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Pour obtenir un aperçu spécifique des rôles que les micro-organismes pourraient jouer dans la stéatose hépatique non alcoolique (NAFLD), certaines bactéries intestinales et lactiques et une levure (Anaerostipes caccae, Bacteroides thetaiotaomicron, Bifidobacterium longum, Enterococcus fecalis, Escherichia coli, Lactobacillus acidophilus, Lactobacillus fermentum, Lactobacillus plantarum, Weissella confusa, Saccharomyces cerevisiae) ont été caractérisées par une chromatographie liquide haute performance pour la production d'éthanol lorsqu'elles sont cultivées sur différents glucides : hexoses (glucose et fructose), pentoses (arabinose et ribose), disaccharides (lactose et lactulose) et inuline. Les quantités les plus élevées d'éthanol ont été produites par S. cerevisiae, L. fermentum et W. confusa sur le glucose et par S. cerevisiae et W. confusa sur le fructose. En raison de la mannitol-déshydrogénase exprimée dans L. fermentum, la production d'éthanol sur le fructose a été significativement réduite (P < 0,05). Le pyruvate et le citrate, deux accepteurs d'électrons potentiels pour la régénération du NAD+/NADP+, ont considérablement réduit la production d'éthanol avec de l'acétate produit à la place dans L. fermentum cultivé sur glucose et W. confusa cultivé sur glucose et fructose, respectivement. Dans les boues fécales préparées à partir des matières fécales de quatre volontaires en surpoids, on a constaté que l'éthanol était produit lors de l'ajout de fructose. L'ajout d'A. caccae, L. acidophilus, L. fermentum, ainsi que de citrate et de pyruvate, respectivement, a aboli la production d'éthanol. Cependant, l'ajout de W. confusa a entraîné une augmentation significative (P < 0,05) de la production d'éthanol. Ces résultats indiquent que des micro-organismes comme W. confusa, une bactérie lactique hétéro-fermentaire, négative à la mannitol-déshydrogénase, peuvent favoriser la NAFLD par l'éthanol produit à partir de la fermentation du sucre, tandis que d'autres bactéries intestinales et des bactéries lactiques homo- et hétéro-fermentaires mais positives à la mannitol-déshydrogénase peuvent ne pas favoriser la NAFLD. En outre, nos études indiquent que les facteurs alimentaires interférant avec le microbiote gastro-intestinal et le métabolisme microbien peuvent être importants dans la prévention ou la promotion de la NAFLD. Para obtener información específica sobre los roles que podrían desempeñar los microorganismos en la enfermedad del hígado graso no alcohólico (NAFLD, por sus siglas en inglés), algunas bacterias intestinales y del ácido láctico y una levadura (Anaerostipes caccae, Bacteroides thetaiotaomicron, Bifidobacterium longum, Enterococcus fecalis, Escherichia coli, Lactobacillus acidophilus, Lactobacillus fermentum, Lactobacillus plantarum, Weissella confusa, Saccharomyces cerevisiae) se caracterizaron por cromatografía líquida de alto rendimiento para la producción de etanol cuando se cultivaron en diferentes carbohidratos: hexosas (glucosa y fructosa), pentosas (arabinosa y ribosa), disacáridos (lactosa y lactulosa) e inulina. Las cantidades más altas de etanol fueron producidas por S. cerevisiae, L. fermentum y W. confusa en glucosa y por S. cerevisiae y W. confusa en fructosa. Debido a la manitol-deshidrogenasa expresada en L. fermentum, la producción de etanol en fructosa se redujo significativamente (P < 0.05). El piruvato y el citrato, dos aceptores de electrones potenciales para la regeneración de NAD+/NADP+, redujeron drásticamente la producción de etanol con acetato producido en su lugar en L. fermentum cultivado en glucosa y W. confusa cultivado en glucosa y fructosa, respectivamente. En suspensiones fecales preparadas a partir de heces de cuatro voluntarios con sobrepeso, se encontró que