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Background Termites are among the most successful insects on Earth and can feed on a broad range of organic matter at various stages of decomposition. The termite gut system is often referred to as a micro-reactor and is a complex structure consisting of several components. It includes the host, its gut microbiome and fungal gardens, in the case of fungi-growing higher termites. The digestive tract of soil-feeding higher termites is characterised by radial and axial gradients of physicochemical parameters (e.g. pH, O2 and H2 partial pressure), and also differs in the density and structure of residing microbial communities. Although soil-feeding termites account for 60% of the known termite species, their biomass degradation strategies are far less known compared to their wood-feeding counterparts. Results In this work, we applied an integrative multi-omics approach for the first time at the holobiont level to study the highly compartmentalised gut system of the soil-feeding higher termite Labiotermes labralis. We relied on 16S rRNA gene community profiling, metagenomics and (meta)transcriptomics to uncover the distribution of functional roles, in particular those related to carbohydrate hydrolysis, across different gut compartments and among the members of the bacterial community and the host itself. We showed that the Labiotermes gut was dominated by members of the Firmicutes phylum, whose abundance gradually decreased towards the posterior segments of the hindgut, in favour of Bacteroidetes, Proteobacteria and Verrucomicrobia. Contrary to expectations, we observed that L. labralis gut microbes expressed a high diversity of carbohydrate active enzymes involved in cellulose and hemicelluloses degradation, making the soil-feeding termite gut a unique reservoir of lignocellulolytic enzymes with considerable biotechnological potential. We also evidenced that the host cellulases have different phylogenetic origins and structures, which is possibly translated into their different specificities towards cellulose. From an ecological perspective, we could speculate that the capacity to feed on distinct polymorphs of cellulose retained in soil might have enabled this termite species to widely colonise the different habitats of the Amazon basin. Conclusions Our study provides interesting insights into the distribution of the hydrolytic potential of the highly compartmentalised higher termite gut. The large number of expressed enzymes targeting the different lignocellulose components make the Labiotermes worker gut a relevant lignocellulose-valorising model to mimic by biomass conversion industries.

Miscanthus sp. biomass could satisfy future biorefinery value chains. However, its use is largely untapped due to high recalcitrance. The termite and its gut microbiome are considered the most efficient lignocellulose degrading system in nature. Here, we investigate at holobiont level the dynamic adaptation of Cortaritermes sp. to imposed Miscanthus diet, with a long-term objective of overcoming lignocellulose recalcitrance. We use an integrative omics approach combined with enzymatic characterisation of carbohydrate active enzymes from termite gut Fibrobacteres and Spirochaetae. Modified gene expression profiles of gut bacteria suggest a shift towards utilisation of cellulose and arabinoxylan, two main components of Miscanthus lignocellulose. Low identity of reconstructed microbial genomes to closely related species supports the hypothesis of a strong phylogenetic relationship between host and its gut microbiome. This study provides a framework for better understanding the complex lignocellulose degradation by the higher termite gut system and paves a road towards its future bioprospecting., SCOPUS: ar.j, info:eu-repo/semantics/published

Background: Termites are among the most successful insect lineages on the globe and are responsible for providing numerous ecosystem services. They mainly feed on wood and other plant material at different stages of humification. Lignocellulose is often a principal component of such plant diet, and termites largely rely on their symbiotic microbiota and associated enzymes to decompose their food efficiently. While lower termites and their gut flagellates were given larger scientific attention in the past, the gut lignocellulolytic bacteria of higher termites remain less explored. Therefore, in this study, we investigated the structure and function of gut prokaryotic microbiomes from 11 higher termite genera representative of Syntermitinae, Apicotermitinae, Termitidae and Nasutitermitinae subfamilies, broadly grouped into plant fibre- and soil-feeding termite categories. Results: Despite the different compositional structures of the studied termite gut microbiomes, reflecting well the diet and host lineage, we observed a surprisingly high functional congruency between gut metatranscriptomes from both feeding groups. The abundance of transcripts encoding for carbohydrate active enzymes as well as expression and diversity profiles of assigned glycoside hydrolase families were also similar between plant fibre- and soil-feeding termites. Yet, dietary imprints highlighted subtle metabolic differences specific to each feeding category. Roughly, 0.18% of de novo re-constructed gene transcripts were shared between the different termite gut microbiomes, making each termite gut a unique reservoir of genes encoding for potentially industrially applicable enzymes, e.g. relevant to biomass degradation. Taken together, we demonstrated the functional equivalence in microbial populations across different termite hosts. Conclusions: Our results provide valuable insight into the bacterial component of the termite gut system and significantly expand the inventory of termite prokaryotic genes participating in the deconstruction of plant biomass. [MediaObject not available: see fulltext.], SCOPUS: ar.j, info:eu-repo/semantics/published

Miscanthussp. is regarded as suitable biomass for different biorefinery value chains. However, due to high recalcitrance, its wide use is yet untapped. Termite is the most efficient lignocellulose degrading insect, and its success results from synergistic cooperation with its gut microbiome. Here, we investigated at holobiont level the dynamic adaptation of a higher termiteCortaritermessp. to imposedMiscanthusdiet, with a long-term objective of overcoming lignocellulose recalcitrance. We used an integrative omics approach, comprising amplicon sequencing, metagenomics and metatranscriptomics that we combined with enzymatic characterisation of carbohydrate active enzymes from termite gut Fibrobacteres and Spirochaetae. Adaptation to the new diet was evidenced by reduced gut bacterial diversity and modified gene expression profiles, further suggesting a shift towards utilisation of cellulose and arabinoxylan, two main components ofMiscanthuslignocellulose. Low identity of reconstructed microbial genomes to microbes from closely related termite species, supported the hypothesis of a strong phylogenetic relationship between host and its gut microbiome. Application-wise, this makes each termite gut system an endless source of enzymes that are potentially industrially relevant.This study provides a framework for better understanding the complex lignocellulose degradation by the higher termite gut system and paves a road towards its future bioprospecting.

