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239 results for “cereals”
Variant, Metabolite and Source Data for: Population genomics uncover loci for trait improvement in the indigenous African cereal tef (Eragrostis tef)
<p>These files contain the variant and metabolome for a collection of 220 tef (<em>Eragrsotis tef)</em> accessions from an ethiopian diversity panel. The accessions were assembled and managed by the Ethiopian Institute of Agricultural Research (EIAR, Ethiopia). The variant data was produced at the John Innes Centre (UK). The metabolome data was produced at Aberystwyth University (UK). These dataset are described in Jones et al. (2024), <em>bioRxiv</em>, https://doi.org/10.1101/2024.09.30.615331. The source data for main figures in the publication are also included.</p> <p>The submission contains</p> <ol> <li>EIAR_filtered.vcf.gz: This is the variant data obtained from alignment of Illumina reads from all 220 teff accessions to the reference assembly of tef (Dabbi). Low quality variants were filtered out. This variant data was used for constructing the phylogenetic relationship between the accessions. The samples names corresponds to the DNA code in Supplementary Table S10 (Jones et al, 2024).</li> <li>pooled_EIAR_filtered.vcf.gz: After the phylogentic analysis described above, reads from accessions that were found to be genetically redundant were pooled before variant calling. This file was used for the SNP GWAS analysis. The samples names corresponds to the DNA code in Supplementary Table S10 (Jones et al, 2024).</li> <li> Metabolite_Profile.xlxs (source data for Figure 5): This file contains m/z feature intensities from untargeted metabolite fingerprinting using Flow Infusion Electrospray High-resolution Mass Spectrometry (FIE-HRMS). The sample names contains a combination of Location code and Plot number in Supplementary Table S10 e.g AT plot 1, CD plot 1, DZ plot 1, where AT, CD and DZ represent Alem Tena, Chefe Donsa and Debre Zeit, respectively. The data was used for the partial least squares discriminant analysis and differentially accumulated metabolites analysis presented in Figure 5.</li> <li>Source data: Numerical source data for graphs and charts in Figures 3 - 7.</li> <li>Tsedey TT2 Sequence from Improved Assembly: The 4A and 4B sequences around the TT2 orthologue in tef from the improved PacBio-based chromosome-scale assembly of tef. These sequences were used for plotting the LTR Copia alignments presented in Supplementary Figure 9. We thank Corteva for pre-publication access to this improved Tsedey genome assembly.</li> </ol>
A global dataset gathering 37 field experiments involving cereal-legume intercrops and their corresponding sole crops.
<p>The overall description of the dataset is reported in the <strong>data_report.pdf</strong> file. The methodology for data curation and tidying is published in Peer Community Journal (<a href="https://doi.org/10.24072/pcjournal.389">Mahmoud2024</a>).</p> <p>This dataset gathers the results of 37 field experiments, which involved cereal-legume intercrops and their corresponding sole crops. The field experiments were carried in 5 European countries (France, Denmark, Italy, Germany and England) from 2001 to 2017. The dataset includes:</p> <ul> <li>5 legume species , <em>i.e.</em> chickpea (<em>Cicer arietinum</em> L.), faba bean (<em>Vicia faba</em> L.), lentil (<em>Lens culinaris</em> Med.), lupin (<em>Lupinus albus</em> L.) and pea (<em>Pisum sativum</em> L.),</li> <li>3 cereal species, <em>i.e.</em> barley (<em>Hordeum vulgare</em> L.), durum wheat (<em>Triticum turgidum</em> L.) and soft wheat (<em>Triticum aestivum</em> L.), </li> <li>8 resulting intercrops, <em>i.e.</em> i) barley associated with faba bean, lupin or pea, ii) durum wheat associated with chickpea, faba bean or pea, and iii) soft wheat associated with lentil or pea. </li> </ul> <p>In total, the dataset contains 299 sole crop and 308 intercrop experimental units, one given experimental unit being defined as the unique combination of {site, year, crop management}, with the crop management including species and cultivar choice as well as agricultural interventions (sowing conditions, inputs).