop specific monitoring at parcel level over large countries. This research investigated the feasibility to propose methods and to develop an open source system able to generate, at national scale, cloud-free composites, dynamic cropland masks, crop type maps and vegetation status indicators suitable for most cropping systems. The so-called Sen2-Agri system automatically ingests and processes Sentinel-2 and Landsat 8 time series in a seamless way to derive these four products, thanks to streamlined processes based on machine learning algorithms and quality controlled in situ data. It embeds a set of key principles proposed to address the new challenges arising from countrywide 10 m resolution agriculture monitoring. The full-scale demonstration of this system for three entire countries (Ukraine, Mali, South Africa) and five local sites distributed across the world was a major challenge met successfully despite the availability of only one Sentinel-2 satellite in orbit. In situ data were collected for calibration and validation in a timely manner allowing the production of the four Sen2-Agri products over all the demonstration sites. The independent validation of the monthly cropland masks provided for most sites overall accuracy values higher than 90%, and already higher than 80% as early as the mid-season. The crop type maps depicting the 5 main crops for the considered study sites were also successfully validated: overall accuracy values higher than 80% and F1 Scores of the different crop type classes were most often higher than 0.65. These respective results pave the way for countrywide crop specific monitoring system at parcel level bridging the gap between parcel visits and national scale assessment. These full-scale demonstration results clearly highlight the operational agriculture monitoring capacity of the Sen2-Agri system to exploit in near real-time the observation acquired by the Sentinel-2 mission over very large areas. Scaling this open source system on cloud computing infrastructure becomes instrumental to support market transparency while building national monitoring capacity as requested by the AMIS and GEOGLAM G-20 initiatives.

This Is the Place Bats, bees, seals, and many seabirds practice central-place foraging, leaving a central home site, such as a hive or a rookery, to forage in a specific territory. Such species also share the challenge of competing for local resources with individuals from separate colonies. Using satellite tags, Wakefield et al. (p. 68 , published online 6 June; see the Perspective by Weimerskirch ) followed over 180 northern gannets to determine potential drivers of foraging territory division. Boundaries among colonial territories arose as a result of competition with individuals from other territories. Individuals from the same colony appeared to share information about foraging sites, presumably contributing to the establishment and maintenance of specific, long-term colonial territories.

When individuals face an emotional upheaval, they naturally talk and think about it. If they are unable to talk with others but continue to think about the event, they are at greater risk for a variety of psychological and health problems. Drawing on survey data gathered from San Francisco residents after the Loma Prieta Earthquake and from Dallas residents during and after the Persian Gulf War, we found evidence to support a social stage model of coping. Immediately after an upheaval, individuals openly talk and think about the event for approximately two weeks. Following this emergency stage, individuals progress into an inhibition stage wherein they stop talking about the upheaval but continue thinking about it for approximately six weeks. Certain indicators of distress, such as hostility and dreaming, peak during the inhibition phase. After this time, people enter an adaptation phase wherein they neither talk nor think about the upheaval. Implications for theory and interventions for both broad‐scale collective upheavals as well as personal traumas are discussed.

The recent deployment of ESA's Sentinel operational satellites has established a new paradigm for remote sensing applications. In this context, Sentinel-1 radar images have made it possible to retrieve surface soil moisture with a high spatial and temporal resolution. This paper presents two methodologies for the retrieval of soil moisture from remotely-sensed SAR images, with a spatial resolution of 100 m. These algorithms are based on the interpretation of Sentinel-1 data recorded in the VV polarization, which is combined with Sentinel-2 optical data for the analysis of vegetation effects over a site in Urgell (Catalunya, Spain). The first algorithm has already been applied to observations in West Africa by Zribi et al., 2008, using low spatial resolution ERS scatterometer data, and is based on change detection approach. In the present study, this approach is applied to Sentinel-1 data and optimizes the inversion process by taking advantage of the high repeat frequency of the Sentinel observations. The second algorithm relies on a new method, based on the difference between backscattered Sentinel-1 radar signals observed on two consecutive days, expressed as a function of NDVI optical index. Both methods are applied to almost 1.5 years of satellite data (July 2015-November 2016), and are validated using field data acquired at a study site. This leads to an RMS error in volumetric moisture of approximately 0.087 m³/m³ and 0.059 m³/m³ for the first and second methods, respectively. No site calibrations are needed with these techniques, and they can be applied to any vegetation-covered area for which time series of SAR data have been recorded.

