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The data consists of annual measurements of standing aboveground plant biomass, annual aboveground net primary productivity and annual soil respiration between 1998 and 2012. Data were collected from seven European shrublands that were subject to the climate manipulations drought and warming. Sites were located in the United Kingdom (UK), the Netherlands (NL), Denmark ( two sites, DK-B and DK-M), Hungary (HU), Spain (SP) and Italy (IT). All field sites consisted of untreated control plots, plots where the plant canopy air is artificially warmed during night time hours, and plots where rainfall is excluded from the plots at least during the plants growing season. Standing aboveground plant biomass (grams biomass per square metre) was measured in two undisturbed areas within the plots using the pin-point method (UK, DK-M, DK-B), or along a transect (IT, SP, HU, NL). Aboveground net primary productivity was calculated from measurements of standing aboveground plant biomass estimates and litterfall measurements. Soil respiration was measured in pre-installed opaque soil collars bi-weekly, monthly, or in measurement campaigns (SP only). The datasets provided are the basis for the data analysis presented in Reinsch et al. (2017) Shrubland primary production and soil respiration diverge along European climate gradient. Scientific Reports 7:43952 https://doi.org/10.1038/srep43952 Standing biomass was measured using the non-destructive pin-point method to assess aboveground biomass. Measurements were conducted at the state of peak biomass specific for each site. Litterfall was measured annually using litterfall traps. Litter collected in the traps was dried and the weight was measured. Aboveground biomass productivity was estimated as the difference between the measured standing biomass in year x minus the standing biomass measured the previous year. Soil respiration was measured bi-weekly or monthly, or in campaigns (Spain only). It was measured on permanently installed soil collars in treatment plots. The Gaussen Index of Aridity (an index that combines information on rainfall and temperature) was calculated using mean annual precipitation, mean annual temperature. The reduction in precipitation and increase in temperature for each site was used to calculate the Gaussen Index for the climate treatments for each site. Data of standing biomass and soil respiration was provided by the site responsible. Data from all sites were collated into one data file for data analysis. A summary data set was combined with information on the Gaussen Index of Aridity Data were then exported from these Excel spreadsheet to .csv files for ingestion into the EIDC.

Here, we investigate how stochasticity and age-dependence in energy dynamics influence maternal allocation in iteroparous females. We develop a state-dependent model to calculate the optimal maternal allocation strategy with respect to maternal age and energy reserves, focusing on allocation in a single offspring at a time. We introduce stochasticity in energetic costs– in terms of the amount of energy required to forage successfully and individual differences in metabolism – and in feeding success. We systematically assess how allocation is influenced by age-dependence in energetic costs, feeding success, energy intake per successful feeding attempt, and environmentally-driven mortality. First, using stochastic dynamic programming, we calculate the optimal amount of reserves M that mothers allocate to each offspring depending on their own reserves R and age A. The optimal life history strategy is then the set of allocation decisions M(R, A) over the whole lifespan which maximizes the total reproductive success of distant descendants. Second, we simulated the life histories of 1000 mothers following the optimisation strategy and the reserves at the start of adulthood R1, the distribution of which was determined, the distribution of which was determined using an iterative procedure as described . For each individual, we calculated maternal allocation Mt, maternal reserves Rt, and relative allocation Mt⁄Rt at each time period t. The relative allocation helps us to understand how resources are partitioned between mother and offspring. Third, we consider how the optimal strategy varies when there is age-dependence in resource acquisition, energetic costs and survival. Specifically, we include varying scenarios with an age-dependent increase or a decrease with age in energetic costs (c_t), feeding success (q_t), energy intake per successful feeding attempt (y_t), and environmentally-driven extrinsic mortality rate (d_t) (Table 2). We consider the age-dependence of parameters one at a time or in pairs, altering the slope, intercept, or asymptote of the age-dependence (linear or asymptotic function). Our aim is to identify whether the observed reproductive senescence can arise from optimal maternal allocation. As such, we do not impose a decline in selection in later life as all offspring are equally valuable at all ages (for a given maternal allocation), and there are no mutations. For each scenario, we run the backward iteration process with these age-dependent functions, obtain the allocation strategy, and simulate the life history of 1000 individuals based on the novel strategy. We then fit quadratic and linear models to the reproduction of these 1000 individuals using the lme function, nlme package in R. For these models, the response variable is the maternal allocation Mt and explanatory variables are the time period t and t2 (for the quadratic fit only), with individual identity as a random term. We use likelihood ratio tests to compare linear and quadratic models using the anova function (package nlme) with the maximum-likelihood method. If the comparison is significant (p-value <0.05), we considered the quadratic model to have a better fit, otherwise the linear model is considered more parsimonious. We were particularly interested in identifying scenarios where the fit was quadratic with a negative quadratic term. For each scenario, the pseudo R2 conditional value (proportion of variance explained by the fixed and random terms, accounting for individual identity) is calculated to assess the goodness-of-fit of the lme model, on a scale from 0 to 1, using the “r.squared” function, package gabtool. All calculations and coding are done in R. Iteroparous parents face a trade-off between allocating current resources to reproduction versus maximizing survival to produce further offspring. Optimal allocation varies across age, and follows a hump-shaped pattern across diverse taxa, including mammals, birds and invertebrates. This non-linear allocation pattern lacks a general theoretical explanation, potentially because most studies focus on offspring number rather than quality and do not incorporate uncertainty or age-dependence in energy intake or costs. Here, we develop a life history model of maternal allocation in iteroparous animals. We identify the optimal allocation strategy in response to stochasticity when energetic costs, feeding success, energy intake, and environmentally-driven mortality risk are age-dependent. As a case study, we use tsetse, a viviparous insect that produces one offspring per reproductive attempt and relies on an uncertain food supply of vertebrate blood. Diverse scenarios generate a hump-shaped allocation: when energetic costs and energy intake increase with age; and also when energy intake decreases, and energetic costs increase or decrease. Feeding success and mortality risk have little influence on age-dependence in allocation. We conclude that ubiquitous evidence for age-dependence in these influential traits can explain the prevalence of non-linear maternal allocation across diverse taxonomic groups.