</p> <p>The global dataset includes four tables, all sharing a common identifier (experiment_id):</p> <ul> <li>data_trials.csv: the global features describing the experimental sites,</li> <li>data_management.csv: the agricultural management actions carried out on each of the experimental sites,</li> <li>data_traits.csv: measured plant and crop characteristics,</li> <li>data_climate.csv: climate for the experimental sites, retrieved from NASA POWER API.</li> </ul> <p>Additionally, a metadata file is provided (<strong>metadata.xlsx</strong>), describing the table to which the variables belong (variable_type, i.e. trials, management, traits or climate), their name (variable_name), their significance (description) and their unit (unit). Finally, a table including the original references related to experimental files gathered (<strong>references.xlsx</strong>) is also provided.</p> <p>Data providers and field experiments: Laurent Bedoussac, Eric Justes, Etienne-Pascal Jour- net, Christophe Naudin, Henrik Hauggaard-Nielsen, Erik Steen Jensen, Elise Pelzer, Guénaëlle Corre-Hellou, Bochra Kammoun, Loic Viguier, Romain Barillot, Antoine Couëdel, Philippe Hinsinger</p> <p>Database and management: Noémie Gaudio, Rémi Mahmoud, Pierre Casadebaig</p>
Density independent prey choice, taxonomy, life history and web characteristics determine the diet and biocontrol potential of spiders (Linyphiidae and Lycosidae) in cereal crops - Dataset
<p>Materials and Methods</p> <p>Fieldwork</p> <p>Money spiders (Araneae: Linyphiidae) and wolf spiders (Araneae: Lycosidae) were the two most common families present in these field surveys, so were prioritised for collection. Spiders were visually located along transects in two adjacent barley fields at Burdons Farm, Wenvoe in South Wales (51°26'24.8"N, 3°16'17.9"W) and collected from occupied webs and the ground, between April and September 2018. Surveys and sampling were conducted five days per week across this period. Each transect was adjacent to a randomly selected tramline and they were distributed across the entire field. The areas searched were 4 m<sup>2</sup> quadrats at least 10 m apart and all observed linyphiids and lycosids were collected in approximately 15-minute searches. The spiders included in this study were taken from 64 locations across 24 days (Supplementary Table 3) along the aforementioned transects. Spiders were individually placed into 1.5 ml microcentrifuge tubes containing 100 % ethanol using an aspirator, regularly changing meshing, at least every five spiders, to limit potential cross-contamination between spiders (spiders were also subsequently washed during transferral to fresh ethanol at the identification and, separately, dissection stages). Linyphiids occupying webs were prioritised for collection, but ground-active linyphiid spiders were also collected. For each spider taken from a web, the height of the web from the ground and its approximate dimensions were recorded, the latter calculated as approximate web area. Spiders were taken to Cardiff University, transferred to fresh ethanol, adults identified to species-level and juveniles to genus, and stored at -80 °C in 100 % ethanol until subsequent DNA extraction. To obtain data on local prey density, 4 m<sup>2</sup> of ground and crop stems were suction sampled using a ‘G-vac’ for 30 seconds at each quadrat from which spiders were collected, with the collected material emptied into a bag, any organisms immediately killed with ethyl-acetate and material frozen for storage before sorting into 70 % ethanol in the lab.</p> <p>All invertebrates were identified to family level due to the restriction of many of the metabarcoding-derived dietary data to this level, and the difficulty associated with finer taxonomic resolution of many taxa. Exceptions included springtails of the superfamily Sminthuroidea (Sminthuridae and Bourletiellidae, which were often indistinguishable following suction sampling and preservation due to the fine features necessary to distinguish them) which were left at super-family, mites (many of which were immature or in poor condition) which were identified to order level and wasps of the superfamily Ichneumonoidea (which were identified no further due to obscurity of wing venation due to damage).