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18 results for “crop roots”
Data and results for manuscript "Multi-frequency electrical impedance tomography as a non-invasive tool to characterize and monitor crop root systems "
<p>Root systems are essential in nutrient uptake and translocation, but are difficult to characterize non-invasively with existing methods. We propose electrical impedance tomography (EIT) as a new tool for the imaging and monitoring of crop root systems. In a laboratory experiment we demonstrate the capability of the method to capture physiological responses of root systems with high spatial and temporal resolution. We conclude that EIT is a promising functional imaging technique for crop roots.</p> <p>This package contains measured raw EIT data, electrical imaging results, spectral results from the Debye decomposition, and the Python scripts used to generate the plots in the manuscript.</p>
Limited effect of thermal pruning on wild blueberry crop and its root-associated microbiota - Agricultural dataset
<p>These datasets contain all the agricultural data (soil chemistry, blueberry performance, weeds and diseases...) used in our study.</p>
Data and results for manuscript: "Imaging and functional characterization of crop root systems using spectroscopic electrical impedance measurements"
<p>This package contains measured raw EIT data, electrical imaging results, spectral results from the Debye decomposition, and the Python scripts used to generate the plots in the manuscript titled:<br> <br> Imaging and functional characterization of crop root systems using spectroscopic electrical impedance measurement</p>
Meta-analysis of multiple ion uptake kinetics in crop roots
<p>This dataset collates measures of Michaelis-Menten root uptake parameters for several nutrients based on reported literature. The analysis appears in a review article by Marcus Griffiths (mdgriffiths at noble.org) and Larry York (lmyork at noble.org).</p> <p>Please cite this repository and the review article if the data is reused.</p> <p><strong>Citation:</strong></p> <p>Griffiths, M. G., York, L. M. (In Press) Targeting root ion uptake kinetics to increase plant productivity and nutrient use efficiency. Plant Physiology. <a href="https://doi.org/10.1104/pp.19.01496">https://doi.org/10.1104/pp.19.01496</a></p> <p><strong>File descriptions and use</strong></p> <p>The R script assumes that all data files are in the working directory and that the "tidyverse" meta-package is installed.</p> <p><strong>Kinetics_metaanalysis_all_files_v2.zip</strong> - Contains all the files.<br> <br> <strong>Kinetics_metaanalysis_column_definitions.txt </strong>- Definitions of column names used in data files (not used by R script)<br> <br> <strong>Kinetics_metaanalysis_csv.csv </strong>- Complete dataset in CSV format<strong> </strong>(used by R script)<br> <br> <strong>Kinetics_metaanalysis_excel.xlsx</strong> - Complete dataset in Excel format (not used by R script)<br> <br> <strong>Kinetics_metaanalysis_figure_subset.csv </strong>- Subset of total data used for figure generation (used by R script)<br> <br> <strong>Kinetics_metaanalysis_figures_code.R </strong>- Required R script for statistical analysis </p> <p>Version 2 of this repository includes minor updates to include new papers and data.</p> <p> </p>
Overyielding is accounted for partly by plasticity and dissimilarity of crop root traits in maize/legume intercropping systems
<p><span>Positive biodiversity-productivity relationships have been found in biodiversity field experiments of grassland, forestry, and other natural terrestrial ecosystems, where diversity effects were separated by complementarity (CE) and selection effects (SE). However, we know little about how CE and SE are related to root traits and root dissimilarity.</span></p> <p><span>A four-year field experiment was carried out with a split-plot design, where main plot was four nitrogen (N) applications (N0, N1, N2, N3) and five cropping systems (maize (</span><span><em>Zea mays</em> </span><span>L.</span><span>)/soybean (</span><span><em>Glycine max</em> </span><span>L. Merrill.</span><span>), maize/peanut (</span><em><span>Arachis hypogaea</span></em> <span>L.</span><span>) intercropped and the corresponding monocultures) with three replicates. Roots were sampled in the N0 and N2 treatments in two years. Intercropping effects were analyzed based on grain yield for four years and roots were sampled down to 60 cm depth, and analyzed with morphological parameters at different crop growth stages in two years.