Recent Submissions

  • Journal Article

    Spectral Patterns Reveal Early Resistance Reactions of Barley Against Blumeria graminis f. sp. hordei 

    Kuska, Matheus Thomas; Brugger, Anna; Thomas, Stefan; Wahabzada, Mirwaes; Kersting, Kristian; Oerke, Erich-Christian; Steiner, Ulrike; Mahlein, Anne-Katrin
    Phytopathology 2017; 107(11) p.1388-1398
    Differences in early plant-pathogen interactions are mainly characterized by using destructive methods. Optical sensors are advanced techniques for phenotyping host-pathogen interactions on different scales and for detecting subtle plant resistance responses against pathogens. A microscope with a hyperspectral camera was used to study interactions between Blumeria graminis f. sp. hordei and barley (Hordeum vulgare) genotypes with high susceptibility or resistance due to hypersensitive response (HR) and papilla formation. Qualitative and quantitative assessment of pathogen development was used to explain changes in hyperspectral signatures. Within 48 h after inoculation, genotype-specific changes in the green and red range (500 to 690 nm) and a blue shift of the red-edge inflection point were observed. Manual analysis indicated resistance-specific reflectance patterns from 1 to 3 days after inoculation. These changes could be linked to host plant modifications depending on individual host-pathogen interactions. Retrospective analysis of hyperspectral images revealed spectral characteristics of HR against B. graminis f. sp. hordei. For early HR detection, an advanced data mining approach localized HR spots before they became visible on the RGB images derived from hyperspectral imaging. The link among processes during pathogenesis and host resistance to changes in hyperspectral signatures provide evidence that sensor-based phenotyping is suitable to advance time-consuming and cost-expensive visual rating of plant disease resistances
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  • Journal Article

    Development of a DNA Microarray-Based Assay for the Detection of Sugar Beet Root Rot Pathogens 

    Liebe, Sebastian; Christ, Daniela S.; Ehricht, Ralf; Varrelmann, Mark
    Phytopathology 2016; 106(1) p.76-86
    Sugar beet root rot diseases that occur during the cropping season or in storage are accompanied by high yield losses and a severe reduction of processing quality. The vast diversity of microorganism species involved in rot development requires molecular tools allowing simultaneous identification of many different targets. Therefore, a new microarray technology (ArrayTube) was applied in this study to improve diagnosis of sugar beet root rot diseases. Based on three marker genes (internal transcribed spacer, translation elongation factor 1 alpha, and 16S ribosomal DNA), 42 well-performing probes enabled the identification of prevalent field pathogens (e.g., Aphanomyces cochlioides), storage pathogens (e.g., Botrytis cinerea), and ubiquitous spoilage fungi (e.g., Penicillium expansum). All probes were proven for specificity with pure cultures from 73 microorganism species as well as for in planta detection of their target species using inoculated sugar beet tissue. Microarray-based identification of root rot pathogens in diseased field beets was successfully confirmed by classical detection methods. The high discriminatory potential was proven by Fusarium species differentiation based on a single nucleotide polymorphism. The results demonstrate that the ArrayTube constitute an innovative tool allowing a rapid and reliable detection of plant pathogens particularly when multiple microorganism species are present.
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  • Journal Article

    Effect of Environment and Sugar Beet Genotype on Root Rot Development and Pathogen Profile During Storage 

    Liebe, Sebastian; Varrelmann, Mark
    Phytopathology 2016; 106(1) p.65-75
    Storage rots represent an economically important factor impairing the storability of sugar beet by increasing sucrose losses and invert sugar content. Understanding the development of disease management strategies, knowledge about major storage pathogens, and factors influencing their occurrence is crucial. In comprehensive storage trials conducted under controlled conditions, the effects of environment and genotype on rot development and associated quality changes were investigated. Prevalent species involved in rot development were identified by a newly developed microarray. The strongest effect on rot development was assigned to environment factors followed by genotypic effects. Despite large variation in rot severity (sample range 0 to 84%), the spectrum of microorganisms colonizing sugar beet remained fairly constant across all treatments with dominant species belonging to the fungal genera Botrytis, Fusarium, and Penicillium. The intensity of microbial tissue necrotization was strongly correlated with sucrose losses (R² = 0.79 to 0.91) and invert sugar accumulation (R² = 0.91 to 0.95). A storage rot resistance bioassay was developed that could successfully reproduce the genotype ranking observed in storage trials. Quantification of fungal biomass indicates that genetic resistance is based on a quantitative mechanism. Further work is required to understand the large environmental influence on rot development in sugar beet.
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  • Journal Article

