Open Access

The majority of Niger ’s population faces a widespread lack of access to electricity. Althoughthe country lies in the Sahara belt, exploitation of solar energy is so far minimal. Due to ongoing fossilfuel exploration in the country, this fuel might dominate the future electricity supply. Today, Nigerimports the most of its electricity from Nigeria. There is a need to expand electricity generation andsupply infrastructures in Niger. When doing so, it is important to choose a proper set of electricitygeneration resource/technology that fulfils sustainability criteria. Thus, the objective of this work isto analyze a methodology in order to assess different energy technologies for Niger. A multi-criteriadecision approach was selected to assess the most accessible energy system for the country. Forthis purpose, indicators were developed and weighted for ranking electricity generation options.Altogether 40 indicators are selected under six dimensions (availability, risk, technology, economics,environment and social) to assess eight different alternatives, considering the aggregated results andcorresponding scores under each dimension. A merit list of technology and resources for electricitygeneration presented in this work could support the stakeholders in their decision-making for furtherprojects implementation in the country.

Chagas disease is a parasitic infection endemic to America, caused by the protozoan Trypanosoma cruzi and mainly transmitted to humans by contact with insect species of the Triatominae subfamily (Hemiptera). The disease is known to affect disproportionally rural impoverished human communities where it is known to cause premature death and is considered a social and economic burden. The Mexican government has made important progress into the detection, surveillance, treatment, and prevention of the disease in the last decades, however, Chagas disease has also been reported in areas where it had not been previously reported, and there are still barriers for access to treatment. In the state of San Luis Potosi, the disease is more studied in the east, nevertheless, it has been estimated that the reported cases of the entire state have been underestimated. New approaches to detect Chagas risk areas could help prioritize locations for Chagas disease education and prevention programs, detect cases of the disease in a timely manner, and provide access to the necessary treatments. The objective of this study was to identify risk areas for the transmission of Chagas disease in San Luis Potosí using species distribution modelling to estimate vectors and reservoirs’ distributions. To do this, firstly, important vectors and one reservoir species of T. cruzi were identified by reviewing their reported infection rates in literature and the number of times reported in Mexico. Next, species distribution models were calculated for the chosen vector and reservoir species present in the state. The models were done using the Maxent algorithm. Lastly, the resulting distribution models were combined into a risk map by thresholding the model outputs to produce binary predictions and then performing an overlap spatial analysis. Vector species were found to have suitable areas in 36.08% of the state’s territory while areas suitable for both vectors and reservoir were 7.4% of the state’s total area. While this figure may look small at first glance, the analysis suggests that 30% of the rural population and 52% of the urban population of the state are living in an area suitable for vectors and reservoir and therefore at risk. Species distribution modelling can be a powerful tool for identifying human populations at risk of contracting Chagas disease. In the future, including different species of reservoirs into the analysis could help to discover new risk areas in the state.

Urbanization processes are one of the main factors for habitat loss and fragmentation, driving global biodiversity loss and species extinction. The neotropical Atlantic forest in Brazil is considered a global key biodiversity hotspot and used to be one of the most extensive forests of the Americas. Due to substantial deforestation over centuries, its landscape was transformed into a mosaic of small forest fragments surrounded by a predominantly agricultural matrix. Urban expansion and rural urbanization have created peri-urban zones, which still can harbor natural habitat remnants, contributing to biological diversity and thus providing essential ecosystem services to urban and rural areas. The maintenance of such ecosystem services requires an understanding of the ecological processes in the ecosystem. A prerequisite for such an in-depth insight is the quantification of the underlying ecosystem functions. The ecosystem function pest control, a trophic interaction between insectivorous birds and herbivorous arthropods, was quantified in an empirical study using artificial caterpillars as prey models. This technique allows the identification of predator groups and the assessment of their predation rates. A total of 888 plasticine caterpillars were distributed at eight sites in secondary forest fragments surrounding the university campus of the federal university of São Carlos (UFScar) in peri-urban Sorocaba, southeastern Brazil. In sixteen point counts, 72 insect-eating birds, belonging to 19 species, were identified as possible artificial caterpillar attackers. Local habitat variables were measured to describe the forest vegetation structure and the landscape context. The study aimed to assess which structural components of the forest fragments, together with the recorded bird community variables (abundance, richness, αdiversity), best explain the estimated predation rates by birds. The mean predation rate for birds was 8.25 ± 6.3 % for a reference period of eight days, representing the first quantification of the ecosystem function pest control for the study area. The three treatments of caterpillar placement heights (ground, stem: 0.5 -1.0 m, leaf: 1.5 - 2.0 m) were the best and only estimator to explain bird predation rates. The little dense understory and ground vegetation might have facilitated the accessibility of artificial caterpillars, especially for carnivorous arthropods and birds. The detected contrast in their foraging and predation patterns suggests that arthropods and birds complement each other in their function of pest control. Bird predation rates were found to be negatively related to the vegetation structure. Thus, more open habitats, with less understory and low tree density, but high canopy cover and including dead trees were correlated with the highest predation rates and also exhibited more specialized forest-dependent bird species. This study confirms the importance of the maintenance of forest fragments in peri-urban areas, even if they are small, to preserve forest-associated birds, to contribute to the biological diversity on a broader scale, and to prevent the loss of ecosystem functions and services, mitigating some of the adverse effects of urbanization. Further investigation of the effect among the three treatments of caterpillar placement on the predation rates is encouraged, including comparative studies among different habitat types. For future studies, it is recommended to model the avian community variables with the vegetation structure measures to predict habitat preferences of insectivorous birds. Therefore, the sampling of more units and on a bigger scale, including over a more extended period, is necessary to improve the robustness of the results, which could provide the basis for a monetary analysis of the ecosystem service pest control by birds.