</span></p> <p><span>Intercropping significantly increased grain yield and aboveground biomass in both intercropping systems under all N treatments. The partitioning of the net intercropping effects showed that yield advantage in intercropping was due to a positive CE under the N0 treatment, and to a positive SE with N application.</span></p> <p><span>Maize showed greater root morphological plasticity than the legumes did, with greater changes in root length density (RLD), root weight density (RWD) and total root surface (TS) in intercropping than in monoculture. Intercropped maize occupied a larger soil space, while lateral RLD distribution of legumes was decreased by maize. The RLD, RWD, and TS of intercropped maize were constant or increased in later growth stages. SE showed a significantly positive relationship with root dissimilarity. Principal component analysis showed mean root depth and specific root length of legumes drove the positive CE in the absence of N fertilization. </span></p> <p><span>Root dissimilarity determined by maize explained the selection effects in overyielding. Complementarity effects under N0 were closely associated with specific root traits such as mean root depth and specific root length. Linking changes of root traits with intercropping effects aboveground helps understand yield advantages in diverse agroecosystem.</span><span> In general, a cereal species with strong phenotypic plasticity intercropped with a legume species with strong physiological plasticity can maximize the yield advantage of intercropping.</span></p>
Images, data, and statistical analysis scripts for review article on cover crop roots
<p>Images, data, and statistical analysis scripts for review article on cover crop roots.</p> <blockquote> <p><strong>Optimization of root traits to provide enhanced ecosystem services in agricultural systems: a focus on cover crops</strong> - [<a href="https://doi.org/10.1111/pce.14247">https://doi.org/10.1111/pce.14247</a>]</p> </blockquote> <ul> <li>Research site, planting, and growth <ul> <li>10/2020 - 04/26/2021 cover crop field trial. DDPSC FRS at Planthaven Farm, O'Fallon, MO 63366 (latitude 38.848240°, longitude -90.686640°). </li> <li>The field was tilled before sowing of cover crops. Seed for each cover crop were spread in using a push seed spreader and were lightly irrigated.</li> <li>Alfalfa (<em>Medicago sativa</em>), dundale pea (<em>Pisum sativum</em>), milkvetch (<em>Astragalus canadensis</em>, <em>Astragalus bisulcatus</em>), crimson clover (<em>Trifolium incarnatum</em>), hairy vetch (<em>Vicia villosa</em>), mustard (<em>Brassica junce</em>a var Mighty Mustard, var Kodiak), barley (<em>Hordeum vulgare</em>), wheat (<em>Triticum aestivum</em>, winter, spring), winter rye (<em>Secale cereale</em>), and triticale (× T<em>riticosecale</em> Wittmack).</li> </ul> </li> <li> <p>Field harvest measurements</p> <ul> <li> <p>Four canopy images were taken across each cover crop row using a Canon 5DS R camera. Images were taken from above each plot at 5ft height manually. Green color was thresholded from the canopy images in batch using OpenCV python script and the percent green cover calculated (Jupiter notebook).</p> </li> <li> <p>Five soil monoliths were excavated using a "shovelomics" approach with an average monolith size of 25.4cm x 25.4cm x 20 cm. The remaining four soil monoliths were destructively analyzed.</p> </li> <li> <p>One soil monolith was imaged using a Canon 50D DLSR camera in a photogrammetry shed. All photogrammetric analysis was conducted using Pix4D mapper software (Pix4D S.A. Prilly, Switzerland), and point cloud cleaning was conducted in CloudCompare V2. 10.2.</p> </li> <li> <p>Cover crop shoots from the remaining soil monoliths were cut and placed into a paper bag for dry biomass determination (60oC for 5 days). A cover crop shoot count was conducted for each monolith with each tiller considered as a shoot for the grasses (barley, wheat, triticale). After cover crop shoot harvesting, a photo was then taken of each soil monolith with remaining weed biomass. A weed score was assigned to each image by one trained researcher with a score 1 low weeds to 5 high weed presence.</p> </li> <li> <p>Soil monoliths were the soaked briefly in water and then the soil washed using a hose keeping the roots. Roots were then scanned on an Epson Expression 12000XL Photo Scanner with transparency unit. Images labeled with "_part" were samples with too many roots for scanning and so were separately weighed. Dry root biomass was taken for the scanned and unscanned roots separately. Root length was determined from images using software RhizoVision Explorer (https://doi.org/10.5281/zenodo.4095629), total root length was estimated using scanned root length and scanned dry biomass with unscanned root biomass.