    Assessment of Fusarium Infection and Mycotoxin Contamination of Wheat Kernels and Flour Using Hyperspectral Imaging 

    Alisaac, Elias; Behmann, Jan; Rathgeb, Anna; Karlovsky, Petr; Dehne, Heinz-Wilhelm; Mahlein, Anne-Katrin
    Toxins 2019; 11(10): Art. 556
    Fusarium head blight (FHB) epidemics in wheat and contamination with Fusarium mycotoxins has become an increasing problem over the last decades. This prompted the need for non-invasive and non-destructive techniques to screen cereal grains for Fusarium infection, which is usually accompanied by mycotoxin contamination. This study tested the potential of hyperspectral imaging to monitor the infection of wheat kernels and flour with three Fusarium species. Kernels of two wheat varieties inoculated at anthesis with F. graminearum, F. culmorum, and F. poae were investigated. Hyperspectral images of kernels and flour were taken in the visible-near infrared (VIS-NIR) (400-1000 nm) and short-wave infrared (SWIR) (1000-2500 nm) ranges. The fungal DNA and mycotoxin contents were quantified. Spectral reflectance of Fusarium-damaged kernels (FDK) was significantly higher than non-inoculated ones. In contrast, spectral reflectance of flour from non-inoculated kernels was higher than that of FDK in the VIS and lower in the NIR and SWIR ranges. Spectral reflectance of kernels was positively correlated with fungal DNA and deoxynivalenol (DON) contents. In the case of the flour, this correlation exceeded r = -0.80 in the VIS range. Remarkable peaks of correlation appeared at 1193, 1231, 1446 to 1465, and 1742 to 2500 nm in the SWIR range.
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  • Journal Article

    In-Field Detection of Yellow Rust in Wheat on the Ground Canopy and UAV Scale 

    Bohnenkamp, David; Behmann, Jan; Mahlein, Anne-Katrin
    Remote Sensing 2019; 11(21): Art. 2495
    The application of hyperspectral imaging technology for plant disease detection in the field is still challenging. Existing equipment and analysis algorithms are adapted to highly controlled environmental conditions in the laboratory. However, only real time information from the field scale is able to guide plant protection measures and to optimize the use of resources. At the field scale, many parameters such as the optimal measurement distance, informative feature sets, and suitable algorithms have not been investigated. In this study, the hyperspectral detection and quantification of yellow rust in wheat was evaluated using two measurement platforms: a ground-based vehicle and an unmanned aerial vehicle (UAV). Di erent disease development stages and disease severities were provided in a plot-based field experiment. Measurements were performed weekly during the vegetation period. Data analysis was performed by three prediction algorithms with a focus on the selection of optimal feature sets. In this context, the across-scale application of optimized feature sets, an approach of information transfer between scales, was also evaluated. Relevant aspects for an on-line disease assessment in the field integrating a ordable sensor technology, sensor spatial resolution, compact analysis models, and fast evaluation have been outlined and reflected upon. For the first time, a hyperspectral imaging observation experiment of a plant disease was comparatively performed at two scales, ground canopy and UAV.
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  • Journal Article

    Infrared Thermography as a Non-Invasive Tool to Explore Differences in the Musculoskeletal System of Children with Hemophilia Compared to an Age-Matched Healthy Group 