el etanol se producía tras la adición de fructosa. La adición de A. caccae, L. acidophilus, L. fermentum, así como citrato y piruvato, respectivamente, abolió la producción de etanol. Sin embargo, la adición de W. confusa resultó en un aumento significativo (P < 0.05) de la producción de etanol. Estos resultados indican que microorganismos como W. confusa, una bacteria de ácido láctico hetero-fermentativa, negativa para manitol-deshidrogenasa, pueden promover NAFLD a través del etanol producido a partir de la fermentación de azúcar, mientras que otras bacterias intestinales y bacterias de ácido láctico homo- y hetero-fermentativas pero positivas para manitol-deshidrogenasa pueden no promover NAFLD. Además, nuestros estudios indican que los factores dietéticos que interfieren con la microbiota gastrointestinal y el metabolismo microbiano pueden ser importantes para prevenir o promover la EHGNA. To gain some specific insight into the roles microorganisms might play in non-alcoholic fatty liver disease (NAFLD), some intestinal and lactic acid bacteria and one yeast (Anaerostipes caccae, Bacteroides thetaiotaomicron, Bifidobacterium longum, Enterococcus fecalis, Escherichia coli, Lactobacillus acidophilus, Lactobacillus fermentum, Lactobacillus plantarum, Weissella confusa, Saccharomyces cerevisiae) were characterized by high performance liquid chromatography for production of ethanol when grown on different carbohydrates: hexoses (glucose and fructose), pentoses (arabinose and ribose), disaccharides (lactose and lactulose), and inulin. Highest amounts of ethanol were produced by S. cerevisiae, L. fermentum and W. confusa on glucose and by S. cerevisiae and W. confusa on fructose. Due to mannitol-dehydrogenase expressed in L. fermentum, ethanol production on fructose was significantly (P < 0.05) reduced. Pyruvate and citrate, two potential electron acceptors for regeneration of NAD+/NADP+, drastically reduced ethanol production with acetate produced instead in L. fermentum grown on glucose and W. confusa grown on glucose and fructose, respectively. In fecal slurries prepared from feces of four overweight volunteers, ethanol was found to be produced upon addition of fructose. Addition of A. caccae, L. acidophilus, L. fermentum, as well as citrate and pyruvate, respectively, abolished ethanol production. However, addition of W. confusa resulted in significantly (P < 0.05) increased production of ethanol. These results indicate that microorganisms like W. confusa, a hetero-fermentative, mannitol-dehydrogenase negative lactic acid bacterium, may promote NAFLD through ethanol produced from sugar fermentation, while other intestinal bacteria and homo- and hetero-fermentative but mannitol-dehydrogenase positive lactic acid bacteria may not promote NAFLD. Also, our studies indicate that dietary factors interfering with gastrointestinal microbiota and microbial metabolism may be important in preventing or promoting NAFLD. لاكتساب بعض الأفكار المحددة حول الأدوار التي قد تلعبها الكائنات الحية الدقيقة في مرض الكبد الدهني غير الكحولي (NAFLD)، تميزت بعض بكتيريا حمض الأمعاء واللاكتيك وخميرة واحدة (Anaerostipes caccae، Bacteroides thetaiotaomicron، Bifidobacterium longum، Enterococcus fecalis، Escherichia coli، Lactobacillus acidophilus، Lactobacillus fermentum، Lactobacillus plantarum، Weissella confusa، Saccharomyces cerevisiae) بتصوير سائل عالي الأداء لإنتاج الإيثانول عند زراعته على كربوهيدرات مختلفة: hexoses (الجلوكوز والفركتوز)، pentoses (الأرابينوز والريبوز)، disaccharides (اللاكتوز واللاكتولوز)، و inulin. تم إنتاج أعلى كميات من الإيثانول بواسطة S. cerevisiae و L. fermentum و W. confusa على الجلوكوز و S. cerevisiae و W. confusa على الفركتوز. بسبب نازعة هيدروجين المانيتول المعبر عنها في L. fermentum، انخفض إنتاج الإيثانول على الفركتوز بشكل كبير (P < 0.05). قلل البيروفات