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37 results for “Sugar beet”
The Cassandra retrotransposon landscape in sugar beet (Beta vulgaris): Recombination and re-shuffling leads to a high structural variability
<p>Here we provide supplementary data for our study of non-autonomous Cassandra terminal-repeat retrotransposons in miniature (TRIMs) in sugar beet and related genomes.</p> <p>Cassandra sequences are distributed across the plant kingdom and share a unique feature: conserved 5S rDNA promoter motifs within their long terminal repeats (LTRs). This dataset contains two multiple sequence alignments and a sequence list of tandemly-arranged (TA) Cassandra sequences in FASTA format. Alignments cover LTR and internal regions of all Amaranthaceae Cassandra (Ama-Cassandra) from our study. This includes Cassandra full-length sequences from <em>B. vulgaris</em> (Ama_Cassandra_Beet_full-length) and <em>C. quinoa</em> (Ama_Cassandra_Quinoa_full-length). Sequence names include information on host plant, subfamily classification, localisation (scaffold), start and stop position, a Lab-unique TE identifier and sequence orientation. For the tandemly-arranged Cassandra sequences from sugar beet, we provide a sequence list of twelve sequences (Ama_Cassandra_TA_Beet_list). Here, sequence names refer to TA copy number, host, localisation (scaffold), start and stop position, a Lab-unique TE identifier and sequence orientation.</p> <p>All sequences were identified in the recent genome assemblys of <em>B. vulgaris</em> (RefBeet1.2; Dohm <em>et al</em>. 2014) and <em>C. quinoa</em> (ASM168347v1; Jarvis <em>et al</em>., 2017).</p>
Hiding in the heterochromatin: Endogenous pararetroviruses escape elimination from the genome of sugar beet (Beta vulgaris)
<p>Here we provide supplementary data for our study of endogenous pararetroviruses in the sugar beet genome (beetEPRVs).</p> <p>Our genome-wide screen showed the presence of three beetEPRVs families in the genome of sugar beet (<em>Beta vulgaris</em>). This dataset contains the consensus sequence for each beetEPRV family (Data S1) and the underlying multiple sequence alignments of individual, genomic beetEPRV copies (Data S2-S5). All data are provided in FASTA format. Sequence names include information on the analysed sugar beet assembly (EL10.1; genebank accession SAMN07736104; Funk <em>et al.</em>, 2018), chromosomal position (chromosome identifier; start and stop position), and sequence orientation (forward = plus; reverse = minus).</p>
Figure 4 in Development of a decision support system for managing Heterodera schahtii in sugar beet production
Figure 4: Crop sequence in rotation 3: standard sugar beets variety "Mixer"; Cereals; WOSRWWOSR; Cereals; Oil radish; "Mixer." The SBN initial population (Pi eggs g−1 soil) = 2.
Figure 8 in Development of a decision support system for managing Heterodera schahtii in sugar beet production
Figure 8: SBN population dynamic under the semi-tolerant variety "Rosalinda" followed by non-host crops at The SBN initial population Pi (eggs g−1 soil) = 4, Tolerance (T) = 0.273, proportion of the population survived (s) = 0.35 and reproduction factor (Rf) = 3.8. The suggested number of waiting years by SBN-Watch to the next "Rosalinda" crop is four years (Pf <T).
Figure 2 in Development of a decision support system for managing Heterodera schahtii in sugar beet production
Figure 2: Crop sequence in rotation 1: standard sugar beets variety "Mixer"; Cereals; Cereals; "Mixer." The SBN initial population (Pi eggs g−1 soil) = 2.
Figure 3 in Development of a decision support system for managing Heterodera schahtii in sugar beet production
Figure 3: Crop sequence in rotation 2: standard sugar beets variety "Mixer"; Cereals; WOSR; Cereals; "Mixer." The SBN initial population (Pi eggs g−1 soil) = 2.
Figure 1 in Development of a decision support system for managing Heterodera schahtii in sugar beet production
Figure 1: A screenshot of the user interface showing a selected crop rotation and the estimated final SBN population (Pf) values, sugar yield (tonnes/ha), income (SEK/ha) and the reproduction factor (Rf) values.