Three anaerobic digestion reactors were initially maintained at steady-state with low nitrogen input, then progressively exposed to increasing nitrogen input until extreme ammonia intoxication, leading to interruption of biogas production. Such interruption occurred for a free ammonia nitrogen (FAN) concentration of about 5 g Lslurry−1. A fourth reactor used as a control was maintained at low nitrogen steady-state. The biogas composition (CH4, CO2, H2 and H2S) was recorded over time and data were fed into a multivariate statistical process control model based on principal components analysis (PCA-MSPC). Static PCA-MSPC failed to distinguish between the actual reactor performance decrease and the drift of the in-control process baseline. However, recursively updated PCA-MSPC (effective memory length of 14 days, i.e. 0.25 hydraulic retention time) allowed detection of the early signs of FAN induced process dysfunction, which were simultaneous with drastic changes in the microbiome of the replicate reactors. However, the process control model was not able to adapt to a drastic modification of the microbiome. Propionate continuously accumulated since the detection of the first signs of process dysfunction.

Two industrial-scale membrane filtration pilots were evaluated on their ability to separate organic matter, nutrients and water from digestates. Both pilots were equipped with a screw press for solid-liquid separation, followed sequentially by a dynamic nanofiltration treating the liquid phase of digestates and two reverse osmosis filtrations. The prototypes were efficient in nutrient recovery and separation. Indeed, the nanofiltration concentrate represented 45–55% of the output fraction and concentrated >90% of the organic matter, >95% of phosphorus and >90% of the organic nitrogen of the digestate liquid fraction. Two groups of fertilizers were obtained: (i) organic NPK fertilizers including solid fractions of the digestate and retentates of nanofiltration (5–8% wet weight (ww) of organic matter, 0.16–0.35%ww P2O5 and a total NPK content of 1.1–1.45%ww), and (ii) mineral NK fertilizers composed of retentates of reverse osmosis filtration (ammonia nitrogen: 0.2–0.43%ww N; potassium: 0.22−0.73%ww K2O). Water recovery rate was 10–12% of the digestate liquid phase input. Chemical oxygen demand and total suspended solids in permeate water were 27–37 mgO2 L−1 and 2–2.5 mg L−1, respectively, whereas the ammonia nitrogen and potassium content was above 600 mg L−1. Energy consumption was 11.6 kWhel t−1digestate. Dynamic nanofiltration combined with low-pressure reverse osmosis constitutes a valuable technique for nutrient recovery and fractionation in anaerobic digestates with low energy consumption. However, this technique is still in need of further optimization, especially for both ammonia nitrogen and potassium retention, and water recovery.

To mitigate the effects of climate change, the emission of greenhouse gases (GHG) should be reduced. In the near future, an increased use of biomass for energy production will be prescribed by law in the European Union (EU) to achieve the target of emission reduction. One possibility to use biomass energetically is its transformation into biogas. The most widely grown crop for this purpose is maize. Although, the increased use of maize as biogas feedstock, due to generous subsidies, is linked to several ecological and (socio)economic problems. The most important are soil erosion, depletion and compaction, high demand and subsequent leaching of fertilizers/biocides, heavy workload, and competition for land between food and energy production. In contrast, the use of second-generation perennial energy crops (PECs) as biogas feedstock can be an auspicious approach to improve the environmental footprint of biomass production. Therefore, we evaluated and compared the biochemical methane potential (BMP) of five different perennial species (cup plant, virginia mallow, tall wheatgrass, giant knotweed, reed canary grass) and a wild plant mix (composite of 25 in parts perennial plants) on three different sites in West-Germany. In terms of methane production per area, tall wheatgrass and reed canary grass exceeded the productivity of maize under favorable conditions. Hence, we recommend both species as biogas feedstock for Central Europe or comparable climates. Additionally, other species might be suitable for biomethanation from an overall perspective, to enhance (agro)biodiversity in rural areas.

Meteorological conditions are important factors in the development of fungal diseases in winter wheat and are the main inputs of the decision support systems used to forecast disease and thus determine timing for efficacious fungicide application. This study uses the Fourier transform method (FTM) to characterize temporal patterns of meteorological conditions between two neighbouring experimental sites used in a regional fungal disease monitoring and forecasting experiment in Luxembourg. Three meteorological variables (air temperature, relative humidity, and precipitation) were included, all conducive to infection of wheat by Zymoseptoria tritici cause of Septoria leaf blotch (STB) in winter wheat, from 2006 to 2009. The intraday, diurnal, dekadal and intra-seasonal variations of the meteorological variables were assessed using FTM, and the impact of existing contrasts between sites on the development of STB was analyzed. Although STB severities varied between sites and years (P ≤ 0.0003), the results indicated that the two sites presented the same patterns of meteorological conditions when compared at larger temporal scales (diurnal to intra-seasonal scales, with time periods >11 h). However, the intraday variations of all the variables were well discriminated between the sites and were highly correlated to STB severities. Our findings highlight and confirm the importance of intraday meteorological variation in the development of STB in winter wheat fields. Furthermore, the FTM approach has potential for identifying microclimatic conditions prevailing at given sites and could help in improving the prediction of disease forecast models used in regional warning systems.

The use of perennial crops instead of maize as feedstock in biogas plants can be associated with multiple environmental and economic benefits. One promising species in this domain is the C4-grass Miscanthus × giganteus. The use of its biomass can mitigate carbon dioxide emissions by substitution of fossil fuels, sequestration of carbon in soils and reduced fertilizing. We compared Miscanthus from two different old fields (established 1995 and 2008) at three different harvest dates over 2 years. While the harvest in spring, like usual for combustion purposes, led to relatively low methane yields per hectare, the harvest in autumn, when the biomass is still green, exceeded the average methane yields per hectare of maize. The comparison of different old Miscanthus fields showed that there is no significant difference in terms of biomass yield, specific BMP and BMP per hectare. Only the influence of repeated autumn harvest showed differences in the methane production per hectare between both stand ages. The methane yield of the younger stand did not change considerable, while in the older stand, the productivity decreased about 15% after 1 year.