والسيترات، وهما مستقبلان محتملان للإلكترون لتجديد NAD +/NADP+، بشكل كبير من إنتاج الإيثانول مع الأسيتات المنتجة بدلاً من ذلك في L. fermentum المزروع على الجلوكوز و W. confusa المزروع على الجلوكوز والفركتوز، على التوالي. في الملاط البرازي الذي تم تحضيره من براز أربعة متطوعين يعانون من زيادة الوزن، وجد أن الإيثانول يتم إنتاجه عند إضافة الفركتوز. إضافة A. caccae، L. acidophilus، L. fermentum، وكذلك السترات والبيروفات، على التوالي، ألغت إنتاج الإيثانول. ومع ذلك، أدت إضافة W. confusa إلى زيادة كبيرة في إنتاج الإيثانول (P < 0.05). تشير هذه النتائج إلى أن الكائنات الحية الدقيقة مثل W. confusa، وهي بكتيريا حمض اللاكتيك السلبية غير المتجانسة، قد تعزز NAFLD من خلال الإيثانول المنتج من تخمير السكر، في حين أن البكتيريا المعوية الأخرى وبكتيريا حمض اللاكتيك الإيجابية غير المتجانسة ولكن غير المتجانسة قد لا تعزز NAFLD. أيضًا، تشير دراساتنا إلى أن العوامل الغذائية التي تتداخل مع الكائنات الحية الدقيقة في الجهاز الهضمي والتمثيل الغذائي الميكروبي قد تكون مهمة في منع أو تعزيز NAFLD.

AbstractObjective: The marked increase in the prevalence of obesity in the United States has recently been attributed to the increased fructose consumption. To determine if and how fructose might promote obesity in an animal model, we measured body composition, energy intake, energy expenditure, substrate oxidation, and several endocrine parameters related to energy homeostasis in mice consuming fructose.Research Methods and Procedures: We compared the effects of ad libitum access to fructose (15% solution in water), sucrose (10%, popular soft drink), and artificial sweetener (0% calories, popular diet soft drink) on adipogenesis and energy metabolism in mice.Results: Exposure to fructose water increased adiposity, whereas increased fat mass after consumption of soft drinks or diet soft drinks did not reach statistical significance (n = 9 each group). Total intake of energy was unaltered, because mice proportionally reduced their caloric intake from chow. There was a trend toward reduced energy expenditure and increased respiratory quotient, albeit not significant, in the fructose group. Furthermore, fructose produced a hepatic lipid accumulation with a characteristic pericentral pattern.Discussion: These data are compatible with the conclusion that a high intake of fructose selectively enhances adipogenesis, possibly through a shift of substrate use to lipogenesis.

Climate change affects the timing of phenological events, such as the start, end, and length of the growing season of vegetation. A better understanding of how the phenology responded to climatic determinants is important in order to better anticipate future climate-ecosystem interactions. We examined the changes of three phenological events for the Mongolian Plateau and their climatic determinants. To do so, we derived three phenological metrics from remotely sensed vegetation indices and associated these with climate data for the period of 1982 to 2011. The results suggested that the start of the growing season advanced by 0.10 days yr-1, the end was delayed by 0.11 days yr-1, and the length of the growing season expanded by 6.3 days during the period from 1982 to 2011. The delayed end and extended length of the growing season were observed consistently in grassland, forest, and shrubland, while the earlier start was only observed in grassland. Partial correlation analysis between the phenological events and the climate variables revealed that higher temperature was associated with an earlier start of the growing season, and both temperature and precipitation contributed to the later ending. Overall, our findings suggest that climate change will substantially alter the vegetation phenology in the grasslands of the Mongolian Plateau, and likely also in biomes with similar environmental conditions, such as other semi-arid steppe regions.