</p> <p> </p> <p>Extraction and high-throughput sequencing of spider gut DNA</p> <p>Given their prevalence in field collections, dietary analysis was carried out for the linyphiid genera <em>Erigone</em>, <em>Tenuiphantes</em>, <em>Bathyphantes</em> and <em>Microlinyphia </em>(Araneae: Linyphiidae), and the Lycosidae genus <em>Pardosa</em>. Spiders were transferred to and washed in fresh 100 % ethanol to reduce external contaminants prior to identification via morphological key <sup>1</sup>. Abdomens were removed from spiders and again washed in and transferred to fresh 100 % ethanol. DNA was extracted from the abdomens via Qiagen TissueLyser II and DNeasy Blood & Tissue Kit (Qiagen) as per the manufacturer protocol, but with an extended lysis time of 12 hours to account for the complex and branched gut system in spider abdomens <sup>2</sup>. At least one extraction negative (blank tubes treated identically to samples) was included per 12 spiders (each extraction typically contained 24 spiders, thus two extraction negatives), which was included in subsequent PCR and high-throughput sequencing to detect instances of lab/reagent contamination.</p> <p>For amplification of DNA, two primer pairs were used. BerenF-LuthienR <sup>3</sup> amplified a broad range of invertebrates including spiders, and TelperionF-LaureR, amplified a range of invertebrates but fewer spiders (modified from TelperionF-LaurelinR <sup>3</sup> via one base-pair change from Laurelin; 5’-ggrtawacwgttcawccagt-3’). Primers were labelled with unique 10 bp molecular identifier tags (MID-tags) so that each individual had a unique pairing of forward and reverse tags for identification of each spider post-sequencing. PCR reactions of 25 µl contained 12.5 µl Qiagen PCR Multiplex kit, 0.2 µmol (2.5 µl of 2 µM) of each primer and 5 µl template DNA. Reactions were carried out in the same thermocycler, optimised via temperature gradient, with an initial 15 minutes at 95 °C, 35 cycles of 95 °C for 30 seconds, the primer-specific annealing temperature for 90 seconds and 72 °C for 90 seconds, respectively, followed by a final extension at 72 °C for 10 minutes. BerenF-LuthienR and TelperionF-LaureR used annealing temperatures of 52 °C and 42 °C, respectively.</p> <p>Within each PCR 96-well plate, 12 negative controls (extraction and PCR), 2 blank controls and 2 positive controls were included (i.e. 80 samples per plate), based on Taberlet <em>et al. </em>(2018). Positive controls were mixtures of invertebrate DNA comprised of non-native Asiatic species in four different proportions (Supplementary Table 1) and blanks were empty wells within each plate to identify tag-jumping into unused MID-tag combinations. PCR negative controls were DNase-free water treated identically to DNA samples. A negative control was present for each MID-tag to identify any contamination of primers. All PCR products were visualised in a 2 % agarose gel with SYBRSafe (Thermo Fisher Scientific, Paisley, UK) and placed in categories based on their relative brightness. The concentration of these brightness categories was quantified via Qubit dsDNA High-sensitivity Assay Kits (Thermo Fisher Scientific, Waltham, MA, USA) with at least three representatives of each category per plate. The PCR products were then proportionally pooled according to these concentrations. Each pool was cleaned via SPRIselect beads (Beckman Coulter, Brea, USA), with a left-side size selection using a 1:1 ratio (retaining ~300-1000 bp fragments). The concentration of the pooled DNA was then determined via Qubit dsDNA High-sensitivity Assay Kits and pooled together into one library per primer pair. Library preparation for Illumina sequencing was carried out on the cleaned libraries via NEXTflex Rapid DNA-Seq Kit (Bioo Scientific, Austin, USA) and samples were sequenced on an Illumina MiSeq via a V3 chip with 300-bp paired-end reads (expected capacity ≤25,000,000 reads). Bioinformatic analysis followed (Drake et al., 2021; Supplementary Information 1).