Aim: Climate warming and biodiversity loss both alter plant productivity, yet we lack an understanding of how biodiversity regulates the responses of ecosystems to warming. In this study, we examine how plant diversity regulates the responses of grassland productivity to experimental warming using meta-analytic techniques. Location: Global Major taxa studied: Grassland ecosystems Methods: Our meta-analysis is based on warming responses of 40 different plant communities obtained from 20 independent studies on grasslands across five continents. Results: Our results show that plant diversity and its responses to warming were the most important factors regulating the warming effects on plant productivity, among all the factors considered (plant diversity, climate and experimental settings). Specifically, warming increased plant productivity when plant diversity (indicated by effective number of species) in grasslands was lesser than 10, whereas warming decreased plant productivity when plant diversity was greater than 10. Moreover, the structural equation modelling showed that the magnitude of warming enhanced plant productivity by increasing the performance of dominant plant species in grasslands of diversity lesser than 10. The negative effects of warming on productivity in grasslands with plant diversity greater than 10 were partly explained by diversity-induced decline in plant dominance. Main Conclusions: Our findings suggest that the positive or negative effect of warming on grassland productivity depends on how biodiverse a grassland is. This could mainly owe to differences in how warming may affect plant dominance and subsequent shifts in interspecific interactions in grasslands of different plant diversity levels.

The data used in this article are related to the rice yield following the use of the RiceAdvice technology recommendation and the rice yield following the recommended level of fertilizer recommendations. The data were collected in 5 sites in Mali. In all sites, rice yield with RiceAdvice was higher than rice yield following the conventional levels of fertilizer. On average, rice yield with RiceAdvice was 0.7 t/ha higher than rice yield with the conventional level of fertilizer recommendations.

The Intergovernmental Panel on Climate Change (IPCC) report that to limit warming to 1.5 °C, Bioenergy with Carbon Capture and Storage (BECCS) is required. Integrated assessment models (IAMS) predict that a land area between the size of Argentina and Australia is required for bioenergy crops, a 3–7 time increase in the current bioenergy planting area globally. The authors pose the question of whether vertical farming (VF) technology can enable BECCS deployment, either via land sparing or supply. VF involves indoor controlled environment cultivation, and can increase productivity per unit land area by 5–10 times. VF is predominantly being used to grow small, high value leafy greens with rapid growth cycles. Capital expenditure, operational expenditure, and sustainability are challenges in current VF industries, and will affect the ability to utilise this technology for other crops. The authors argue that, whilst challenging, VF could help reach wider climate goals. Application of VF for bioenergy crops could be a game changer in delivering BECCS technologies and may reduce the land footprint required as well as the subsequent associated negative environmental impacts. VF bioenergy could allow us to cultivate the future demand for bioenergy for BECCS on the same, or less, land area than is currently used globally.

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.

Data was either measured in situ in the field (N2O flux, soil moisture, rainfall and air temperature) or samples were taken, processed, and analysed in the laboratory (soil pH, electrical conductivity (EC), ammonium, nitrate, microbial community composition and crop yield). N2O flux data was measured on a mobile gas chromatograph (GC) system and integrated to obtain peak areas on Peak490Win10Canabis programme. The times, peak areas and sample ID were then exported into a .CHR file and imported into Flux.NET.3.3 which calculated N2O flux as an output in Excel which was exported as .csv file for deposit in EIDC. N2O flux was used to calculate cumulative N2O flux using trapezoidal integration in Excel and saved in a separate .csv file for deposit in EIDC. Soil moisture was measured on Accilmas with data stored as a .csv on a DataSnap that was downloaded and sorted by treatment and saved as a .csv file. Rainfall and air temperature were downloaded from the weather station as .csv file. Soil pH and EC were recorded manually into a notebook and input into an Excel spreadsheet and exported as a .csv file. Soil ammonium and nitrate content was measured using the microplate method using a programme called Gen5. Date was exported into an Excel spreadsheet and absorbance units used to calculate ammonium/nitrate content in milligrams per kilogram using a calibration curve from a set of standards in an Excel spreadsheet. This was exported as a .csv file. Crop growth data was recorded in the field in a notebook and input into an Excel spreadsheet and exported as a .csv file. Crop yield was recorded in a notebook and input into an Excel spreadsheet and exported as a .csv file. Microbial community composition was measured using 16S gene sequencing on an Illumina MiSeq. This generated raw sequencing reads which were processed using Python and filtered using QIIME v1.3.1. creating asv.count.table.csv of counts of each Amplicon Sequence Variants (ASVs) per sample and taxa.table.csv of the taxonomic lineage for each ASVs. This dataset contains field data on nitrous oxide (N2O) emissions, microbial community composition, crop yield and growth and soil biochemical properties. The field trial consisted of three different treatments of control, conventional microplastic addition and biodegradable microplastic addition where winter barley was grown. The data presented are from field and laboratory measurements. Data was collected by the data authors. The field trial was carried out from September 2020 to July 2021 at Henfaes Field Centre, UK. Research was funded through NERC Grant NE/V005871/1. Do agricultural microplastics undermine food security and sustainable development in developing countries?