Figure 9 in Development of a decision support system for managing Heterodera schahtii in sugar beet production
Figure 9: (A) relationship between initial SBN population (Pi eggs g−1 soil) and reproduction factors (Rf) of three sugar beets varieties estimated by SBNWatch; (B) relationship between initial SBN population Pi (eggs g−1 soil) and reproduction factors (Rf) of four sugar beets varieties sown in microplots in 2013–2014 (n = 4). The bars represent means of Rf ± Sd.
Figure 7 in Development of a decision support system for managing Heterodera schahtii in sugar beet production
Figure 7: Crop sequence in rotation 6: tolerant sugar beets variety "Julietta"; Cereals; WOSR; Cereals; Oil radish; "Julietta." The SBN initial population (Pi eggs g−1 soil) = 2.
Figure 5 in Development of a decision support system for managing Heterodera schahtii in sugar beet production
Figure 5: Crop sequence in rotation 4: tolerant sugar beets variety "Julietta"; Cereals; Cereals; "Julietta." The SBN initial population (Pi eggs g−1 soil) = 2.
Figure 6 in Development of a decision support system for managing Heterodera schahtii in sugar beet production
Figure 6: Crop sequence in rotation 5: tolerant sugar beets variety "Julietta"; Cereals; WOSR; Cereals; "Julietta." The SBN initial population (Pi eggs g−1 soil) = 2.
Dataset of the Techno-economic assessment (TEA) of the sugar beet pulp biorefinery
<p>This dataset contains information of the CAPEX, OPEX, raw material costs, and labor costs of a sugar beet pulp. These data can be used to perform techno-economic analysis. More information is provided in the file.</p>
Dataset of life cycle assessment (LCA) model for sugar beet pulp biorefinery
<p>This dataset contains information of the foreground and background systems used to model a sugar beet pulp biorefinery. More information can be found in the file.</p>
AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase 1 output data set: LPJ-GUESS sugar beet
<p>This is model output from LPJ-GUESS for sugar beet as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase 1 output data set.</p> <p>The data have been generated following the modeling protocol of Elliott et al. (2015) and has been used to evaluate the models (Müller et al., 2017). A data description paper has been published in Scientific Data (Müller et al. 2019).</p> <p>References:</p> <p>Elliott J, Müller C, Deryng D, Chryssanthacopoulos J, Boote KJ, Büchner M, Foster I, Glotter M, Heinke J, Iizumi T, Izaurralde RC, Mueller ND, Ray DK, Rosenzweig C, Ruane AC, and Sheffield J. 2015, The Global Gridded Crop Model intercomparison: data and modeling protocols for Phase 1 (v1.0). Geosci. Model Dev. 8, 261-277, doi:10.5194/gmd-8-261-2015</p> <p>Müller C, Elliott J, Chryssanthacopoulos J, Arneth A, Balkovic J, Ciais P, Deryng D, Folberth C, Glotter M, Hoek S, Iizumi T, Izaurralde RC, Jones C, Khabarov N, Lawrence P, Liu W, Olin S, Pugh TAM, Ray DK, Reddy A, Rosenzweig C, Ruane AC, Sakurai G, Schmid E, Skalsky R, Song CX, Wang X, de Wit A, and Yang H. 2017, Global gridded crop model evaluation: benchmarking, skills, deficiencies and implications, Geosci. Model Dev., 10, 1403-1422, doi: 10.5194/gmd-10-1403-2017</p> <p>Müller C, Elliott J, Kelly D, Arneth A, Balkovic J, Ciais P, Deryng D, Folberth C, Hoek S, Izaurralde RC, Jones CD, Khabarov N, Lawrence P, Liu W, Olin S, Pugh TAM, Reddy A, Rosenzweig C, Ruane AC, Sakurai G, Schmid E, Skalsky R, Wang X, de Wit A, and Yang H. 2019, The Global Gridded Crop Model Intercomparison phase 1 simulation dataset, Scientific Data, 6, 50, doi: 10.1038/s41597-019-0023-8</p>
AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase 1 output data set: LPJmL sugar beet