To provide sufficient quantities of food and feed, farming systems have to overcome limiting factors such as the nutrient depletion of arable soils. Nitrogen being the main mineral element required for plant growth, has led to the extensive use of chemical fertilizers causing nitrogen pollution of the ecosystems. This field study investigates the use of biogas residues (BRs) as biofertilizers and their contribution to the mitigation of nitrate leaching in agricultural soils, while also demonstrating the polluting nature of chemical fertilizers. Nine different fertilization treatments classified in three schemes and two nitrogen doses were tested for three consecutive years on a grassland in the Walloon Region of Belgium. Residual soil mineral nitrogen, percentage contribution of treatments in residual nitrate and agronomic performance were assessed for each fertilization treatment. The results obtained showed significant differences on treatment and scheme level regarding nitrate accumulation in the soil, with chemical fertilizers posing the highest nitrate leaching risk. BRs did not cause nitrate accumulation in the soil, and were N rate and rainfall independent, while the chemical treatments indicated a cumulative tendency under high N rate and low precipitation. Forage yield did not demonstrate statistical differences on treatment and scheme level but varied with changing precipitation, while the maximum application rate suggested a plateau. Aboveground nitrogen content was significantly higher after the application of chemical fertilizers only in the first year, while all the chemical treatments indicated a dilution effect under elevated annual rainfall. Finally, the partial substitution of chemical fertilizers by raw digestate reduced the concentration of NO 3 − in the soil without having a negative impact on the yield and N content of the biomass. These results strongly advocate for the environmental benefits of BRs over chemical fertilizers and underline their suitability as biofertilizers and substitutes for chemical fertilizers in similar agricultural systems., SCOPUS: ar.j, info:eu-repo/semantics/published

An enzyme-linked immunosorbent assay (ELISA) procedure was developed to test peanut seed for Indian peanut clump virus (IPCV). A double antibody sandwich form of ELISA detected the Hyderabad isolate (IPCV-H) in seed of peanut. Correlation was established between the results from ELISA performed on cotyledons of peanut seed and grow-out tests. Seed transmission in the field-infected peanut plants ranged from 3.5 to 17%, depending on the genotype. The transmission frequency was 48 to 55% in seed collected from plants infected through seed. Because testae of all seed contained viral antigen, their removal was essential for the determination of frequency of seed transmission. Apparently the virus present only in cotyledons and embryo contributed to the seed transmission. For the first time, IPCV-H was shown to be seed transmitted in finger millet (Eleusine coracana), foxtail millet (Setaria italica), and pearl millet (Pennisetum glaucum) at frequencies of 5.2, 9.7, and 0.9%, respectively. Seed transmission was not observed in sorghum (Sorghum bicolor). Significance of seed transmission in millet crops is discussed.

The recycling of biogas residues resulting from the anaerobic digestion of organic waste on agricultural land is among the means to reduce chemical fertilizer use and combat climate change. This in sacco decomposition study investigates (1) the potential of the granulated biogas residue fraction to provide nutrients and enhance soil carbon sequestration when utilized as exogenous organic matter in grassland soils, and (2) the impact of different nitrogen fertilizers on the organic matter decomposition and nutrient release processes. The experiment was conducted in two permanent grasslands of the Greater Region over one management period using rooibos tea as a comparator material. The decomposition and chemical changes of the two materials after incubation in the soil were assessed by measuring the mass loss, total carbon and nitrogen status, and fibre composition in cellulose, hemicellulose and lignin. Overall, after the incubation period, granulated biogas residue maintained up to 68% of its total mass, organic matter and total carbon; increased its content in recalcitrant organic matter by up to 45% and released 45% of its total nitrogen. Granulated biogas residue demonstrated resilience and a higher response uniformity when exposed to different nitrogen fertilizers, as opposed to the comparator material of rooibos tea. However, the magnitude of fertilizer-type effect varied, with ammonium nitrate and the combinatorial treatment of raw biogas residue mixed with urea leading to the highest organic matter loss from the bags. Our findings suggest that granulated biogas residue is a biofertilizer with the potential to supply nutrients to soil biota over time, and promote carbon sequestration in grassland soils, and thereby advance agricultural sustainability while contributing to climate change mitigation.

The effect of rater bias and assessment method on hypothesis testing was studied for representative experimental designs for plant disease assessment using balanced and unbalanced data sets. Data sets with the same number of replicate estimates for each of two treatments are termed “balanced” and those with unequal numbers of replicate estimates are termed “unbalanced”. The three assessment methods considered were nearest percent estimates (NPEs), an amended 10% incremental scale, and the Horsfall-Barratt (H-B) scale. Estimates of severity of Septoria leaf blotch on leaves of winter wheat were used to develop distributions for a simulation model. The experimental designs are presented here in the context of simulation experiments which consider the optimal design for the number of specimens (individual units sampled) and the number of replicate estimates per specimen for a fixed total number of observations (total sample size for the treatments being compared). The criterion used to gauge each method was the power of the hypothesis test. As expected, at a given fixed number of observations, the balanced experimental designs invariably resulted in a higher power compared with the unbalanced designs at different disease severity means, mean differences, and variances. Based on these results, with unbiased estimates using NPE, the recommended number of replicate estimates taken per specimen is 2 (from a sample of specimens of at least 30), because this conserves resources. Furthermore, for biased estimates, an apparent difference in the power of the hypothesis test was observed between assessment methods and between experimental designs. Results indicated that, regardless of experimental design or rater bias, an amended 10% incremental scale has slightly less power compared with NPEs, and that the H-B scale is more likely than the others to cause a type II error. These results suggest that choice of assessment method, optimizing sample number and number of replicate estimates, and using a balanced experimental design are important criteria to consider to maximize the power of hypothesis tests for comparing treatments using disease severity estimates.

A static multivariate statistical process control model based on principal component analysis (PCA-MSPC) was developed for an anaerobic reactor maintained in steady-state, joining the biogas composition (CH4, CO2, H2) to the total solids (TS), volatile solids (VS), total inorganic carbon (TIC) and total ammonia nitrogen (TAN) contents of the slurry. The principal component analysis (PCA) highlighted a lack of correlation between the individual process variables (IPVs) measured in the slurry and the gas phase. The application of this model to the data set collected for an independent anaerobic reactor (fed with the same substrate) progressively led from steady-state to critical volatile fatty acids (VFA) intoxication did not allow evaluating its process status. A second static PCA-MSPC model was built for the steady-state reactor in excluding the TS and VS content of the slurry and was successfully transferred to the overfed reactor. The TA² and SPE control charts built for the overfed reactor using this second model closely reflected its process status and delivered valuable warning signals approaching the VFA intoxication. The gas phase composition (CH4, CO2, H2) brought the main contribution to these warnings.