This research analyzes the effects that eco-labels have on the demand for organic (Bio) and Fairtrade (FT) food products. The thesis also discusses the individual determinants and motivations behind those effects. The analysis builds on data obtained from a self-programmed and self-conducted survey, with a sample of 869 students from different universities of Cologne, Germany. The Bio/FT preference is measured experimentally by randomly assigning individuals to treatment and control groups. The experiment simulates life decisions using actual pictures and prices of four products: packed and processed spaghetti, fresh tomatoes, packed raw meat, and packed orange juice. The existence, size, and direction of statistically significant eco-label effects were obtained with two sample tests of proportions. The results prove that the FT label has a positive differential effect on consumer’s demand. The presence of the FT label makes the purchase of this juice 9.1% higher than other juices not labeled as FT. This finding confirms the hypothesis that eco-labels have a positive effect on sustainable consumption. A surprising finding is that the presence of the Bio label lowers the purchase of organic pasta and tomatoes 7.7% and 9.4% respectively. This finding is interesting because it suggests that Bio labels are not driving the demand for sustainable tomatoes or pasta for this population. Regional and cheaper alternatives are preferred by consumers in this cases. The motivations behind consumer choices of different options were thoroughly analyzed. Binomial logistic regressions and qualitative text analysis show that the variance in the intention to consume eco-labeled food is explained mainly by price concerns and attitudes about value for money, but also by the influence of life partners as shopping referents, and the perceived behavioral controls of time and ability to monitor compliance of label standards, thus trust them. The final remarks support the use of the Fairtrade eco-label as a market-based instrument to guide sustainable food consumption among young adults in this context, and propose changes that could make the Bio label more attractive for the targeted population. The thesis demonstrates which individual factors should be inevitably considered when implementing labeling to foster sustainable consumption. Hence, it is useful for evaluations of public and private certification schemes, and for companies that support sustainable food markets. Projects looking to understand and drive sustainable production and consumption decisions should consider this reading.

Proper satellite-based crop monitoring applications at the farm-level often require near-daily imagery at medium to high spatial resolution. The combination of data from different ongoing satellite missions Sentinel 2 (ESA) and Landsat 7/8 (NASA) provides this unprecedented opportunity at a global scale; however, this is rarely implemented because these procedures are data demanding and computationally intensive. This study developed a robust stream processing for the harmonization of Landsat 7, Landsat 8 and Sentinel 2 in the Google Earth Engine cloud platform, connecting the benefit of coherent data structure, built-in functions and computational power in the Google Cloud. The harmonized surface reflectance images were generated for two agricultural schemes in Bekaa (Lebanon) and Ninh Thuan (Vietnam) during 2018–2019. We evaluated the performance of several pre-processing steps needed for the harmonization including the image co-registration, Bidirectional Reflectance Distribution Functions correction, topographic correction, and band adjustment. We found that the misregistration between Landsat 8 and Sentinel 2 images varied from 10 m in Ninh Thuan (Vietnam) to 32 m in Bekaa (Lebanon), and posed a great impact on the quality of the final harmonized data set if not treated. Analysis of a pair of overlapped L8-S2 images over the Bekaa region showed that, after the harmonization, all band-to-band spatial correlations were greatly improved. Finally, we demonstrated an application of the dense harmonized data set for crop mapping and monitoring. An harmonic (Fourier) analysis was applied to fit the detected unimodal, bimodal and trimodal shapes in the temporal NDVI patterns during one crop year in Ninh Thuan province. The derived phase and amplitude values of the crop cycles were combined with max-NDVI as an R-G-B false composite image. The final image was able to highlight croplands in bright colors (high phase and amplitude), while the non-crop areas were shown with grey/dark (low phase and amplitude). The harmonized data sets (with 30 m spatial resolution) along with the Google Earth Engine scripts used are provided for public use.