Household Surveys performed in four villages selected from Oromia, Amhara and Southern Nations, Nationalities, and Peoples’ Region (SNNPR) following from the ‘Ethiopian Rural Household Survey’ (ERHS) conducted in 2004.It contains detailed data on household consumption and expenditures, assets, income, agricultural activities, land allocation, demographic characteristics, and other variables. From September 2011 to January 2012 another survey of 221 households was conducted in three major regions of central and southern Ethiopia. At the time of this latest survey effort the most recent ERHS survey data available was from 2004. The selection of respondents, determination of sample size, and apportionment of the sample were based on a proportional sampling technique.In addition to addressing important questions from the ERHS survey data, the field survey was designed to generate detailed information on household biomass energy production and consumption practices; as well as farming activities; labour and land allocation; economic and demographic characteristics; and expenditures on food, non-food items, and energy. The 2011 survey effort collected detailed household biomass energy use data. The measurement of household biomass energy use was obtained in traditional units and later converted into kilograms. The conversion factors for each of the biomass were collected from the closest urban centre of each of the study areas. Information obtained on household biomass energy use was collected for a time period of one week before the survey was conducted. It was then aggregated into annual figures, although household biomass energy use may vary seasonally. Quality/Lineage: The data was collected by qualified enumerators who had participated in previous ERHS survey. In addition to myself I recruited assistant supervisor to check the accuracy and quality of data on daily basis and followup interview process closely. Before the survey commenced a pilot survey was conducted in each of the study areas to identify the different types of energy households are using and other critical variables of interest for the research. This information was used to revise and improve questionnaire. Moreover, a one day in-depth training was given to enumerators and assistant supervisor to enrich their deeper understanding of each the question in the survey and to further improve questionnaire from their earlier experiences in those villages. Purpose: Over 90% of Ethiopian rural population rely on biomass energy. However, biomass energy utilization is linked to household livelihood as in rural households produce and consume biomass energy simultaneously with other (on and off-farm)activities. With the rampant rate of deforestation that Ethiopia is facing it is important to investigate the effect of deforestation or fuelwood scarcity which is assumed affect household welfare through influence on wage and price. In light of this, the survey effort collected information on household use of biomass energy sources, expenditure and labour allocation choices and amount of labour time used for each activities.This helped me to investigate the effect of fuelwood scarcity on household welfare from three aspects: labour allocation decision, energy expenditure and fuel choice and biomass energy consumption behavior to better understand the related linkage of household production and utilization of biomass with livelihoods or food security. This dataset was first published on the institutional Repository "Zentrum für Entwicklungsforschung: ZEF Data Portal" with ID={c08e08aa-3055-4651-801b-0383610c1987}.

Natural potentials for future cropland expansion The potential for the expansion of cropland is restricted by the availability of land resources and given local natural conditions. As a result, area that is highly suitable for agriculture according to the prevailing local biophysical conditions but is not under cultivation today has a high natural potential for expansion. Policy regulations can further restrict the availability of land for expansion by designating protected areas, although they may be suitable for agriculture. Conversely, by applying e.g. irrigation practices, land can be brought under cultivation, although it may naturally not be suitable. Here, we investigate the potentials for agricultural expansion for near future climate scenario conditions to identify the suitability of non-cropland areas for expansion according to their local natural conditions. We determine the available energy, water and nutrient supply for agricultural suitability from climate, soil and topography data, by using a fuzzy logic approach according to Zabel et al. (2014). It considers the 16 globally most important staple and energy crops. These are: barley, cassava, groundnut, maize, millet, oil palm, potato, rapeseed, rice, rye, sorghum, soy, sugarcane, sunflower, summer