    Seuser, Axel; Kurnik, Karin; Mahlein, Anne-Katrin
    Sensors 2018; 18(2): Art. 518
    Recurrent joint bleeds and silent bleeds are the most common clinical feature in patients with hemophilia. Every bleed causes an immediate inflammatory response and is the leading cause of chronic crippling arthropathy. With the help of infrared thermography we wanted to detect early differences between a group of clinical non-symptomatic children with hemophilia (CWH) with no history of clinically detected joint bleeds and a healthy age-matched group of children. This could help to discover early inflammation and help implement early treatment and preventative strategies. It could be demonstrated that infrared thermography is sensitive enough to detect more signs of early inflammatory response in the CWH than in healthy children. It seems to detect more side differences in temperature than clinical examination of silent symptoms detects tender points. Silent symptoms/tender points seem to be combined with early local inflammation. Using such a non-invasive and sensor-based early detection, prevention of overloading and bleeding might be achieved.
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  • Journal Article

    Quantitative assessment of disease severity and rating of barley cultivars based on hyperspectral imaging in a non-invasive, automated phenotyping platform 

    Thomas, Stefan; Behmann, Jan; Steier, Angelina; Kraska, Thorsten; Muller, Onno; Rascher, Uwe; Mahlein, Anne-Katrin
    Plant Methods 2018; 14(1)
    Background: Phenotyping is a bottleneck for the development of new plant cultivars. This study introduces a new hyperspectral phenotyping system, which combines the high throughput of canopy scale measurements with the advantages of high spatial resolution and a controlled measurement environment. Furthermore, the measured barley canopies were grown in large containers (called Mini-Plots), which allow plants to develop field-like phenotypes in greenhouse experiments, without being hindered by pot size. Results: Six barley cultivars have been investigated via hyperspectral imaging up to 30 days after inoculation with powdery mildew. With a high spatial resolution and stable measurement conditions, it was possible to automatically quantify powdery mildew symptoms through a combination of Simplex Volume Maximization and Support Vector Machines. Detection was feasible as soon as the first symptoms were visible for the human eye during manual rating. An accurate assessment of the disease severity for all cultivars at each measurement day over the course of the experiment was realized. Furthermore, powdery mildew resistance based necrosis of one cultivar was detected as well. Conclusion: The hyperspectral phenotyping system combines the advantages of field based canopy level measurement systems (high throughput, automatization, low manual workload) with those of laboratory based leaf level measurement systems (high spatial resolution, controlled environment, stable conditions for time series measurements). This allows an accurate and objective disease severity assessment without the need for trained experts, who perform visual rating, as well as detection of disease symptoms in early stages. Therefore, it is a promising tool for plant resistance breeding.
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  • Journal Article

    Crop wild relative populations of Beta vulgaris allow direct mapping of agronomically important genes 

    Capistrano-Gossmann, Gina G.; Ries, D.; Holtgräwe, D.; Minoche, A.; Kraft, T.; Frerichmann, S.L.M.; Rosleff Soerensen, T.; Dohm, J. C.; González, I.; Schilhabel, M.; et al.
    Varrelmann, M.Tschoep, H.Uphoff, H.Schütze, K.Borchardt, D.Toerjek, O.Mechelke, W.Lein, J. C.Schechert, A. W.Frese, L.Himmelbauer, H.Weisshaar, B.Kopisch-Obuch, F. J.
    Nature Communications 2017; 8(1): Art. 15708
    Rapid identification of agronomically important genes is of pivotal interest for crop breeding. One source of such genes are crop wild relative (CWR) populations. Here we used a CWR population of <200 wild beets (B. vulgaris ssp. maritima), sampled in their natural habitat, to identify the sugar beet (Beta vulgaris ssp. vulgaris) resistance gene Rz2 with a modified version of mapping-by-sequencing (MBS). For that, we generated a draft genome sequence of the wild beet. Our results show the importance of preserving CWR in situ and demonstrate the great potential of CWR for rapid discovery of causal genes relevant for crop improvement. The candidate gene for Rz2 was identified by MBS and subsequently corroborated via RNA interference (RNAi). Rz2 encodes a CC-NB-LRR protein. Access to the DNA sequence of Rz2 opens the path to improvement of resistance towards rhizomania not only by marker-assisted breeding but also by genome editing.
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  • Journal Article

    Monitoring wound healing in a 3D wound model by hyperspectral imaging and efficient clustering 