<p>This is model output from LPJmL for sugar beet as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase 1 output data set.</p> <p>The data have been generated following the modeling protocol of Elliott et al. (2015) and has been used to evaluate the models (Müller et al., 2017). A data description paper has been published in Scientific Data (Müller et al. 2019).</p> <p>References:</p> <p>Elliott J, Müller C, Deryng D, Chryssanthacopoulos J, Boote KJ, Büchner M, Foster I, Glotter M, Heinke J, Iizumi T, Izaurralde RC, Mueller ND, Ray DK, Rosenzweig C, Ruane AC, and Sheffield J. 2015, The Global Gridded Crop Model intercomparison: data and modeling protocols for Phase 1 (v1.0). Geosci. Model Dev. 8, 261-277, doi:10.5194/gmd-8-261-2015</p> <p>Müller C, Elliott J, Chryssanthacopoulos J, Arneth A, Balkovic J, Ciais P, Deryng D, Folberth C, Glotter M, Hoek S, Iizumi T, Izaurralde RC, Jones C, Khabarov N, Lawrence P, Liu W, Olin S, Pugh TAM, Ray DK, Reddy A, Rosenzweig C, Ruane AC, Sakurai G, Schmid E, Skalsky R, Song CX, Wang X, de Wit A, and Yang H. 2017, Global gridded crop model evaluation: benchmarking, skills, deficiencies and implications, Geosci. Model Dev., 10, 1403-1422, doi: 10.5194/gmd-10-1403-2017</p> <p>Müller C, Elliott J, Kelly D, Arneth A, Balkovic J, Ciais P, Deryng D, Folberth C, Hoek S, Izaurralde RC, Jones CD, Khabarov N, Lawrence P, Liu W, Olin S, Pugh TAM, Reddy A, Rosenzweig C, Ruane AC, Sakurai G, Schmid E, Skalsky R, Wang X, de Wit A, and Yang H. 2019, The Global Gridded Crop Model Intercomparison phase 1 simulation dataset, Scientific Data, 6, 50, doi: 10.1038/s41597-019-0023-8</p>
PaimioSB2023 dataset for sugar beet and weeds discrimination
<p>The PSB2023 dataset includes <span>2272</span> images of sugar beet plants on a field taken by a drone, action camera and cell phone. The dataset was split into 23265 1280x1280 images, where 18593 of them were manually labelled with 186442 sugar beets and 151729 weeds.</p> <p>In addition, labels and some images from the following datasets were fixed.</p> <p><a href="http://www.ipb.uni-bonn.de/data/sugarbeets2016/"><span>http://www.ipb.uni-bonn.de/data/sugarbeets2016/</span></a></p> <p><a href="https://www.ipb.uni-bonn.de/html/projects/SemCUT_DomainAdaption_2022/data.zip"><span>https://www.ipb.uni-bonn.de/html/projects/SemCUT_DomainAdaption_2022/data.zip</span></a></p> <p><a href="https://github.com/LAR/lincolnbeet_dataset"><span>https://github.com/LAR/lincolnbeet_dataset</span></a></p> <p><a href="https://www.phenobench.org/dataset.html"><span>https://www.phenobench.org/dataset.html</span></a></p> <p><a href="https://github.com/inkyusa/weedNet/tree/rgb-samples"><span>https://github.com/inkyusa/weedNet/tree/rgb-samples</span></a></p>
Repeatome turnover meets stable chromosomes: repetitive DNA sequences mark speciation and gene pool boundaries in sugar beet and wild beets
<p>The present repository provides zipped archives containing the results of the RepeatExplorer2 runs of individual as well as comparative repeat analyses in beet genomes.</p> <p> </p> <p>Sugar beet (<em>Beta vulgaris</em> subsp. <em>vulgaris</em>) and its crop wild relatives share a base chromosome number of nine and similar chromosome morphologies. Yet, interspecific breeding is impeded by chromosome and sequence divergence that is still not fully understood. Since repetitive DNA sequences represent the fastest evolving parts of the genome, they likely impact genomic variability and contribute to the separation of beet gene pools. Hence, we investigated if innovations and losses in the repeatome can be linked to chromosomal differentiation and speciation.</p> <p>We traced genome- and chromosome-wide evolution across sugar beet and twelve wild beets comprising all sections of the beet genera <em>Beta </em>and <em>Patellifolia</em>. For this, we combined data from short and long read sequencing, flow cytometry, and cytogenetics to build a comprehensive data framework for our beet panel that spans the complete scale from DNA sequence to chromosome up to the genome. Genome sizes and repeat profiles reflect the separation of the beet species into three gene pools. These gene pools harbor repeats with contrasting evolutionary patterns: We identified section- and species-specific repeat emergences and losses, e.g. of the retrotransposons causal for genome expansions in the section <em>Corollinae</em>/<em>Nanae</em>. Since most genomic variability was found in the satellite DNAs, we focused on tracing the 19 beetSat families