The worldwide antibiotic crisis has led to a renewed interest in phage therapy. Since time immemorial phages control bacterial populations on Earth. Potent lytic phages against bacterial pathogens can be isolated from the environment or selected from a collection in a matter of days. In addition, phages have the capacity to rapidly overcome bacterial resistances, which will inevitably emerge. To maximally exploit these advantage phages have over conventional drugs such as antibiotics, it is important that sustainable phage products are not submitted to the conventional long medicinal product development and licensing pathway. There is a need for an adapted framework, including realistic production and quality and safety requirements, that allows a timely supplying of phage therapy products for 'personalized therapy' or for public health or medical emergencies. This paper enumerates all phage therapy product related quality and safety risks known to the authors, as well as the tests that can be performed to minimize these risks, only to the extent needed to protect the patients and to allow and advance responsible phage therapy and research.

BACKGROUND: Rodents are recognized as hosts for at least 60 zoonotic diseases and may represent a serious threat for human health. In the context of global environmental changes and increasing mobility of humans and animals, contacts between pathogens and potential animal hosts and vectors are modified, amplifying the risk of disease emergence. An accurate identification of each rodent at a specific level is needed in order to understand their implications in the transmission of diseases. Among the Muridae, the Rattini tribe encompasses 167 species inhabiting South East Asia, a hotspot of both biodiversity and emerging and re-emerging diseases. The region faces growing economical development that affects habitats, biodiversity and health. Rat species have been demonstrated as significant hosts of pathogens but are still difficult to recognize at a specific level using morphological criteria. DNA-barcoding methods appear as accurate tools for rat species identification but their use is hampered by the need of reliable identification of reference specimens. In this study, we explore and highlight the limits of the current taxonomy of the Rattini tribe. RESULTS: We used the DNA sequence information itself as the primary information source to establish group membership and estimate putative species boundaries. We sequenced two mitochondrial and one nuclear genes from 122 rat samples to perform phylogenetic reconstructions. The method of Pons and colleagues (2006) that determines, with no prior expectations, the locations of ancestral nodes defining putative species was then applied to our dataset. To give an appropriate name to each cluster recognized as a putative species, we reviewed information from the literature and obtained sequences from a museum holotype specimen following the ancient DNA criteria. CONCLUSIONS: Using a recently developed methodology, this study succeeds in refining the taxonomy of one of the most difficult groups of mammals. Most of the species expected within the area were retrieved but new putative species limits were also indicated, in particular within Berylmys and Rattus genera, where future taxonomic studies should be directed. Our study lays the foundations to better investigate rodent-born diseases in South East Asia and illustrates the relevance of evolutionary studies for health and medical sciences.

Sentinel-2 images are expected to improve global crop monitoring even in challenging tropical small agricultural systems that are characterized by high intra- and inter-field spatial variability and where satellite observations are disturbed by the presence of clouds. To overcome these constraints, we analyzed and optimized the performance of a combined Random Forest (RF) classifier/object-based approach and applied it to multisource satellite data to produce land use maps of a smallholder agricultural zone in Madagascar at five different nomenclature levels. The RF classifier was first optimized by reducing the number of input variables. Experiments were then carried out to (i) test cropland masking prior to the classification of more detailed nomenclature levels, (ii) analyze the importance of each data source (a high spatial resolution (HSR) time series, a very high spatial resolution (VHSR) coverage and a digital elevation model (DEM)) and data type (spectral, textural or other), and (iii) quantify their contributions to classification accuracy levels. The results show that RF classifier optimization allowed for a reduction in the number of variables by 1.5- to 6-fold (depending on the classification level) and thus a reduction in the data processing time. Classification results were improved via the hierarchical approach at all classification levels, achieving an overall accuracy of 91.7% and 64.4% for the cropland and crop subclass levels, respectively. Spectral variables derived from an HSR time series were shown to be the most discriminating, with a better score for spectral indices over the reflectances. VHSR data were only found to be essential when implementing the segmentation of the area into objects and not for the spectral or textural features they can provide during classification.