    Wahabzada, Mirwaes; Besser, Manuela; Khosravani, Milad; Kuska, Matheus Thomas; Kersting, Kristian; Mahlein, Anne-Katrin; Stürmer, Ewa
    PLOS ONE 2017; 12(12): Art. e0186425
    Wound healing is a complex and dynamic process with different distinct and overlapping phases from homeostasis, inflammation and proliferation to remodelling. Monitoring the healing response of injured tissue is of high importance for basic research and clinical practice. In traditional application, biological markers characterize normal and abnormal wound healing. Understanding functional relationships of these biological processes is essential for developing new treatment strategies. However, most of the present techniques (in vitro or in vivo) include invasive microscopic or analytical tissue sampling. In the present study, a non-invasive alternative for monitoring processes during wound healing is introduced. Within this context, hyperspectral imaging (HSI) is an emerging and innovative non-invasive imaging technique with different opportunities in medical applications. HSI acquires the spectral reflectance of an object, depending on its biochemical and structural characteristics. An in-vitro 3-dimensional (3-D) wound model was established and incubated without and with acute and chronic wound fluid (AWF, CWF), respectively. Hyperspectral images of each individual specimen of this 3-D wound model were assessed at day 0/5/10 in vitro, and reflectance spectra were evaluated. For analysing the complex hyperspectral data, an efficient unsupervised approach for clustering massive hyperspectral data was designed, based on efficient hierarchical decomposition of spectral information according to archetypal data points. It represents, to the best of our knowledge, the first application of an advanced Data Mining approach in context of non-invasive analysis of wounds using hyperspectral imagery. By this, temporal and spatial pattern of hyperspectral clusters were determined within the tissue discs and among the different treatments. Results from non-invasive imaging were compared to the number of cells in the various clusters, assessed by Hematoxylin/Eosin (H/E) staining. It was possible to correlate cell quantity and spectral reflectance during wound closure in a 3-D wound model in vitro.
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  • Journal Article

    Comparison and Combination of Thermal, Fluorescence, and Hyperspectral Imaging for Monitoring Fusarium Head Blight of Wheat on Spikelet Scale 

    Mahlein, Anne-Katrin; Alisaac, Elias; Al Masri, Ali; Behmann, Jan; Dehne, Heinz-Wilhelm; Oerke, Erich-Christian
    Sensors 2019; 19(10): Art. 2281
    Optical sensors have shown high capabilities to improve the detection and monitoring of plant disease development. This study was designed to compare the feasibility of different sensors to characterize Fusarium head blight (FHB) caused by Fusarium graminearum and Fusarium culmorum. Under controlled conditions, time-series measurements were performed with infrared thermography (IRT), chlorophyll fluorescence imaging (CFI), and hyperspectral imaging (HSI) starting 3 days after inoculation (dai). IRT allowed the visualization of temperature differences within the infected spikelets beginning 5 dai. At the same time, a disorder of the photosynthetic activity was confirmed by CFI via maximal fluorescence yields of spikelets (Fm) 5 dai. Pigment-specific simple ratio PSSRa and PSSRb derived from HSI allowed discrimination between Fusarium-infected and non-inoculated spikelets 3 dai. This effect on assimilation started earlier and was more pronounced with F. graminearum. Except the maximum temperature difference (MTD), all parameters derived from different sensors were significantly correlated with each other and with disease severity (DS). A support vector machine (SVM) classification of parameters derived from IRT, CFI, or HSI allowed the differentiation between non-inoculated and infected spikelets 3 dai with an accuracy of 78, 56 and 78%, respectively. Combining the IRT-HSI or CFI-HSI parameters improved the accuracy to 89% 30 dai.
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  • Journal Article

    Crop identity and memory effects on aboveground arthropods in a long‐term crop rotation experiment 