wheat, winter wheat. The parameterization of the membership functions that describe each of the crops’ specific natural requirements is taken from Sys et al. (1993). The considered natural conditions are: climate (temperature, precipitation, solar radiation), soil properties (texture, proportion of coarse fragments and gypsum, base saturation, pH content, organic carbon content, salinity, sodicity), and topography (elevation, slope). As a result of the fuzzy logic approach, values in a range between 0 and 1 describe the suitability of a crop for each of the prevailing natural conditions at a certain location. The smallest suitability value over all parameters finally determines the suitability of a crop. The daily climate data is provided by simulation results from the global climate model ECHAM5 (Jungclaus et al. 2006) for near future (2011-2040) SRES A1B climate scenario conditions. Soil data is taken from the Harmonized World Soil Database (HWSD) (FAO et al. 2012), and topography data is applied from the Shuttle Radar Topography Mission (SRTM) (Farr et al. 2007). In order to gather a general crop suitability, which does not refer to one specific crop, the most suitable crop with the highest suitability value is chosen at each pixel. In addition the natural biophysical conditions, we consider today’s irrigated areas according to (Siebert et al. 2013). We assume that irrigated areas globally remain constant until 2040, since adequate data on the development of irrigated areas do not exist, although it is likely that freshwater availability for irrigation could be limited in some regions, while in other regions surplus water supply could be used to expand irrigation practices (Elliott et al. 2014). However, it is difficult to project where irrigation practices will evolve, since it is driven by economic investment costs that are required to establish irrigation infrastructure. In principle, all agriculturally suitable land that is not used as cropland today has the natural potential to be converted into cropland. We assume that only urban and built-up areas are not available for conversion, although more than 80% of global urban areas are agriculturally suitable (Avellan et al. 2012). However, it seems unlikely that urban areas will be cleared at the large scale due to high investment costs, growing cities and growing demand for settlements. Concepts of urban and vertical farming usually are discussed under the aspects of cultivating fresh vegetables and salads for urban population. They are not designed to extensively grow staple crops such as wheat or maize for feeding the world in the near future. Urban farming would require one third of the total global urban area to meet only the global vegetable consumption of urban dwellers (Martellozzo et al. 2015). Thus, urban agriculture cannot substantially contribute to global agricultural production of staple crops. Protected areas or dense forested areas are not excluded from the calculation, in order not to lose any information in the further combination with the biodiversity patterns (see chapter 2.3). We use data on current cropland distribution by Ramankutty et al. (2008) and urban and built-up area according to the ESA-CCI land use/cover dataset (ESA 2014). From this data, we calculate the ‘natural expansion potential index’ (Iexp) that expresses the natural potential for an area to be converted into cropland as follows: Iexp = S * Aav The index is determined by the quality of agricultural suitability (S) (values between 0 and 1) multiplied with the amount of available area (Aav) for conversion (in percentage of pixel area). The available area includes all suitable area that is not cultivated today, and not classified as urban or artificial area. The index ranges between 0 and 100 and indicates where the conditions for cropland expansion are more or less favorable, when taking only natural conditions into account, disregarding socio-economic factors, policies and regulations that drive or inhibit cropland expansion. The index is a helpful indicator for identifying areas where cropland expansion could take place in the near future. Further information Detailled information are available in the following publication: Delzeit, R., F. Zabel, C. Meyer and T. Václavík (2017). Addressing future trade-offs between biodiversity and cropland expansion to improve food security. Regional Environmental Change 17(5): 1429-1441. DOI: 10.1007/s10113-016-0927-1 Contact Please contact: Dr. Florian Zabel, f.zabel@lmu.de, Department für Geographie, LMU München (www.geografie.uni-muenchen.de) This research was carried out within the framework of the GLUES (Global Assessment of Land Use Dynamics, Greenhouse Gas Emissions and Ecosystem Services) Project, which has been supported by the German Ministry of Education and Research (BMBF) program on sustainable land management (grant number: 01LL0901E).