across the three beet sections/genera. These taxa harbor evidence for contrasting strategies in repeat evolution, leading to contrasting satellite DNA profiles and fundamentally different centromere architectures, ranging from chromosomal uniformity in <em>Beta</em> and <em>Patellifolia</em> species to the formation of patchwork chromosomes in <em>Corollinae/Nanae</em> species. </p> <p>We show that repetitive DNA sequences are causal for genome size expansion and contraction across the beet genera, providing insights into the genomic underpinnings of beet speciation. Satellite DNAs in particular vary considerably among beet taxa, leading to the evolution of distinct chromosomal setups. These differences likely contribute to the barriers in beet breeding between the three gene pools. Thus, with their isokaryotypic chromosome sets, beet genomes present an ideal system for studying the link between repeats, genome variability, and chromosomal differentiation/evolution and provide a theoretical basis for understanding barriers in crop breeding.</p>
Data from: Enhancing natural enemies in sugar beet fields: The impact of flower strip types and landscape elements
Open the record for dataset details and reuse information.
Data from: An antagonistic pair of FT homologs mediates the control of flowering time in sugar beet
Cultivated beets (Beta vulgaris) are unable to form reproductive shoots during the first year of their life cycle. Flowering only occurs if plants get vernalized, i.e. pass through the winter, and are subsequently exposed to an increasing day length (photoperiod) in spring. Here we show that the regulation of flowering time in beet is controlled by the interplay of two paralogs of the FLOWERING LOCUS T (FT) gene in Arabidopsis that have evolved antagonistic functions. BvFT2 is functionally conserved with FT and essential for flowering. In contrast, BvFT1, which is down-regulated in response to vernalization, represses flowering and is crucial for the vernalization response in beet. These data suggest that beet utilizes a different strategy relative to Arabidopsis and cereals to regulate vernalization.
Determination of process parameters and precipitation methods for potential large-scale production of sugar beet leaf protein concentrate
<p>Abstract</p> <p><br>BACKGROUND</p> <p>Sugar beet is one of the most produced industrial plants in the world, and during manufacturing, it produces a large quantity of leaf waste. Because this waste is rich in protein, this study aimed to identify an efficient method for producing large-scale protein concentrate from sugar beet leaves.</p> <p><br>RESULTS</p> <p>Results showed that protein extraction from fresh leaves was more effective than dried leaves. Maximum protein extraction was achieved at pH 9, compared with pH 7 or 8. Blanching as a pretreatment reduced protein yield during isoelectric precipitation, with a yield of 2.31% compared to 20.20% without blanching. Consequently, blanching was excluded from the extraction process. After extraction, isoelectric precipitation, heat coagulation, and isoelectric–ammonium sulfate precipitation were compared. Although the latter resulted in the highest protein yield, Fourier transform infrared analysis revealed that excessive salt was not removed during dialysis, making it unsuitable for scale-up due to its additional cost and complexity. Therefore, isoelectric precipitation was selected as the appropriate method for protein precipitation from sugar beet leaves. To increase yield, extractions were assisted by ultrasound or enzyme addition. Ultrasound-assisted extraction resulted in an increased protein yield from 20.20% to 28.60%, while Pectinex Ultra SP-L-assisted extraction was the most effective, increasing protein yield from 20.20% to 38.09%.</p> <p><br>CONCLUSION</p> <p>Proteins were extracted from fresh sugar beet leaves using optimum conditions (50 °C, 30 min, pH 9) and precipitated at the isoelectric point, with enzymatic-assisted extraction yielding the maximum protein recovery.<br><br></p>
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