    Meyer, Michael; Ott, David; Götze, Philipp; Koch, Heinz‐Josef; Scherber, Christoph
    Ecology and Evolution 2019; 9(12) p.7307-7323
    Agricultural landscapes are globally dominated by monocultures under intensive management. This is one of the main reasons for biodiversity loss and insect population decline in many regions all over the world. Agroecosystem biodiversity in these areas can be enhanced by cropping system diversification, such as crop rotations. Yet, long‐term studies on effects of crop rotations on aboveground agrobiodiversity are lacking. We set up a 10‐year long‐term crop rotation experiment in Central Germany and monitored the temporal dynamics of aboveground arthropods over a full cultivation period to investigate influence of current and preceding crop identity and cropping system diversification on activity density, species richness, and community structure. We found that species composition was strongly influenced by currently grown crop although effect on arthropods varied between species groups. Especially, winter oilseed rape strongly affects arthropod community structure. Interestingly, we were also able to show an influence of the preceding crops, indicating an ecological memory effect in the aboveground arthropod community. Our results show that crop identity of both currently and previously grown crops in crop rotations may lead to an increase in arthropod activity density and changes in species composition. Diversified crop rotations including appropriate crops can be an easily implemented tool to increase arthropod biodiversity and biomass at large spatial and temporal scales, particularly in areas dominated by a single crop (e.g., wheat, maize). Our results may help to design optimized crop rotations for large‐scale enhancement of insect biodiversity in agroecosystems.
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  • Journal Article

    Extending Hyperspectral Imaging for Plant Phenotyping to the UV-Range 

    Brugger, Anna; Behmann, Jan; Paulus, Stefan; Luigs, Hans-Georg; Kuska, Matheus Thomas; Schramowski, Patrick; Kersting, Kristian; Steiner, Ulrike; Mahlein, Anne-Katrin
    Remote Sensing 2019; 11(12): Art. 1401
    Previous plant phenotyping studies have focused on the visible (VIS, 400–700 nm), near-infrared (NIR, 700–1000 nm) and short-wave infrared (SWIR, 1000–2500 nm) range. The ultraviolet range (UV, 200–380 nm) has not yet been used in plant phenotyping even though a number of plant molecules like flavones and phenol feature absorption maxima in this range. In this study an imaging UV line scanner in the range of 250–430 nm is introduced to investigate crop plants for plant phenotyping. Observing plants in the UV-range can provide information about important changes of plant substances. To record reliable and reproducible time series results, measurement conditions were defined that exclude phototoxic effects of UV-illumination in the plant tissue. The measurement quality of the UV-camera has been assessed by comparing it to a non-imaging UV-spectrometer by measuring six different plant-based substances. Given the findings of these preliminary studies, an experiment has been defined and performed monitoring the stress response of barley leaves to salt stress. The aim was to visualize the effects of abiotic stress within the UV-range to provide new insights into the stress response of plants. Our study demonstrated the first use of a hyperspectral sensor in the UV-range for stress detection in plant phenotyping.
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  • Journal Article

    Measuring crops in 3D: using geometry for plant phenotyping 

    Paulus, Stefan
    Plant Methods 2019; 15(1): Art. 103
    Using 3D sensing for plant phenotyping has risen within the last years. This review provides an overview on 3D traits for the demands of plant phenotyping considering different measuring techniques, derived traits and use-cases of biological applications. A comparison between a high resolution 3D measuring device and an established measuring tool, the leaf meter, is shown to categorize the possible measurement accuracy. Furthermore, different measuring techniques such as laser triangulation, structure from motion, time-of-flight, terrestrial laser scanning or structured light approaches enable the assessment of plant traits such as leaf width and length, plant size, volume and development on plant and organ level. The introduced traits were shown with respect to the measured plant types, the used measuring technique and the link to their biological use case. These were trait and growth analysis for measurements over time as well as more complex investigation on water budget, drought responses and QTL (quantitative trait loci) analysis. The used processing pipelines were generalized in a 3D point cloud processing workflow showing the single processing steps to derive plant parameters on plant level, on organ level using machine learning or over time using time series measurements. Finally the next step in plant sensing, the fusion of different sensor types namely 3D and spectral measurements is introduced by an example on sugar beet. This multi-dimensional plant model is the key to model the influence of geometry on radiometric measurements and to correct it. This publication depicts the state of the art for 3D measuring of plant traits as they were used in plant phenotyping regarding how the data is acquired, how this data is processed and what kind of traits is measured at the single plant, the miniplot, the experimental field and the open field scale. Future research will focus on highly resolved point clouds on the experimental and field scale as well as on the automated trait extraction of organ traits to track organ development at these scales.
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  • Journal Article