</p> <p> </p> <p>Statistical analysis</p> <p>All analyses were conducted in R v4.0.0 <sup>6</sup>. Initial multivariate analyses used binary data (i.e., presence/absence) given the various problems inherent to quantifying metabarcoding data <sup>7,8</sup>. Prey species that occurred only once across all of the dietary samples were removed before further analyses to prevent outliers skewing the results, which is particularly problematic for non-metric multidimensional scaling. Spider diets were compared between variables using multivariate generalized linear models (MGLMs) via ‘manyglm’ in the ‘mvabund’ package <sup>9</sup> with a binomial error family and Monte Carlo resampling. Model independent variables included spider genus, spider life stage (juvenile or adult, the latter defined by fully developed genitalia), spider sex and all two-way interactions between these variables. Pairwise two-way interactions were also included between the aforementioned variables and Julian day to account for how seasonality may affect these relationships.</p> <p>Coarse dietary differences were visualised by non-metric multidimensional scaling (NMDS) via metaMDS in the ‘vegan’ package <sup>10</sup> with Jaccard distance in two dimensions and 999 tries. For NMDS, outliers (usually samples containing rare taxa) were identified by plotting and subsequently removed to facilitate separation of samples and achieve minimum stress. For visualisation of the effect of categorical variables against the dietary NMDS, spider plots were created using ‘ordispider’ with ‘ggplot’ and the ‘RColorBrewer’ ‘Accent’ colour palette <sup>11</sup>. Spider diet was compared against web characteristics for spiders for which both data were available using the MGLM process outlined above, but with starting models containing web height, web area, an interaction between the two, and pairwise interactions between genus, life stage and sex with the two web variables. This model used the same binomial error family as above, but with a ‘cloglog’ link function. For visualisation of the effect of continuous variables against the NMDS, surf plots were created with scaled coloured contours using the function “ordisurf” of the “ggplot” package in R.</p> <p>All prey taxa were classified as agricultural pests, natural enemies or excluded from subsequent analyses of intraguild predation and biocontrol (Supplementary Table 2). Intraguild predation and biocontrol variables were created by counting the number of natural enemy taxa, and, separately, of agriculturally relevant “pest” taxa (taxa containing species that commonly adversely affect agricultural productivity; Supplementary Table 2) in each spider’s diet. These resultant count data (effectively the diversity of pests and natural enemies predated by each individual spider) were separately analysed against spider genus, life stage and sex via GLM. “Site” (denoting the 4 m<sup>2</sup> area from which spiders were collected within fields) was initially included as a random effect in generalized linear mixed-models, but no significant effect was observed when comparing this model against a standard GLM via a likelihood ratio test of nested models using the ‘lrtest’ command in the ‘lmtest’ package <sup>12</sup>. Standard GLMs were thus used to avoid issues relating to singularity in the mixed models. The assumptions for the resultant Poisson error family GLMs were tested using the “testResiduals” function of the ‘DHARMa’ package <sup>13</sup>. Intraguild predation and biocontrol differences between significant terms were visualised using violin plots with the quartiles, median and 95 % upper limit annotated using the ‘geom_violin’ function in ‘ggplot2’.</p> <p><em>In situ</em> spider prey choice was analysed using network-based null models in the ‘econullnetr’ package <sup>14</sup> with the ‘generate_null_net’ command, visually represented with the ‘plot_preferences’ command. Binary dietary data were used alongside suction sample count data to represent prey availability. These suction sample data, as described above, were collected at the same sites as the spiders three days after spider collection. Prior to the taxonomic prey choice analysis, an hemipteran identified no further than order level through dietary analysis was removed due to the inability to pair it to any present prey taxa with certainty. Standardised effect sizes (SES) were extracted for all comparisons for each individual spider and compared between genera, life stages and sexes using permutational multivariate analysis of variance (PerMANOVA) using the ‘adonis’ function of the ’vegan’ package with 9999 permutations and a Euclidean distance matrix to determine overall differences in prey choice.