</p> </li> <li> <p>Along each cover crop plot a 10ft trench was dug using a Yanmar Excavator Vi020-6 perpendicular to the row with each trench fully bisecting the plot. Trench was one bucket wide (19 inches) and approximately 36 inches deep in the middle of the row. The five deepest roots that could be observed in the trench wall was measured manually with a tape measure for each cover crop. A garden trowel and shovel were used to excavate and confirm roots in trench wall.</p> </li> <li> <p>Data was analyzed using R Statistics script and raw data used for data processing and figure generation (2021PlantHavenCovercrop_dataprocessing.R). PCA analysis was conducted using the “FactoMineR” package (Husson <em>et al</em>. 2019) to explore the relationships between the traits within the dataset and clustered by family.</p> </li> </ul> </li> </ul> <p>Individual ZIP file contents:</p> <ul> <li><code><strong>2021PlantHavenCovercrop_CanopyImages.zip</strong></code> – Raw canopy images, processed percent green cover images, and Jupiter notebook python script (2021PlantHavenCovercrop_ImageBatchColorThreshold.ipynb).</li> <li><code><strong>2021PlantHavenCovercrop_RootFlatbedImages.zip</strong></code> – Raw flatbed root scans of cover crops and processed images using RhizoVision Explorer.</li> <li><code><strong>2021PlantHavenCovercrop_SoilMonolithWeedImages.zip</strong></code> – Images of soil monoliths after cover crop shoot biomass was removed.</li> <li><code><strong>2021PlantHavenCovercrop_dataprocessing.zip</strong></code> – R Statistics script and raw data used for data processing and figure generation (2021PlantHavenCovercrop_dataprocessing.R).</li> <li><code><strong>2021PlantHavenCovercrop_ShootPhotogrammetry.zip</strong></code> – 3D models of cover crop shoots from excavated soil monoliths. The .bin files can be opened using CloudCompare app.</li> </ul> <p> </p> <p> </p>
Data for the manuscript 'Cover crop root morphology rather than quality controls the fate of root and rhizodeposition C into distinct soil C pools'
<p><strong>Data for manuscript</strong></p> <p>The data provided in the present document corresponds to the manuscript:</p> <p>Engedal, T., Magid, J., Hansen, V., Rasmussen, J., Sørensen, H., Jensen, L. S. (2023): Cover crop root morphology rather than quality controls the fate of root and rhizodeposition C into distinct soil C pools. <em>Global Change Biology, in press</em>.</p> <p> </p> <p><strong>Short abstract</strong></p> <p>In order to investigate the fate of cover crop-derived belowground C as rhizodeposition and, over time, into the distinct soil organic carbon pools of particulate- and mineral-associated organic carbon (POC and MAOC), a column trial was esblished with 0.25 m top soil and 0.25 m sub soil. Four cover crops were grown for 3 months and 14CO2-labelled twice a week. Four out of eight replicate columns were destructively harvested to quantify root C and the carbon lost via rhizodeposition in absolute (qClvR) and relative terms (%ClvR) in bulk soil and rhizosphere soil from top- and subsoil (t1). The other four replicate columns were harvested for undisturbed incubation for one year, before final sampling (t2). Bulk soil from both sampling times were subject to a simple fractionation protocol by size, where particles larger from 50 microns were assigned to POC and smaller than 50 microns assigned to MAOC after dispersion in NaHMP. All fractions were dried, weighed and analyzed for 14C activity as disintegrations per minute (DPM).</p> <p> </p> <p><strong>Further details</strong></p> <p>Column ID 1-16 refer to columns sampled at t1, while column ID 17-32 refer to columns sampled at t2. Underlying assumptions and detailed descriptions of the different fractions are to be found in the manuscript.</p>
Stimulated saprotrophic fungi in arable soil extend their activities to the rhizosphere and root microbiomes of crop seedlings
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Overyielding is accounted for partly by plasticity and dissimilarity of crop root traits in maize/legume intercropping systems
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Nitrogen fixation and crop productivity enhancements codriven by intercrop root exudates and key rhizosphere bacteria