    Screening of Barley Resistance Against Powdery Mildew by Simultaneous High-Throughput Enzyme Activity Signature Profiling and Multispectral Imaging 

    Kuska, Matheus T.; Behmann, Jan; Großkinsky, Dominik K.; Roitsch, Thomas; Mahlein, Anne-Katrin
    Frontiers in Plant Science 2018; 9 p.1074-1074
    Molecular marker analysis allow for a rapid and advanced pre-selection and resistance screenings in plant breeding processes. During the phenotyping process, optical sensors have proved their potential to determine and assess the function of the genotype of the breeding material. Thereby, biomarkers for specific disease resistance traits provide valuable information for calibrating optical sensor approaches during early plant-pathogen interactions. In this context, the combination of physiological, metabolic phenotyping and phenomic profiles could establish efficient identification and quantification of relevant genotypes within breeding processes. Experiments were conducted with near-isogenic lines of H. vulgare (susceptible, mildew locus o (mlo) and Mildew locus a (Mla) resistant). Multispectral imaging of barley plants was daily conducted 0-8 days after inoculation (dai) in a high-throughput facility with 10 wavelength bands from 400 to 1,000 nm. In parallel, the temporal dynamics of the activities of invertase isoenzymes, as key sink specific enzymes that irreversibly cleave the transport sugar sucrose into the hexose monomers, were profiled in a semi high-throughput approach. The activities of cell wall, cytosolic and vacuole invertase revealed specific dynamics of the activity signatures for susceptible genotypes and genotypes with mlo and Mla based resistances 0-120 hours after inoculation (hai). These patterns could be used to differentiate between interaction types and revealed an early influence of Blumeria graminis f.sp. hordei (Bgh) conidia on the specific invertase activity already 0.5 hai. During this early powdery mildew pathogenesis, the reflectance intensity increased in the blue bands and at 690 nm. The Mla resistant plants showed an increased reflectance at 680 and 710 nm and a decreased reflectance in the near infrared bands from 3 dai. Applying a Support Vector Machine classification as a supervised machine learning approach, the pixelwise identification and quantification of powdery mildew diseased barley tissue and hypersensitive response spots were established. This enables an automatic identification of the barley-powdery mildew interaction. The study established a proof-of-concept for plant resistance phenotyping with multispectral imaging in high-throughput. The combination of invertase analysis and multispectral imaging showed to be a complementing validation system. This will provide a deeper understanding of optical data and its implementation into disease resistance screening.
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  • Journal Article

    Crop Rotational Effects on Yield Formation in Current Sugar Beet Production - Results From a Farm Survey and Field Trials 

    Koch, Heinz-Josef; Trimpler, Kerrin; Jacobs, Anna; Stockfisch, Nicol
    Frontiers in plant science 2018; 9: Art. 231
    In Europe, the framework for sugar beet (Beta vulgaris L.) production was subject to considerable changes and for the future it is expected that sugar beet cultivation might concentrate around the sugar factories for economic reasons. Based on data from a national sugar beet farmers' survey and multi-year crop rotation trials, the effects of cropping interval (number of years in between two subsequent sugar beet crops) and of preceding crops on sugar yield were elucidated under current Central European management conditions. The dominating sugar beet cropping interval was ≥4 years in the farm survey with pronounced differences between regions. However, the cropping intervals 2, 3, and ≥4 years did not affect the sugar yield. Therefore, significant differences in sugar yield between regions were assumed to be caused by multiple interactions between year, site, and farmers' skills. Throughout Germany, the dominating preceding crops in sugar beet cultivation were winter wheat (Triticum aestivum L.) and winter barley (Hordeum vulgare L.). In the field trials, the sugar yield was 5% higher after pea (Pisum sativum L.) compared to maize (Zea mays L.) as preceding crop, while differences between the preceding crops pea and winter wheat, and wheat and maize were not significant. Repeated measurements of canopy development and leaf color during the growing season revealed a higher N-availability after pea as preceding crop. However, decreased growth after maize was not completely compensated for by high N-fertilizer doses. Overall, the causes for the differences in sugar yield between the preceding crops remained open. The results do not support concerns about substantial yield losses in sugar beet production due to a reduction in the cropping interval from 3 to 2 years. Nevertheless, short rotations with maize and sugar beet might increase the risk of Rhizoctonia solani crown and root rot infestation. Leguminous crops such as pea offer the potential for higher sugar beet yield with lower N-fertilizer doses.
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  • Journal Article

    Yield Potential of Sugar Beet - Have We Hit the Ceiling? 