</p> <p> </p> <p>References</p> <p>1. Roberts, M. J. <em>The Spiders of Great Britain and Ireland (Compact Edition)</em>. (Harley Books, 1993).</p> <p>2. Krehenwinkel, H., Kennedy, S., Pekár, S. & Gillespie, R. G. A cost-efficient and simple protocol to enrich prey DNA from extractions of predatory arthropods for large-scale gut content analysis by Illumina sequencing. <em>Methods Ecol. Evol.</em> <strong>8</strong>, 126–134 (2017).</p> <p>3. Cuff, J. P. <em>et al.</em> Money spider dietary choice in pre- and post-harvest cereal crops using metabarcoding. <em>Ecol. Entomol.</em> <strong>46</strong>, 249–261 (2021).</p> <p>4. Taberlet, P., Bonin, A., Zinger, L. & Coissac, E. <em>Environmental DNA</em>. (Oxford University Press, 2018).</p> <p>5. Drake, L. E. <em>et al.</em> An assessment of minimum sequence copy thresholds for identifying and reducing the prevalence of artefacts in dietary metabarcoding data. <em>Methods Ecol. Evol.</em> <strong>in press</strong>, (2021).</p> <p>6. R Core Team. R: A language and environment for statistical computing. (2020).</p> <p>7. Deagle, B. E., Thomas, A. C., Shaffer, A. K. & Trites, A. W. Quantifying sequence proportions in a DNA-based diet study using Ion Torrent amplicon sequencing: which counts count? <em>Mol. Ecol. Resour.</em> <strong>13</strong>, 620–633 (2013).</p> <p>8. Deagle, B. E. <em>et al.</em> Counting with DNA in metabarcoding studies: How should we convert sequence reads to dietary data? <em>Mol. Ecol.</em> <strong>28</strong>, 391–406 (2019).</p> <p>9. Wang, Y., Naumann, U., Wright, S. T. & Warton, D. I. mvabund – an R package for model-based analysis of multivariate abundance data. <em>Methods Ecol. Evol.</em> <strong>3</strong>, 471–474 (2012).</p> <p>10. Oksanen, J. <em>et al.</em> vegan: Community Ecology Package. (2016).</p> <p>11. Neuwirth, E. RColorBrewer: ColorBrewer palettes. (2014).</p> <p>12. Zeileis, A. & Hothorn, T. Diagnostic checking in regression relationships. <em>R News</em> <strong>2</strong>, 7–10 (2002).</p> <p>13. Hartig, F. DHARMa: residual diagnostics for hierarchical (multi-level/mixed) regression models. (2020).</p> <p>14. Vaughan, I. P. <em>et al.</em> econullnetr: an r package using null models to analyse the structure of ecological networks and identify resource selection. <em>Methods Ecol. Evol.</em> <strong>9</strong>, 728–733 (2018).</p>
Data from: Behavioural responses to potential dispersal cues in two economically important cereal-feeding eriophyoid mite species
<p>Variables:</p> <ol> <li>species (ABH = <em>Abacarus hystrix</em>, WCM = <em>Aceria tosichella</em> MT1 genetic lineage)</li> <li>variant - experimental treatment (type of dispersal cue): wind, an insect vector, presence of a fresh plant</li> <li>feeding - no. of feeding specimens</li> <li>walking - no. of walking specimens</li> <li>standing - no. of specimens standing vertically</li> <li>cha - no. of specimens forming chains</li> <li>mob - no. of specimens capable to move</li> <li>pop - no. of all specimens (including quiescent stages)</li> </ol>
Cereal aphids monitored in 2,110 fields in Denmark 2002-2019
<p>The aphids were accounted for visually as the percentage of tillers infested. The type of crop (winter wheat or spring barley) and its growth stage at the time of aphid assessment are included in the data set.</p> <p>The data is provided as both a tab-separated text file and a binary R data file. The R files provides code to read and plot the data. The two plots produced are also provided as PNG files.</p> <p>The data were collected by SEGES Innovation, Denmark.</p>
Dataset for the article "Barriers and Opportunities for Sustainable Farming Practices and Crop Diversification Strategies in Mediterranean Cereal-Based Systems"
<p>Datasets from the surveys applied for the article "Barriers and Opportunities for Sustainable Farming Practices and Crop Diversification Strategies in Mediterranean Cereal-Based Systems" <a href="https://doi.org/10.3389/fenvs.2022.861225">https://doi.org/10.3389/fenvs.2022.861225</a></p>