Wheat stripe rust (caused by Puccinia striiformis f. sp. tritici) is a major threat in most wheat growing regions worldwide, which potentially causes substantial yield losses when environmental conditions are favorable. Data from 1999 to 2015 for three representative wheat-growing sites in Luxembourg were used to develop a threshold-based weather model for predicting wheat stripe rust. First, the range of favorable weather conditions using a Monte Carlo simulation method based on the Dennis model were characterized. Then, the optimum combined favorable weather variables (air temperature, relative humidity, and rainfall) during the most critical infection period (May-June) was identified and was used to develop the model. Uninterrupted hours with such favorable weather conditions over each dekad (i.e., 10-day period) during May-June were also considered when building the model. Results showed that a combination of relative humidity >92% and 4°C < temperature < 16°C for a minimum of 4 continuous hours, associated with rainfall ≤0.1 mm (with the dekad having these conditions for 5 to 20% of the time), were optimum to the development of a wheat stripe rust epidemic. The model accurately predicted infection events: probabilities of detection were ≥0.90 and false alarm ratios were ≤0.38 on average, and critical success indexes ranged from 0.63 to 1. The method is potentially applicable to studies of other economically important fungal diseases of other crops or in different geographical locations. If weather forecasts are available, the threshold-based weather model can be integrated into an operational warning system to guide fungicide applications.

The enzymatic hydrolysis of lignocellulosic biomass is mainly driven by the action of carbohydrate-active enzymes. By characterizing the gene profiles at the different stages of the anaerobic digestion experiment, we showed that the microbiome retains its hydrolytic functional redundancy even during severe acidosis, despite significant changes in taxonomic composition. By analyzing reconstructed bacterial genomes, we demonstrate that Bacteroidetes hydrolytic gene diversity likely favors the abundance of this phylum in some anaerobic digestion systems. Further, we observe genetic redundancy within the Bacteroidetes group, which accounts for the preserved hydrolytic potential during acidosis. This work also uncovers new polysaccharide utilization loci involved in the deconstruction of various biomasses and proposes the model of acetylated glucomannan degradation by Bacteroidetes. Acetylated glucomannan-enriched biomass is a common substrate for many industries, including pulp and paper production. Using naturally evolved cocktails of enzymes for biomass pretreatment could be an interesting alternative to the commonly used chemical pretreatments., Increased hydrolysis of easily digestible biomass may lead to acidosis of anaerobic reactors and decreased methane production. Previously, it was shown that the structure of microbial communities changed during acidosis; however, once the conditions are back to optimal, biogas (initially CO2) production quickly restarts. This suggests the retention of the community functional redundancy during the process failure. In this study, with the use of metagenomics and downstream bioinformatics analyses, we characterize the carbohydrate hydrolytic potential of the microbial community, with a special focus on acidosis. To that purpose, carbohydrate-active enzymes were identified, and to further link the community hydrolytic potential with key microbes, bacterial genomes were reconstructed. In addition, we characterized biochemically the specificity and activity of selected enzymes, thus verifying the accuracy of the in silico predictions. The results confirm the retention of the community hydrolytic potential during acidosis and indicate Bacteroidetes to be largely involved in biomass degradation. Bacteroidetes showed higher diversity and genomic content of carbohydrate hydrolytic enzymes that might favor the dominance of this phylum over other bacteria in some anaerobic reactors. The combination of bioinformatic analyses and activity tests enabled us to propose a model of acetylated glucomannan degradation by Bacteroidetes. IMPORTANCE The enzymatic hydrolysis of lignocellulosic biomass is mainly driven by the action of carbohydrate-active enzymes. By characterizing the gene profiles at the different stages of the anaerobic digestion experiment, we showed that the microbiome retains its hydrolytic functional redundancy even during severe acidosis, despite significant changes in taxonomic composition. By analyzing reconstructed bacterial genomes, we demonstrate that Bacteroidetes hydrolytic gene diversity likely favors the abundance of this phylum in some anaerobic digestion systems. Further, we observe genetic redundancy within the Bacteroidetes group, which accounts for the preserved hydrolytic potential during acidosis. This work also uncovers new polysaccharide utilization loci involved in the deconstruction of various biomasses and proposes the model of acetylated glucomannan degradation by Bacteroidetes. Acetylated glucomannan-enriched biomass is a common substrate for many industries, including pulp and paper production. Using naturally evolved cocktails of enzymes for biomass pretreatment could be an interesting alternative to the commonly used chemical pretreatments.

Background Anaerobic digestion (AD) is a microbe-driven process of biomass decomposition to CH4 and CO2. In addition to renewable and cost-effective energy production, AD has emerged in the European Union as an environmentally friendly model of bio-waste valorisation and nutrient recycling. Nevertheless, due to the high diversity of uncharacterised microbes, a typical AD microbiome is still considered as “dark matter”. Results Using the high-throughput sequencing of small rRNA gene, and a monthly monitoring of the physicochemical parameters for 20 different mesophilic full-scale bioreactors over 1 year, we generated a detailed view of AD microbial ecology towards a better understanding of factors that influence and shape these communities. By studying the broadly distributed OTUs present in over 80% of analysed samples, we identified putatively important core bacteria and archaea to the AD process that accounted for over 70% of the whole microbial community relative abundances. AD reactors localised at the wastewater treatment plants were shown to operate with distinct core microbiomes than the agricultural and bio-waste treating biogas units. We also showed that both the core microbiomes were composed of low (with average community abundance ≤ 1%) and highly abundant microbial populations; the vast majority of which remains yet uncharacterised, e.g. abundant candidate Cloacimonetes. Using non-metric multidimensional scaling, we observed microorganisms grouping into clusters that well reflected the origin of the samples, e.g. wastewater versus agricultural and bio-waste treating biogas units. The calculated diversity patterns differed markedly between the different community clusters, mainly due to the presence of highly diverse and dynamic transient species. Core microbial communities appeared relatively stable over the monitoring period. Conclusions In this study, we characterised microbial communities in different AD systems that were monitored over a 1-year period. Evidences were shown to support the concept of a core community driving the AD process, whereas the vast majority of dominant microorganisms remain yet to be characterised. Electronic supplementary material The online version of this article (10.1186/s13068-018-1195-8) contains supplementary material, which is available to authorized users.