1 The methods used to indicate the biological state of streams are often based on taxonomic composition, and the abundance of species or other taxa. This ‘taxonomic structure’ varies among ecoregions and cannot be applied to wider geographical areas. Therefore, we assessed the species traits of benthic macroinvertebrates from semi-natural reference sites as a potential benchmark for large-scale biomonitoring. Our purpose was to assess the stability of community structure, based on the representation of taxa and of traits, across large gradients of geology (sedimentary to granitic), altitude (65–1982 m), geographical coordinates (0° 48′ W to 7° 20′ E and 42° 52′ to 48° 44′ N), stream order (1–5) and slope (0.5–60‰). 2 We used invertebrate abundance data from the 62 most natural French stream sites available. These abundance data served to weight the occurrence of ‘biological’ traits, such as reproductive characteristics, mobility, resistance forms, food, feeding habits, respiration, and ‘ecological’ traits, such as preferences for temperature, trophic level, saprobity, biogeographic distribution, longitudinal zonation, substratum and current velocity. 3 Multivariate analyses of taxonomic composition demonstrated a clear site gradient from lowlands to uplands and from calcareous to granitic geology. In contrast, community structure based on both biological and ecological traits was stable across environmental gradients. 4 The frequency distribution of biological traits indicated that the stream benthos of the ‘reference sites’ had a mixture of categories which confirmed theoretical predictions for temporally stable and spatially variable habitats. A mixture of ecological trait categories also occurred at our reference sites. Thus, semi-natural benthic macroinvertebrate communities are functionally diverse. Moreover, we included an initial application of these traits to a case of slightly to moderately polluted sites to show that the impact of humans significantly changes this natural functional diversity. 5 Future studies should focus on the potential for various biological and ecological traits to discriminate different human impacts on the benthic macroinvertebrates of running waters, and on the integration of this functional application into a general ‘reference-condition’ approach.

Abstract Assessing the level of diversity in plant communities from field‐based data is difficult for a number of practical reasons: (1) establishing the number of sampling units to be investigated can be difficult; (2) the choice of sample design can impact on results; and (3) defining the population of concern can be challenging. Satellite remote sensing (SRS) is one of the most cost‐effective approaches to identify biodiversity hotspots and predict changes in species composition. This is because, in contrast to field‐based methods, it allows for complete spatial coverages of the Earth's surface under study over a short period of time. Furthermore, SRS provides repeated measures, thus making it possible to study temporal changes in biodiversity. Here, we provide a concise review of the potential of satellites to help track changes in plant species diversity, and provide, for the first time, an overview of the potential pitfalls associated with the misuse of satellite imagery to predict species diversity. Our work shows that, while the assessment of alpha‐diversity is relatively straightforward, calculation of beta‐diversity (variation in species composition between adjacent locations) is challenging, making it difficult to reliably estimate gamma‐diversity (total diversity at the landscape or regional level). We conclude that an increased collaboration between the remote sensing and biodiversity communities is needed in order to properly address future challenges and developments.

Fuel cells are electrochemical devices that convert the chemical energy of a gaseous fuel directly into electricity. They are widely regarded as potential future stationary and mobile power sources. The response of a fuel-cell system depends on the air and hydrogen feed, flow and pressure regulation, and heat and water management. In this paper, the study is concentrated on the air subsystem that feeds the fuel-cell cathode with oxygen. Proceeding from a fourth-order model representing the air subsystem of a proton exchange membrane (PEM) fuel cell, a reduced third-order model is presented. Simulations show that the relative error caused by this reduction does not exceed 5%. Experimental validation has been done on a 33-kW PEM fuel cell, for both fourth- and reduced third-order models with less than 5% relative error. Additionally, a higher order sliding-mode supertwisting algorithm, with a well-known heuristic modification using variable gains, has been designed and validated experimentally to control a permanent-magnet synchronous motor that drives a volumetric compressor (double screw) designed to feed the 33-kW fuel cell with air.

Despite a plethora of research on the academic misconduct carried out by U.S. high school and undergraduate university students, little research has been done on the academic misconduct of Canadian students. This paper addresses this shortcoming by presenting the results of a study conducted at 11 Canadian higher education institutions between January 2002 and March 2003. We maintain that academic misconduct does indeed occur in Canada – amongst high school, undergraduate and graduate students. Common self-reported behaviours were as follows: working on an assignment with others when asked for individual work, getting questions and answers from someone who has already taken a test, copying a few sentences of material without footnoting, fabricating or falsifying lab data, and receiving unauthorized help on an assignment. Possible factors associated with these behaviours include student maturity, perceptions of what constitutes academic misconduct, faculty assessment and invigilation practices, low perceived risk, ineffective and poorly understood policies and procedures, and a lack of education on academic misconduct. Canadian educational institutions are encouraged to address these issues, beginning with a recommitment to academic integrity.