Video Series: Integrated Pest Management focusing on disease control in cereals
<p>Welcome to this video series on IPM, focusing on disease control in cereals. </p> <p>The challenge of IPM is to make the control methods we use appropriate to the circumstances, and to balance between the productivity of the crop and minimising the impact on the environment. The control decisions we make on one field or in one season may not be appropriate in another set of circumstances – there is no ‘blue print’. In these videos we delve into the physiology of the crop, the epidemiology of the diseases and how different control methods work. By understanding the biology of the systems we're trying to control, we're better equipped to make appropriate decisions. Going into depth means we can’t cover all aspects of IPM. In practice, decisions about disease control are being made alongside decisions about invertebrate pests and weeds, and in the wider context of integrated crop management. In the UK, information on those topics is available from organisations such as LEAF, AHDB and the Voluntary Initiative. Nevertheless, disease control is still a big topic, so we have broken it down into bite size chunks - although each video is still a pretty substantial bite and will need some digesting.</p> <p>The videos can be viewed in any order that interests you, but they'll make most logical sense viewed in the order in the menu.</p> <p>Links to the videos can be found in the summary document. </p> <p>PDF versions of the video presentations are provided. </p>
Confocal stacks from archaeological tools used for harvesting and threshing cereals
<p>Series of confocal stacks obtained with SensoScan, using a S Neox 3D profilometer from Sensofar. Measured surfaces from a series of archaeological tools.</p>
Early-season biomass and weather enable robust cereal rye cover crop biomass predictions
<p>Farmers need accurate estimates of winter cover crop biomass to make informed decisions on termination timing or to estimate potential release of nitrogen from cover crop residues to subsequent cash crops. Utilizing data from an extensive experiment across 11 states from 2016 to 2020, this study explores the most reliable predictors for determining cereal rye cover crop biomass at the time of termination. Our findings demonstrate a strong relationship between early-season and late-season cover crop biomass. Employing a random forest model, we predicted late-season cereal rye biomass with a margin of error of approximately 1,000 kg ha<sup>-1</sup> based on early-season biomass, growing degree days, cereal rye planting and termination dates, photosynthetically active radiation, precipitation, and site coordinates as predictors. Our results suggest that similar modeling approaches could be combined with remotely sensed early-season biomass estimations to improve the accuracy of predicting winter cover crop biomass at termination for decision support tools.</p>
U.S. cereal rye winter cover crop growth database
<p>Winter cover crop performance metrics (i.e., vegetative biomass quantity and quality) affect ecosystem services provisions but vary widely due to differences in agronomic practices, soil properties, and climate. Cereal rye (Secale cereale) is the most common winter cover crop in the United States due to its winter hardiness, low seed cost, and high biomass production. We compiled data on cereal rye winter cover crop performance metrics, agronomic practices, and soil properties across the eastern half of the United States. The dataset includes a total of 5,695 cereal rye biomass observations across 208 site-years between 2001–2022 and encompasses a wide range of agronomic, soil, and climate conditions. Cereal rye biomass values had a mean of 3,428 kg ha−1, a median of 2,458 kg ha−1, and a standard deviation of 3,163 kg ha−1. The data can be used for empirical analyses, to calibrate, validate, and evaluate process-based models, and to develop decision support tools for management and policy decisions.</p>
Data for : Effects of electrokinetic and ultrasonication pre-treatment and two-step anaerobic digestion of biowastes on the nitrogen fertiliser value by injection or surface banding to cereal crops