The yield gap for maize across the Ethiopia has been estimated using crop model LINTUL5 embedded into the modeling framework SIMPLACE (Scientific Impact Assessment and Modelling Platform for Advanced Crop and Ecosystem Management. The yield gap of a crop grown in a certain location and cropping system is defined as the difference between the yield and biomass under optimum management and the average yield achieved by farmers. Yield under optimum management is labeled as potential yield (Yp) under irrigated conditions or water-limited potential yield (Yw) under rain-fed conditions.Yp is location specific because of the climate, and not dependent on soil properties assuming that the required water and nutrients are non-limiting and can be added through management. Thus, in areas without major soil constraints, Yp is the most relevant benchmark for irrigated systems. Whereas, for rain-fed crops, Yw, equivalent to water-limited potential yield, is the most relevant benchmark. Both Yp and Yw are calculated for optimum planting dates, planting density and region-specific crop variety which is critical in determining the feasible growth duration, particularly in tropical climatic conditions where two or even three crops are produced each year on the same field. Purpose: To increase food production, identifying the regions with untapped production capacity is of prime importance and can be achieved by quantitative and spatially explicit estimates of Yield gaps, thus considering the spatial variation in environment and the production system. This dataset was first published on the institutional Repository "Zentrum für Entwicklungsforschung: ZEF Data Portal" with ID={c2bbd5ed-fd4c-4a3f-b0b1-113a5d4f3ddf}. The yield gaps plotted in the map were calculated as the average values of 7 years (the year 2004 -2010). The unit is Megagram per hectare (Mg ha-1) which is equivalent to tons ha-1. The climate data at the national scale was made available from the National Aeronautics and Space Administration (NASA), Goddard Institute of Space Studies(https://data.giss.nasa.gov/impacts/agmipcf/agmerra/), AgMERRA.The dataset is stored at 0.25°×0.25° horizontal resolution (~25km). Soil parameter values were extracted from the soil property maps of Africa at 1 km x 1 km resolution (http://www.isric.org/data/soil-property-maps-africa-1-km). Maize yields (Mg ha-1) and fertilizer application (Nitrogen and Phosphorus) rates over seven years (2004 - 2010) at administrative zone level have been collected from the Central Statistical Agency, Ethiopia.

1. Aphids are abundant in natural and managed vegetation, supporting a diverse community of organisms and causing damage to agricultural crops. Due to a changing climate, periods of drought are anticipated to increase, and the potential consequences of this for aphid-plant interactions are unclear. 2. Using a meta-analysis and synthesis approach, we aimed to advance understanding of how increased drought incidence will affect this ecologically and economically important insect group, and to characterise any potential underlying mechanisms. We used qualitative and quantitative synthesis techniques to determine whether drought stress has a negative, positive, or null effect on aphid fitness and examined these effects in relation to 1) aphid biology, 2) geographical region, 3) host plant biology. 3. Across all studies, aphid fitness is typically reduced under drought. Subgroup analysis detected no difference in relation to aphid biology, geographical region, or the aphid-plant combination, indicating the negative effect of drought on aphids is potentially universal. Furthermore, drought stress had a negative impact on plant vigour and increased plant concentrations of defensive chemicals, suggesting the observed response of aphids is associated with reduced plant vigour and increased chemical defence in drought-stressed plants. 4. We propose a conceptual model to predict drought effects on aphid fitness in relation to plant vigour and defence to stimulate further research. Please check the ReadMe for an explanation of the values included in the dataset. Please note that n/a values are included in the Global_Dataset tab for plant meta-analysis data (_Plant_Vigour, _Plant_Defence, and _Plant_Nutrition), these indicate studies that did not report these parameters. Data was collected and curated using standard systematic literature synthesis approaches. The effect size (Hedges' g) reported in the dataset was calculated from extracted means and standard deviations.

Detailed measurements on soil, plant and atmosphere are required for the development and validation of crop growth and agroecosystem models. These measurements should be available with a high temporal resolution. With the aim of creating a growth model for winter wheat, an experiment with winter wheat under integrated cultivation conditions was carried out at the intensive experimental field of the Müncheberg Research Centre for Soil Fertility, Germany, between 1979 and 1981, both with and without irrigation. Field chambers were used for daily measurements of the CO2 balance of the crop stand. The daily evaporation was measured with two different evaporation pans. The different biomass components of the winter wheat crop stand were measured in weekly intervals from April to harvest in July/August. The different biomass components were analysed in the laboratory concerning their carbon, nitrogen, phosphorus and potassium content. Based on this coherent data set, the growth model TRITSIM for winter wheat was developed at the Müncheberg Research Centre for Soil Fertility in the 1980s. TRITSIM was incorporated into the complex agroecosystem model AGROSIM-WHEAT of the Research Institute of Plant Protection Eberswalde, Germany, for the identification of optimal plant protection measures under practical field conditions. The data set presented here can also be the basis for the verification and validation of further winter wheat growth and/or agroecosystem models.