    Hoffmann, Christa M.; Kenter, Christine
    Frontiers in Plant Science 2018; 9: Art. 289
    The yield of sugar beet has continuously increased in the past decades. The question arises, whether this progress will continue in the future. A key factor for increasing yield potential of the crop is breeding progress. It was related to a shift in assimilate partitioning in the plant toward more storage carbohydrates (sucrose), whereas structural carbohydrates (leaves, cell wall compounds) unintendedly declined. The yield potential of sugar beet was estimated at 24 t sugar ha-1. For maximum yield, sufficient growth factors have to be available and the crop has to be able to fully utilize them. In sugar beet, limitations result from the lacking coincidence of maximum irradiation rates and full canopy cover, sink strength for carbon assimilation and high water demand, which cannot be met by rainfall alone. After harvest, sugar losses during storage occur. The paper discusses options for a further increase in yield potential, like autumn sowing of sugar beet, increasing sink strength and related constraints. It is prospected that yield increase by further widening the ratio of storage and structural carbohydrates will come to its natural limit as a certain cell wall stability is necessary. New challenges caused by climate change and by prolonged processing campaigns will occur. Thus breeding for improved pathogen resistance and storage properties will be even more important for successful sugar beet production than a further increase in yield potential itself.
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  • Journal Article

    Massive up-regulation of LBD transcription factors and EXPANSINs highlights the regulatory programs of rhizomania disease 

    Fernando Gil, Jose; Liebe, Sebastian; Thiel, Heike; Lennefors, Britt-Louise; Kraft, Thomas; Gilmer, David; Maiss, Edgar; Varrelmann, Mark; Savenkov, Eugene I.
    Molecular Plant Pathology 2018; 19(10) p.2333-2348
    Rhizomania of sugar beet, caused by Beet necrotic yellow vein virus (BNYVV), is characterized by excessive lateral root (LR) formation leading to dramatic reduction of taproot weight and massive yield losses. LR formation represents a developmental process tightly controlled by auxin signaling through AUX/IAA-ARF responsive module and LATERAL ORGAN BOUNDARIES DOMAIN (LBD) transcriptional network. Several LBD transcription factors play central roles in auxin-regulated LR development and act upstream of EXPANSINS (EXPs), cell wall (CW)-loosening proteins involved in plant development via disruption of the extracellular matrix for CW relaxation and expansion. Here, we present evidence that BNYVV hijacks these auxin-regulated pathways resulting in formation LR and root hairs (RH). We identified an AUX/IAA protein (BvAUX28) as interacting with P25, a viral virulence factor. Mutational analysis indicated that P25 interacts with domains I and II of BvAUX28. Subcellular localization of co-expressed P25 and BvAUX28 showed that P25 inhibits BvAUX28 nuclear localization. Moreover, root-specific LBDs and EXPs were greatly upregulated during rhizomania development. Based on these data, we present a model in which BNYVV P25 protein mimics action of auxin by removing BvAUX28 transcriptional repressor, leading to activation of LBDs and EXPs. Thus, the evidence highlights two pathways operating in parallel and leading to uncontrolled formation of LRs and RHs, the main manifestation of the rhizomania syndrome.
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  • Journal Article

    Discovering coherency of specific gene expression and optical reflectance properties of barley genotypes differing for resistance reactions against powdery mildew 