<p>Data file for article: Effects of electrokinetic and ultrasonication pre-treatment and two-step anaerobic digestion of biowastes on the nitrogen fertiliser value by injection or surface banding to cereal crops (https://doi.org/10.1016/j.jenvman.2022.116699).</p>
Supplementary material for: "Seeds adapted to mixed cropping increase yield and drought resistance of cereal-legume mixtures"
<p>Supplementary material for the research article: "Seeds adapted to mixed cropping increase yield and drought resistance of cereal-legume mixtures".</p> <ul> <li>Raw data</li> <li>R analysis code</li> <li>Statistical analysis info: ANOVA and Tukey comparisons tables</li> </ul> <p> </p> <p>Abstract:</p> <p>Cropland diversification through mixed cropping has the potential of achieving a more sustainable agriculture while securing food production. This is of special relevance with climate change and the expected drier growing conditions in the future. Seed adaptation to this cropping method is hypothesised to be a fundamental factor to maximise these benefits, as well as the particular species combined. In this study we compared the performance of four cereal-legume mixed crops (wheat and oat mixed with lupin and lentil in pairs) with their respective monocrops. Each crop was sown using seeds adapted to monoculture and mixed cropping, respectively. Moreover, they were grown under early-season and late-season drought treatments and under control conditions. We measured above-ground vegetative biomass, seed yield and harvest index to evaluate crop production, drought resistance and the effect of seed adaptation on each mixed and monocrop. Our results show that mixed cropping either had a beneficial or neutral effect on crop yield, depending on the species combination and drought conditions, but harvest index was generally higher in monocrops. We also confirmed that seed adaptation to a particular type of cropping is clearly a determining factor in its performance. In accordance with the insurance hypothesis, mixed cropping has the effect of protecting crop yields in the case of a sudden bad performance of one of the species, for example, caused by adverse environmental conditions. It is necessary to focus on effective species combinations which have the best responses to mixed cropping. We show for the first time that wheat-lentil mixtures performed poorly, while wheat-lupin showed the most promising results improving yield and drought resistance. Oat mixed crops did not show differences with the respective monocrops, so they can be a viable cropping option as well and benefit from advantages of crop diversity not measured in this study.</p>
Рис. 1. ГнезΔовые биотопы чернобровой камышевки: a — опушка Λеса со спирейно-папоротниковым поΔΛеском на р. Самарга; b — спирейно-зΛакововая опушка в б. Сизиман Fig. 1. Breeding biotopes of the black-browed warbler: a — forest edge with spireal-fern undergrowth on the Samarga river; b — spirey-cereal edge in the Siziman Bay in in the Ussuri region
Рис. 1. ГнезΔовые биотопы чернобровой камышевки: a — опушка Λеса со спирейно-папоротниковым поΔΛеском на р. Самарга; b — спирейно-зΛакововая опушка в б. Сизиман Fig. 1. Breeding biotopes of the black-browed warbler: a — forest edge with spireal-fern undergrowth on the Samarga river; b — spirey-cereal edge in the Siziman Bay
Fig. 1 in Diversity of carabids (Coleoptera: Carabidae) within two Dutch cereal fields and their boundaries
Fig. 1. Positions of sampling locations (squares) within two fields of winter wheat, Droevendaal farm, Wageningen. A – field 1; B – field 2. dd – dry ditch; gr – 2 m high grassy ridge; gs – grassy strip; h – hedge; r – tarred road; t – paved track; tr – tree row; wd – water ditch; ws – weedy strip.
Figure 6 in Morphometric and genetic variability among Mediterranean cereal cyst nematode (Heterodera latipons) populations in Turkey
Figure 6. Phylogenetic tree (maximum likelihood) constructed through the ITS sequence alignment from 42 populations of Heterodera latipons. Bootstrap values (more than 60%) are given for the appropriate clades. Populations are designated with the code described in Table 1.