The first step in the formulation of disease management strategy for any cropping system is to identify the most important risk factors. This is facilitated by basic epidemiological studies of pathogen life cycles, and an understanding of the way in which weather and cropping factors affect the quantity of initial inoculum and the rate at which the epidemic develops. Weather conditions are important factors in the development of fungal diseases in winter wheat, and constitute the main inputs of the decision support systems used to forecast disease and thus determine the timing for efficacious fungicide application. Crop protection often relies on preventive fungicide applications. Considering the slim cost−revenue ratio for winter wheat and the negative environmental impacts of fungicide overuse, necessity for applying only sprays that are critical for disease control becomes paramount for a sustainable and environmentally friendly crop production. Thus, fungicides should only be applied at critical stages for disease development, and only after the pathogen has been correctly identified. This chapter provides an overview of different weather-based disease models developed for assessing the real-time risk of epidemic development of the major fungal diseases (Septoria leaf blotch, leaf rusts and Fusarium head blight) of winter wheat in Luxembourg.

Moonmilk are cave carbonate deposits that host a rich microbiome, including antibiotic-producing Actinobacteria, making these speleothems appealing for bioprospecting. Here, we investigated the taxonomic profile of the actinobacterial community of three moonmilk deposits of the cave “Grotte des Collemboles” via high-throughput sequencing of 16S rRNA amplicons. Actinobacteria was the most common phylum after Proteobacteria, ranging from 9% to 23% of the total bacterial population. Next to actinobacterial operational taxonomic units (OTUs) attributed to uncultured organisms at the genus level (~44%), we identified 47 actinobacterial genera with Rhodoccocus (4 OTUs, 17%) and Pseudonocardia (9 OTUs, ~16%) as the most abundant in terms of the absolute number of sequences. Streptomycetes presented the highest diversity (19 OTUs, 3%), with most of the OTUs unlinked to the culturable Streptomyces strains that were previously isolated from the same deposits. Furthermore, 43% of the OTUs were shared between the three studied collection points, while 34% were exclusive to one deposit, indicating that distinct speleothems host their own population, despite their nearby localization. This important spatial diversity suggests that prospecting within different moonmilk deposits should result in the isolation of unique and novel Actinobacteria. These speleothems also host a wide range of non-streptomycetes antibiotic-producing genera, and should therefore be subjected to methodologies for isolating rare Actinobacteria.

Moonmilk are cave carbonate deposits that host a rich microbiome including antibiotic-producing Actinobacteria making these speleothems appealing for bioprospecting. Here we investigated the taxonomic profile of the actinobacterial community of three moonmilk deposits of the cave “Grotte des Collemboles” via high-throughput sequencing of 16S rRNA amplicons. Actinobacteria was the most common phylum after Proteobacteria, ranging from 9 to 23% of the total bacterial population. Next to actinobacterial OTUs attributed to uncultured organisms at the genus level (~44%), we identified 47 actinobacterial genera with Rhodoccocus (4 OTUs, 17%) and Pseudonocardia (9 OTUs, ~16%) as the most abundant in terms of absolute number of sequences. Streptomycetes presented the highest diversity (19 OTUs, 3%), with most of OTUs unlinked to the culturable Streptomyces strains previously isolated from the same deposits. 43% of OTUs were shared between the three studied collection points while 34% were exclusive to one deposit indicating that distinct speleothems host their own population despite their nearby localization. This important spatial diversity suggests that prospecting within different moonmilk deposits should result in the isolation of unique and novel Actinobacteria. These speleothems also host a wide range of non-streptomycetes antibiotic-producing genera, and should therefore be subjected to methodologies for isolating rare Actinobacteria.

Summary Although viruses are not the key players of the anaerobic digestion process, they may affect the dynamics of bacterial and archaeal populations involved in biogas production. Until now viruses have received very little attention in this specific habitat; therefore, as a first step towards their characterization, we optimized a virus filtration protocol from anaerobic sludge. Afterwards, to assess dsDNA and RNA viral diversity in sludge samples from nine different reactors fed either with waste water, agricultural residues or solid municipal waste plus agro‐food residues, we performed metagenomic analyses. As a result we showed that, while the dsDNA viromes (21 assigned families in total) were dominated by dsDNA phages of the order C audovirales, RNA viruses (14 assigned families in total) were less diverse and were for the main part plant‐infecting viruses. Interestingly, less than 2% of annotated contigs were assigned as putative human and animal pathogens. Our study greatly extends the existing view of viral genetic diversity in methanogenic reactors and shows that these viral assemblages are distinct not only among the reactor types but also from nearly 30 other environments already studied, including the human gut, fermented food, deep sea sediments and other aquatic habitats.

Foliar fungicides in wheat are typically used to safeguard against economic losses from diseases. In this study, we assessed the effects of three fungicide spray regimes [single, double, and triple treatments] on four different grain quality parameters [thousand grain weight (TGW), test weight (TW), grain protein content (GPC), and Zeleny sedimentation volume (ZSV)] during the 2006–2009 period at two sites in Luxembourg. The fungicides used were generally a mix of chlorothalonil and triazoles. At Burmerange,(cultivar Cubus), the values of TGW, TW, GPC and ZSV ranged from 38 to 62 g, 67 to 83 kg hl−1, 12.0%to 14.7% dry matter (DM), and 27 to 54 ml, respectively. Whereas, at Everlange (cultivar Achat), the ranges of TGW, TW, GPC and ZSV were 42 to 65 g, 65 to 81 kg hl−1, 11.0% to 15.0% DM, and 21 to 66 ml,respectively. In more than 75% cases, the results indicate that fungicides did not significantly affect TW or ZSV at either sites (P > 0.05). However, there was a significant and positive fungicide effect on GPC in 2006 and 2009 at Burmerange, and only in 2006 at Everlange (P < 0.05). On the contrary, TGW was significantly affected at Burmerange in all years, except 2008 when a positive increase was observed compared to control plots; and in 2006 and 2007 at Everlange. Interestingly, when there was an effect of fungicides on a quality parameter, there was no difference among different fungicide treatments. Thus under conditions prevailing in Luxembourg, a single fungicide treatment applied with judicious timing generally resulted in statistically similar grain quality parameters when compared with a double or triple fungicide treatment.