The aim of this study was to quantify direct and indirect relationships between soil microbial community properties (potential basal respiration, microbial biomass) and abiotic factors (soil, climate) in three major land-cover types. Location: Europe Time period: 2018 Major taxa studied: Microbial community (fungi and bacteria) We collected 881 soil samples from across Europe in the framework of the Land Use/Land Cover Area Frame Survey (LUCAS). We measured potential soil basal respiration at 20ºC and microbial biomass (substrate-induced respiration) using an O2-microcompensation apparatus. Climate and soil data were obtained from previous LUCAS surveys and online databases. Structural equation modeling (SEM) was used to quantify relationships between variables, and equations extracted from SEMs were used to create predictive maps. Fatty acid methyl esters were measured in a subset of samples to distinguish fungal from bacterial biomass. Soil microbial properties in croplands were more heavily affected by climate variables than those in forests. Potential soil basal respiration and microbial biomass were correlated in forests but decoupled in grasslands and croplands, where microbial biomass depended on soil carbon. Forests had a higher ratio of fungi to bacteria than grasslands or croplands. Soil microbial communities in grasslands and croplands are likely carbon-limited in comparison with those in forests, and forests have a higher dominance of fungi indicating differences in microbial community composition. Notably, the often already-degraded soils of croplands could be more vulnerable to climate change than more natural soils. The provided maps show potentially vulnerable areas that should be explicitly accounted for in coming management plans to protect soil carbon and slow the increasing vulnerability of European soils to climate change. [Methods] Soil samples were collected during the 2018 LUCAS soil sampling campaign. Soil chemical and physical properties were measured at the Joint Research Centre in Ispra, Italy (Orgiazzi et al., 2018). Soil microbial respiration and biomass, as well as water content and water holding capacity, were measured in the Eisenhauer lab of the German Centre for Integrative Biodiversity Research. Fungi/Bacteria was measured by fatty acid analysis by Felipe Bastida at CEBAS CSIC. Climate and geographical data were harvested from various databases, which are listed in Appendix 1 (data sources) of the associated paper. For more details on the soil sampling and physical and chemical properties, see: Orgiazzi, A., Ballabio, C., Panagos, P., Jones, A., & Fernández-Ugalde, O. (2018). LUCAS Soil, the largest expandable soil dataset for Europe: a review. European Journal of Soil Science, 69(1), 140-153. https://doi.org/10.1111/ejss.12499 For more details on the measurements of soil microbial respiration and biomass, fatty acids, and water holding capacity, see the supplementary methods of the associated paper (Appendix 2). [Usage Notes] Fatty acid analysis was performed for a subset of 267 samples. Water holding capacity and associated measurements of basal respiration was analyzed in a subset of 100 samples. The samples that were not in these subsets have NA values for the columns associated with these measurements. In order to protect the precise locations of the LUCAS sampling sites, latitude and longitude values could not be given. The approximate location of each sampling site is instead described by the NUTS3 region. If you wish to replicate the structural equation modeling described in the paper, for which latitude is required, please get in touch. A description of each column is available in the associated metadata file. Deutsche Forschungsgemeinschaft, Award: FZT 118-202548816. European Research Council, Award: 694368. European Commission. Directorate-General for the Environment. Direction Générale Opérationnelle Agriculture, Ressources Naturelles et Environnement du Service Public de Wallonie. Eurostat. Peer reviewed

We provide a dataset from a household survey in Mpanda region in Western Tanzania (N = 137) that was conducted in 2011. Household heads (or replacements) were interviewed. The topics addressed covered a broad range of socio-economic data and including, among others, household information (number of household members, age, sex, religion etc.), agricultural production (e.g. crops produced and livestock owned) including number and size of plots, income generation, energy access and owned assets.