The use of consumer digital cameras or webcams to characterize and monitor different features has become prevalent in various domains, especially in environmental applications. Despite some promising results, such digital camera systems generally suffer from signal aberrations due to the on-board image processing systems and thus offer limited quantitative data acquisition capability. The objective of this study was to test a series of radiometric corrections having the potential to reduce radiometric distortions linked to camera optics and environmental conditions, and to quantify the effects of these corrections on our ability to monitor crop variables. In 2007, we conducted a five-month experiment on sugarcane trial plots using original RGB and modified RGB (Red-Edge and NIR) cameras fitted onto a light aircraft. The camera settings were kept unchanged throughout the acquisition period and the images were recorded in JPEG and RAW formats. These images were corrected to eliminate the vignetting effect, and normalized between acquisition dates. Our results suggest that 1) the use of unprocessed image data did not improve the results of image analyses; 2) vignetting had a significant effect, especially for the modified camera, and 3) normalized vegetation indices calculated with vignetting-corrected images were sufficient to correct for scene illumination conditions. These results are discussed in the light of the experimental protocol and recommendations are made for the use of these versatile systems for quantitative remote sensing of terrestrial surfaces.

ABSTRACT: To better know the cup quality of Honduran coffee, a study was carried out on samples from 52 Arabica coffee plots spread throughout 6 producing regions in Honduras. The aim was to identify environmental and agronomic factors that affect coffee quality. Biochemical and physical characteristics of coffees after roasting were used to link quality descriptors to these variables by means of multiple factor analyses. Coffees of superior quality came from the Olancho and El Paraíso regions. High altitudes and annual rainfall of under 1500 mm were favorable factors for the sensory quality. An optimum roasting time must be sought for each type of coffee, hence for each terroir.

Abstract Transdisciplinarity is generally defined by the inclusion of non-academic stakeholders in the process of knowledge production. Transdisciplinarity is a promising notion, but its ability to efficiently address the world’s most pressing issues still requires improvement. Several typologies of transdisciplinarity have been proposed, generally with a theoretical versus practical dichotomy (Mode 1/Mode 2), and effort has focused on possible linkages between different types. However, in the last two decades, transdisciplinarity has significantly matured to the extent that the classical theoretical versus practical distinction appears clearly limited. In this paper, a reframing of the debate is proposed by considering transdisciplinarity as a new discipline and as a way of being . The conception of transdisciplinarity as a discipline can be related to the recent development of the broader discipline of “integration and implementation sciences” (i2S), to which “practical” Mode 2 transdisciplinarity is a major contributor. When transdisciplinarity is considered as a way of being , it is inseparable from personal life and extends far beyond the professional activities of a researcher. To illustrate this conception, the work and life of Edgar Morin can be used as an exemplary reference in conjunction with other streams of thought, such as integral theory. Transdisciplinarity as a discipline and transdisciplinarity as a way of being have complementarities in terms of researchers’ personal dispositions and space for expression in academia. The proposed distinction also raises the question of the status of consciousness in transdisciplinary projects, which may be a fruitful controversial topic for the transdisciplinary research community.

Sensitivity to weather conditions, and specially to clouds, is a severe limiting factor to the use of optical remote sensing for Earth monitoring applications. A possible alternative is to benefit from weather-insensitive synthetic aperture radar (SAR) images. In many real-world applications, critical decisions are made based on some informative optical or radar features related to items such as water, vegetation or soil. Under cloudy conditions, however, optical-based features are not available, and they are commonly reconstructed through linear interpolation between data available at temporally-close time instants. In this work, we propose to estimate missing optical features through data fusion and deep-learning. Several sources of information are taken into account—optical sequences, SAR sequences, digital elevation model—so as to exploit both temporal and cross-sensor dependencies. Based on these data and a tiny cloud-free fraction of the target image, a compact convolutional neural network (CNN) is trained to perform the desired estimation. To validate the proposed approach, we focus on the estimation of the normalized difference vegetation index (NDVI), using coupled Sentinel-1 and Sentinel-2 time-series acquired over an agricultural region of Burkina Faso from May–November 2016. Several fusion schemes are considered, causal and non-causal, single-sensor or joint-sensor, corresponding to different operating conditions. Experimental results are very promising, showing a significant gain over baseline methods according to all performance indicators.

This paper explains the evolution of research into tourism and climate change, illustrating how early work concentrated on the impact of climate change on tourism, whereas current work also explores the impact of tourism on climate change. It goes on to elaborate a research agenda on both topics. The need to open up tourism research to other fields and professional worlds – such as climatology, the International Panel on Climate Change and transport research – is stressed, as is the need to organise the research community on a global basis, with more collaborative and comparative research.