The effects of bias (over- and underestimates) in estimates of disease severity on hypothesis testing using different assessment methods was explored. Nearest percentage estimates (NPE), the Horsfall–Barratt (H-B) scale, and two linear category scales (10% increments, with and without additional grades at low severity) were compared using simulation modelling to assess effects of bias. Type I and type II error rates were used to compare two treatment differences. The power of the H-B scale and the 10% scale were least for correctly testing a hypothesis compared with the other methods, and the effects of rater bias on type II errors were greater over specific severity ranges. Apart from NPEs, the amended 10% category scale was most often superior to other methods at all severities tested for reducing the risk of type II errors. It should thus be a preferred method for raters who must use a category scale for disease assessments. Rater bias and assessment method had little effect on type I error rates. The power of the hypothesis test using unbiased estimates was most often greater compared with biased estimates, regardless of assessment method. An unanticipated observation was the greater impact of rater bias compared with assessment method on type II errors. Knowledge of the effects of rater bias and scale type on hypothesis testing can be used to improve accuracy and reliability of disease severity estimates, and can provide a logical framework for improving aids to estimate severity visually, including standard area diagrams and rater training software.

Assessment of disease severity is required for several purposes in plant pathology; most often, the estimates are made visually. It is established that visual estimates can be inaccurate and unreliable. The ramifications of biased or imprecise estimates by raters have not been fully explored using empirical data, partly because of the logistical difficulties involved in different raters assessing the same leaves for which actual disease has been measured in a replicated experiment with multiple treatments. In this study, nearest percent estimates (NPEs) of Septoria leaf blotch (SLB) on leaves of winter wheat from nontreated and fungicide-treated plots were assessed in both 2006 and 2007 by four raters and compared with assumed actual values measured using image analysis. Lin’s concordance correlation (LCC, ρc) was used to assess agreement between the two approaches. NPEs were converted to Horsfall-Barratt (HB) midpoints and were compared with actual values. The estimates of SLB severity from fungicide-treated and nontreated plots were analyzed using generalized linear mixed modeling to ascertain effects of rater using both the NPE and HB values. Rater 1 showed good accuracy (ρc = 0.986 to 0.999), while raters 3 and 4 were less accurate (ρc = 0.205 to 0.936). Conversion to the HB scale had little effect on bias but reduced numerically both precision and accuracy for most raters on most assessment dates (precision, r = −0.001 to −0.132; and accuracy, ρc = −0.003 to −0.468). Interrater reliability was also reduced slightly by conversion of estimates to HB midpoint values. Estimates of mean SLB severity were significantly different between image analysis and raters 2, 3, and 4, and there were frequently significant differences among raters (F = 151 to 1,260, P = 0.001 to P < 0.0001). Only on 26 June 2007 did conversion to the HB scale change the means separation ranking of rater estimates. Nonetheless, image analysis and all raters were able to differentiate control and treated-plot treatments (F = 116 to 1,952, P = 0.002 to P < 0.0001, depending on date and rater). Conversion of NPEs to the HB scale tended to reduce F values slightly (2006: NPEs, F = 116 to 276, P = 0.002 to 0.0005; and, for the HB-converted values, F = 101 to 270, P = 0.002 to 0.0005; 2007: NPEs, F = 164 to 1,952, P = 0.001 to P < 0.0001; and, for HB-converted values, F = 126 to 1,633, P = 0.002 to P < 0.0001). The results reaffirm the need for accurate and reliable disease assessment to minimize over- or underestimates compared with actual disease, and the data we present support the view that, where multiple raters are deployed, they should be assigned in a manner to reduce any potential effect of rater differences on the analysis.

We evaluated the cost effectiveness of a decision–support system (DSS) developed for assessing in real time the risk of progression of the main fungal diseases (i.e., Septoria leaf blotch, powdery mildew, leaf rusts and Fusarium head blight) of winter wheat in the Grand-Duchy of Luxembourg (GDL). The study was conducted in replicated field experiments located in four agricultural locations (representative of the main agro-ecological regions of the country) over a 10-year period (2003–2012). Three fungicide spray strategies were compared: a single DSS-based system and two commonly used spray practices in the GDL, a double- (2T) and a triple-spray (3T) treatment; there was also a non-treated control. In years with a high disease pressure, the DSS-based recommendation resulted in protection of the three upper leaves comparable to that achieved with the 2T and 3T treatments, with significant grain yield increases (P > 0.05) compared to the control (a 4 to 42% increase, depending on the site and year). Overall, the financial gain in treated plots compared with the control ranged from 3 to 16% at the study sites. Furthermore, in seasons when dry weather conditions precluded epidemic development, the DSS recommended no fungicide spray, reducing use of fungicide, and thus saving the cost of the product. The gain in yield for the 2T and 3T plots (compared with control) did not necessarily result in a financial gain during the duration of the experiment. This study demonstrates the potential advantages and profitability of using a DSS-based approach for disease management.

The reliability of different models to predict the biochemical methane potential (BMP) of various plant biomasses using a multispecies dataset was compared. The most reliable prediction models of the BMP were those based on the near infrared (NIR) spectrum compared to those based on the chemical composition. The NIR predictions of local (specific regression and non-linear) models were able to estimate quantitatively, rapidly, cheaply and easily the BMP. Such a model could be further used for biomethanation plant management and optimization. The predictions of non-linear models were more reliable compared to those of linear models. The presentation form (green-dried, silage-dried and silage-wet form) of biomasses to the NIR spectrometer did not influence the performances of the NIR prediction models. The accuracy of the BMP method should be improved to enhance further the BMP prediction models.