Cereal yield response to climate change in key grain producing areas of the Northern Hemisphere
<p>Vulnerability mapping is a field of increasing importance as we search for an ecologically and economically sustainable<br> climate-smart land-use system, which is necessary for the development of green infrastructure and agricultuVulnerability<br> mapping is a growing field with increasing importance in developing an ecologically and economically sustainable climate-smart land-use system, which may serve the advancement of green infrastructure and agricultural production. The<br> transformation of the climatic regime has an undeniable impact on plant production, but we rarely have long enough data<br> series to scrutinize the unfolding effects of the microregional patterns of the global climate-cereal yield relationship. To fill this<br> gap, I will analyze three regions of the Northern Hemisphere under continental climate, which represent key landscapes<br> concerning global food production and security, contributing a multi-way knowledge transfer through training, teaching, and<br> different form of communication activities. The relationship between climate indices, maize, and wheat yields will be analyzed<br> applying high geographical resolution data from Northern China, Hungary, and northern states of the US Midwest at a<br> landscape scale by Köppen-Geiger zones using linear, local, and spatial regression and bootstrap resampling tests and<br> developed data visualization techniques. This offers a unique opportunity to comparatively analyze the differences in<br> climate-cereal yield associations by landscape types and regions. The expected results will provide highly supportive factors<br> for planning and managing an ecologically and economically sustainable land-use system. The intended publications,<br> lectures, and collaborative actions will have a positive impact on domestic and EU research. The planned training and the<br> expected results of the action will further strengthen my career opportunities and put me in a position where I will be<br> considered for a tenured position at a research institution in Hungary.</p>
Dataset for "Species diversity and molecular characterization of Alternaria section Alternaria isolates collected mainly from cereal crops in Canada" by Jeremy R. Dettman, Quinn Eggertson, and Natalie E. Kim
<p>Dataset consists of three files, each containing aligned nucleotide sequences from 559 Alternaria strains. The three sequenced loci are ASA-10, ASA-19, and rpb2. Strain names are stated in the header of each sequence.</p>
Selective sweeps identification in distinct groups of cultivated rye (Secale cereale L.) germplasm provides potential candidate genes for crop improvement
<p><strong>Background</strong></p> <p>During domestication and subsequent improvement, plants were subjected to intensive positive selection for desirable traits. Identification of selection targets is important with respect to the future targeted broadening of diversity in breeding programmes. Rye (<em>Secale</em> <em>cereale</em> L.) is a cereal that is closely related to wheat, and it is an important crop in Central, Eastern and Northern Europe. The aim of the study was (i) to identify diverse groups of rye accessions based on high-density, genome-wide analysis of genetic diversity within a set of 478 rye accessions, covering a full spectrum of diversity within the genus, from wild accessions to inbred lines used in hybrid breeding, and (ii) to identify selective sweeps in the established groups of cultivated rye germplasm and putative candidate genes targeted by selection. <strong> </strong></p> <p><strong>Results</strong></p> <p>Population structure and genetic diversity analyses based on high-quality SNP (DArTseq) markers revealed the presence of three complexes in the <em>Secale</em> genus: <em>S. sylvestre, S. strictum </em>and<em> S. cereale/vavilovii</em>, a relatively narrow diversity of <em>S. sylvestre</em>, very high diversity of <em>S. strictum</em>, and signatures of strong positive selection in <em>S. vavilovii</em>. Within cultivated ryes, we detected the presence of genetic clusters and the influence of improvement status on the clustering. Rye landraces represent a reservoir of variation for breeding, and especially a distinct group of landraces from Turkey should be of special interest as a source of untapped variation. Selective sweep detection in cultivated accessions identified 133 outlier positions within 13 sweep regions and 170 putative candidate genes related, among others, to response to various environmental stimuli (such as pathogens, drought, cold), plant fertility and reproduction (pollen sperm cell differentiation, pollen maturation, pollen tube growth), and plant growth and biomass production.</p> <p><strong>Conclusions</strong></p> <p>Our study provides valuable information for efficient management of rye germplasm collections, which can help to ensure proper safeguarding of their genetic potential and provides numerous novel candidate genes targeted by selection in cultivated rye for further functional characterisation and allelic diversity studies.</p>
Potassium fertilization effects on cereal yield and soil organic carbon in agricultural ecosystems at the global scale
<p>This dataset includes the raw data of a global meta-analysis study on the responses of cereal yield and soil organic carbon to potassium fertilization in agricultural ecosystems.</p>
Secale cereale L. (BR0000011607700)
Belgium Herbarium image of <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.