    Kuska, Matheus Thomas; Behmann, Jan; Namini, Mahsa; Oerke, Erich-Christian; Steiner, Ulrike; Mahlein, Anne-Katrin
    PlOS ONE 2019; 14(3): Art. e0213291
    Hyperspectral imaging has proved its potential for evaluating complex plant-pathogen interactions. However, a closer link of the spectral signatures and genotypic characteristics remains elusive. Here, we show relation between gene expression profiles and specific wavebands from reflectance during three barley-powdery mildew interactions. Significant synergistic effects between the hyperspectral signal and the corresponding gene activities has been shown using the linear discriminant analysis (LDA). Combining the data sets of hyperspectral signatures and gene expression profiles allowed a more precise differentiation of the three investigated barley-Bgh interactions independent from the time after inoculation. This shows significant synergistic effects between the hyperspectral signal and the corresponding gene activities. To analyze this coherency between spectral reflectance and seven different gene expression profiles, relevant wavelength bands and reflectance intensities for each gene were computed using the Relief algorithm. Instancing, xylanase activity was indicated by relevant wavelengths around 710 nm, which are characterized by leaf and cell structures. HvRuBisCO activity underlines relevant wavebands in the green and red range, elucidating the coherency of RuBisCO to the photosynthesis apparatus and in the NIR range due to the influence of RuBisCO on barley leaf cell development. These findings provide the first insights to links between gene expression and spectral reflectance that can be used for an efficient non-invasive phenotyping of plant resistance and enables new insights into plant-pathogen interactions.
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  • Journal Article

    Specim IQ: Evaluation of a New, Miniaturized Handheld Hyperspectral Camera and Its Application for Plant Phenotyping and Disease Detection 

    Behmann, Jan; Acebron, Kelvin; Emin, Dzhaner; Bennertz, Simon; Matsubara, Shizue; Thomas, Stefan; Bohnenkamp, David; Kuska, Matheus T.; Jussila, Jouni; Salo, Harri; et al.
    Mahlein, Anne-KatrinRascher, Uwe
    Sensors (Basel, Switzerland) 2018; 18(2): Art. 441
    Hyperspectral imaging sensors are promising tools for monitoring crop plants or vegetation in different environments. Information on physiology, architecture or biochemistry of plants can be assessed non-invasively and on different scales. For instance, hyperspectral sensors are implemented for stress detection in plant phenotyping processes or in precision agriculture. Up to date, a variety of non-imaging and imaging hyperspectral sensors is available. The measuring process and the handling of most of these sensors is rather complex. Thus, during the last years the demand for sensors with easy user operability arose. The present study introduces the novel hyperspectral camera Specim IQ from Specim (Oulu, Finland). The Specim IQ is a handheld push broom system with integrated operating system and controls. Basic data handling and data analysis processes, such as pre-processing and classification routines are implemented within the camera software. This study provides an introduction into the measurement pipeline of the Specim IQ as well as a radiometric performance comparison with a well-established hyperspectral imager. Case studies for the detection of powdery mildew on barley at the canopy scale and the spectral characterization of Arabidopsis thaliana mutants grown under stressed and non-stressed conditions are presented.
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  • Journal Article

    Silage Maize and Sugar Beet for Biogas Production in Rotations and Continuous Cultivation: Dry Matter and Estimated Methane Yield 

    Brauer-Siebrecht, Wiebke; Jacobs, Anna; Christen, Olaf; Götze, Philipp; Koch, Heinz-Josef; Rücknagel, Jan; Märländer, Bernward
    Agronomy 2016; 6(1): Art. 2
    Since silage maize is the main crop grown for biogas production (biomass crop) in Germany; its increasing cultivation is critically discussed in terms of social and agronomical aspects. To investigate if sugar beet is suitable as an alternative biomass crop to silage maize; three-year field trials with both biomass crops in rotations with winter wheat (food crop) and continuous cultivation were conducted at three highly productive sites. Dry matter (DM) yield per hectare was measured via field trials whereas methane yield per hectare was estimated via a calculation. Higher annual DM yield was achieved by silage maize (19.5–27.4 t∙ha−1∙a−1) compared to sugar beet root (10.7–23.0 t∙ha−1∙a−1). Dry matter yield was found to be the main driver for the estimated methane yield. Thus; higher estimated methane yield was produced by silage maize (6458–9388 Nm3∙ha−1) with overlaps to sugar beet root (3729–7964 Nm3∙ha−1). We; therefore; classify sugar beet as a suitable alternative biomass crop to silage maize; especially when cultivated in crop rotations with winter wheat. Additionally; we found that the evaluation of entire crop rotations compared to single crops is a more precise approach since it includes rotational effects.
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