Background: Thanks to specific adaptations developed over millions of years, the efficiency of lignin, cellulose and hemicellulose decomposition of higher termite symbiotic system exceeds that of many other lignocellulose utilizing environments. Especially, the examination of its symbiotic microbes should reveal interesting carbohydrate-active enzymes, which are of primary interest for the industry. Previous metatranscriptomic reports (high-throughput mRNA sequencing) highlight the high representation and overexpression of cellulose and hemicelluloses degrading genes in the termite hindgut digestomes, indicating the potential of this technology in search for new enzymes. Nevertheless, several factors associated with the material sampling and library preparation steps make the metatranscriptomic studies of termite gut prokaryotic symbionts challenging. Methods: In this study, we first examined the influence of the sampling strategy, including the whole termite gut and luminal fluid, on the diversity and the metatranscriptomic profiles of the higher termite gut symbiotic bacteria. Secondly, we evaluated different commercially available kits combined in two library preparative pipelines for the best bacterial mRNA enrichment strategy. Results: We showed that the sampling strategy did not significantly impact the generated results, both in terms of the representation of the microbes and their transcriptomic profiles. Nevertheless collecting luminal fluid reduces the co-amplification of unwanted RNA species of host origin. Furthermore, for the four studied higher termite species, the library preparative pipeline employing Ribo-Zero Gold rRNA Removal Kit "Epidemiology" in combination with Poly(A) Purist MAG kit resulted in a more efficient rRNA and poly-A-mRNAdepletion (up to 98.44% rRNA removed) than the pipeline utilizing MICROBExpress and MICROBEnrich kits. High correlation of both Ribo-Zero and MICROBExpresse depleted gene expression profiles with total non-depleted RNA-seq data has been shown for all studied samples, indicating no systematic skewing of the studied pipelines. Conclusions: We have extensively evaluated the impact of the sampling strategy and library preparation steps on the metatranscriptomic profiles of the higher termite gut symbiotic bacteria. The presented methodological approach has great potential to enhance metatranscriptomic studies of the higher termite intestinal flora and to unravel novel carbohydrate-active enzymes., SCOPUS: ar.j, info:eu-repo/semantics/published

Background Recent advances in high-throughput sequencing allow for much deeper exploitation of natural and engineered microbial communities, and to unravel so-called “microbial dark matter” (microbes that until now have evaded cultivation). Metagenomic analyses result in a large number of genomic fragments (contigs) that need to be grouped (binned) in order to reconstruct draft microbial genomes. While several contig binning algorithms have been developed in the past 2 years, they often lack consensus. Furthermore, these software tools typically lack a provision for the visualization of data and bin characteristics. Results We present ICoVeR, the Interactive Contig-bin Verification and Refinement tool, which allows the visualization of genome bins. More specifically, ICoVeR allows curation of bin assignments based on multiple binning algorithms. Its visualization window is composed of two connected and interactive main views, including a parallel coordinates view and a dimensionality reduction plot. To demonstrate ICoVeR’s utility, we used it to refine disparate genome bins automatically generated using MetaBAT, CONCOCT and MyCC for an anaerobic digestion metagenomic (AD microbiome) dataset. Out of 31 refined genome bins, 23 were characterized with higher completeness and lower contamination in comparison to their respective, automatically generated, genome bins. Additionally, to benchmark ICoVeR against a previously validated dataset, we used Sharon’s dataset representing an infant gut metagenome. Conclusions ICoVeR is an open source software package that allows curation of disparate genome bins generated with automatic binning algorithms. It is freely available under the GPLv3 license at https://git.list.lu/eScience/ICoVeR. The data management and analytical functions of ICoVeR are implemented in R, therefore the software can be easily installed on any system for which R is available. Installation and usage guide together with the example files ready to be visualized are also provided via the project wiki. ICoVeR running instance preloaded with AD microbiome and Sharon’s datasets can be accessed via the website. Electronic supplementary material The online version of this article (doi:10.1186/s12859-017-1653-5) contains supplementary material, which is available to authorized users.

The biomethane yield of various energy crops, selected among potential alternatives to maize in the Greater Region, was assessed. The biomass yield, the volatile solids (VS) content and the biochemical methane potential (BMP) were measured to calculate the biomethane yield per hectare of all plant species. For all species, the dry matter biomass yield and the VS content were the main factors that influence, respectively, the biomethane yield and the BMP. Both values were predicted with good accuracy by linear regressions using the biomass yield and the VS as independent variable. The perennial crop miscanthus appeared to be the most promising alternative to maize when harvested as green matter in autumn and ensiled. Miscanthus reached a biomethane yield of 5.5 ± 1 × 10(3)m(3)ha(-1) during the second year after the establishment, as compared to 5.3 ± 1 × 10(3)m(3)ha(-1) for maize under similar crop conditions.

Methods to estimate disease severity vary in accuracy, reliability, ease of use and cost. Severity of septoria leaf blotch (SLB, caused by Zymoseptoria tritici) was estimated by four raters and by image analysis (assumed actual values) on individual leaves of winter wheat in order to explore accuracy and reliability of estimates, and to ascertain whether there were any general characteristics of error. Specifically, the study determined: (i) the accuracy and reliability of visual assessments of SLB over the full range of severity from 0 to 100%; (ii) whether certain 10% ranges in actual disease severity between 0 and 100% were more prone to estimation error compared with others; and (iii) whether leaf position affected accuracy within those ranges. Lin's concordance correlation analysis of all severities (0-100%) demonstrated that all raters had estimates close to the actual values (agreement: ρc = 0·92-0·99). However, agreement between actual SLB severities and estimates by raters was less good when compared over short 10% subdivisions within the 0-100% range (ρc = -0·12 to 0·99). Despite common rater imprecision at estimating low and high SLB severities, individual raters differed considerably in their accuracy over the short 10% subdivisions. There was no effect of leaf position on accuracy or precision of severity estimate on separate leaves (L1-L3). Pursuing efforts in understanding error in disease estimation should aid in improving the accuracy of assessments, making visual estimates of disease severity more useful for research and applied purposes.

A large set of maize silage samples was produced to assess the major traits influencing the biomethane production of this crop. The biomass yield, the volatile solids contents and the biochemical methane potential (BMP) were measured to calculate the biomethane yield per hectare (average=7266m(3)ha(-1)). The most influential factor controlling the biomethane yield was the cropping environment. The biomass yield had more impact than the anaerobic digestibility. Nevertheless, the anaerobic digestibility of maize silages was negatively affected by high VS content in mature maize. Late maturing maize varieties produced high biomass yield with high digestibility resulting in high biomethane yield per hectare. The BMP was predicted with good accuracy using solely the VS content.

A decision support system (DSS) involving an approach for predicting wheat leaf rust (WLR) infection and progress based on night weather variables (i.e., air temperature, relative humidity, and rainfall) and a mechanistic model for leaf emergence and development simulation (i.e., PROCULTURE) was tested in order to schedule fungicide time spray for controlling leaf rust progress in wheat fields. Experiments including a single fungicide treatment based upon the DSS along with double and triple treatment were carried out over the 2007–2009 cropping seasons in four representative Luxembourgish wheat field locations. The study showed that the WLR occurrences and severities differed according to the site, cultivar, and year. We also found out that the single fungicide treatment based on the DSS allowed a good protection of the three upper leaves of susceptible cultivars in fields with predominant WLR occurrences. The harvested grain yield was not significantly different from that of the double and triple fungicide-treated plots (P < 0.05). Such results could serve as basis or be coupled to cost-effective and environmentally friendly crop management systems in operational context.

An empirical approach for simulating the infection and progress of leaf rust (caused by Puccinia triticina) during stem elongation on winter wheat was analysed for the 2000 to 2006 growing seasons. The approach was elaborated based on night weather conditions (i.e., air temperature, relative humidity and rainfall) and leaf rust occurrences. Data from three consecutive cropping seasons (2000-2002) at four representative sites of the Grand-Duchy of Luxembourg were used in the set-up phase. The capability to correctly simulate the occurrence expression of P. triticina infections on the upper leaf layers was then assessed over the 2003-2006 period. Our study revealed that the development of leaf rust required a period of at least 12 consecutive hours with air temperatures ranging between 8 and 16 °C, a relative humidity greater than 60 % (optimal values being 12-16 °C and up to 80 % for air temperatures and relative humidity, respectively) and rainfall less than 1 mm. Moreover, leaf rust occurrences and infections were satisfactorily simulated. The false alarm ratio was ranged from 0.06 to 0.20 in all the study sites. The probability of detection and critical success index for WLR infection were also close to 1 (perfect score).

Field experiments were conducted over four growing seasons to assess the relationship between fungal disease attacks and wheat grain yield in the agrometeorological part of the Belgian crop yield forecasting system (i.e., Belgian crop growth monitoring system, B-CGMS) by recalibrating the lifespan of wheat leaves (SPAN parameter). The results from 133 experimental fields in Belgium (the Wallonia region) and the Grand-Duchy of Luxembourg were analyzed in terms of site factors such as cultivar, previous crop, sowing date and geographical area. A significant relationship was found between green leaf area (GLA) duration expressing the infection pressure (parameter m; using a modified Gompertz model and calculated from observed GLA) and final grain yield (R2=0.79; P

A mechanistic model, PROCULTURE, for assessing the development of each of the last five leaf layers and the progress of Septoria leaf blotch, caused by Septoria tritici (teleomorph Mycosphaerella graminicola), has been applied on susceptible and weakly susceptible winter wheat (Triticum aestivum) cultivars in two locations (Everlange and Reuland) in Luxembourg over a 3-year period (2000 to 2002). A double performance assessment of PROCULTURE was conducted in this study. First, the capability of PROCULTURE to correctly simulate S. tritici incidence was checked. Second, the model's ability to accurately estimate disease severity was assessed on the basis of the difference between simulated and observed levels of disease development at each leaf layer. The model accurately predicted disease occurrence in the 2000 and 2002 seasons, on susceptible and semi-susceptible cultivars, with a probability of detection (POD) exceeding 0.90. However, in 2001, even though the POD never fell below 0.90, the false alarm ratio (FAR) was too high to consider the simulations satisfactory. Concerning the evaluation of disease severity modeling, statistical tests revealed accurate simulations performed by PROCULTURE for susceptible cultivars in 2000 and 2002. By contrast, for weakly susceptible cultivars, the model overestimated disease severity, especially for the upper leaves, for the same period.

The adoption of non-inversion tillage practices has been widely promoted due to their potential benefits in reducing energy consumption and greenhouse emissions as well as improving soil fertility. However, the lack of soil inversion usually increases weed infestations and changes the composition of the weed community. Weed management is still a main drawback for the wider adoption of reduced tillage practices. However, it is not entirely clear whether these changes in weed communities are a consequence of non-random filters on the functional attributes of weed species and may thus affect the potential weed-crop competition relationship. Here, we analyse the changes in weed diversity, community composition, and the functional attributes of weed communities under reduced (non-inversion) and conventional (inversion) tillage. We discuss their potential effects on the competitiveness against crop production using data from two crops of seven on-going organic and low-input field trials in different climatic regions across Europe. Weeds were evaluated after post-emergence weed control methods. We used the community weighted mean values of the life form (annuals versus perennials), specific leaf area, seed weight, canopy height, seed bank longevity, soil nutrient conditions affinity, beginning of flowering and flowering span. Moreover, the effect of the crop type on the functional attributes was also evaluated. Overall, the tillage system affected the composition and functional attributes of the weed communities. Weed community changes may imply a reduction in weed-crop competition under both tillage systems. For instance, weed communities under reduced tillage were potentially less competitive because they were shorter and had less affinity to nutrients. On the other hand, weed communities under conventional tillage had potentially less seed production and a lower abundance of perennial species. Our study thus supports tillage as an important driver of the functional attributes of weed communities, but both tillage systems can have their downside. However, the crop type was overall more relevant than the tillage in determining most of the trait values of the weed communities.