<p>1. Crop diversity management is widely used to increase agricultural productivity and sustainability. Recent studies have demonstrated that maize root exudates can drive interspecific facilitation to enhance N<sub>2</sub> fixation of bean in intercropping systems. However, the mechanisms of N<sub>2</sub> fixation enhancement stimulated by root exudates in the intercropping systems remain unclear.</p> <p>2. Four experiments were designed to provide a progressively deeper understanding of how root exudates stimulate microbial-mediated N<sub>2</sub> fixation. First, the effects of faba bean/maize intercropping on yields and soil microbial communities were determined in a field experiment. Second, root-derived interspecific facilitation was evaluated using a root partitioning approach. Third, the key microbial taxa in the faba bean rhizosphere were traced using<sup> 13</sup>C-labeled maize root exudates. Fourth, the codriven mechanism of maize root exudates and microorganisms in the faba bean rhizosphere were explored.</p> <p>3. Faba bean/maize intercropping with maize residue return increased the yields of faba bean (26%), maize (27%), and broccoli (9.1%) compared to that under monocropping. Nodulin-like 4 (NODL4), chalcone-flavanone isomerase (CFI), and early nodulin-like (ENODL2) gene expression in faba bean roots intercropped with maize increased by 1.5-2.3-fold compared to that observed under monoculture. More than half of the N<sub>2</sub> fixation of faba bean increase under intercropping was due to interactions with microorganisms. Nine key bacterial genera in the faba bean rhizosphere were identified by <sup>13</sup>C-DNA based stable isotope probing analysis. Among them <i>Agromyces</i>, <i>Arthrobacter</i>, <i>Bacillus</i>, <i>Lysobacter</i>, and <i>Paenibacillus</i> directly fix N<sub>2</sub>, while <i>Gemmatimonas</i>, <i>Heliobacillus</i>, <i>Natronocella</i>, and <i>Sorangium</i> increase the N<sub>2</sub> fixation capacity of Azotobacter by providing additional carbon sources. These key bacteria triggered by maize root exudates played an important role in the rhizosphere facilitation of intercropping.</p> <p>4. <i>Synthesis and applications</i>. We demonstrated a novel root-root facilitation of N<sub>2</sub> fixation and increased crop yields codriven by root exudates and rhizosphere bacteria under faba bean/maize intercropping, and nine key bacteria associated with this process were identified by <sup>13</sup>C-DNA based stable isotope probing. We recommend the adoption and optimization of intercropping systems with residue return to reduce the shortcomings of continuous cropping and to increase the sustainability of crop <a>production</a>.</p>
Data from: No evidence of foliar disease impact on crop root functional strategies and soil microbial communities: What does this mean for organic coffee?
<p><span>Global climate change is increasing pest and pathogen pressures on plant communities, deteriorating optimal plant functioning. In plant communities, root functional trait expression and microbial communities are important indicators of plant functioning belowground, and, when confronted with pathogens aboveground, can simultaneously reflect plant defence strategies. Yet, while research is continuing to emerge on the response of root functional traits and microbial processes to pathogens aboveground, little work has investigated these interactions in tree-crops, or the role organic amendments play in moderating these relationships. The main objective of this study is to disentangle the dynamic effects of pathogens and amendments on root functional traits (i.e., specific root length and area, root diameter, root length density, root nitrogen, and root carbon to nitrogen ratio) and root endophytic fungal communities. As a model, we use <em>Coffea arabica </em>(coffee) variety Caturra along a gradient of Coffee Leaf Rust – a foliar disease prominent in coffee systems – under contrasting but widespread amendment regimes in biodiverse agroforestry systems. We found that root trait expression varies along established conservation and collaboration gradients, where fungal endophyte community composition varies significantly as a function of root traits. Belowground resource acquisition strategies do not change with foliar disease incidence, suggesting they may be decoupled. Rather, amendment regimes </span>differentially shape root trait expression and microbial communities<span>, where coffee plants under organic amendments, regardless of foliar disease incidence, expressed greater acquisitive traits and enhanced collaboration with symbiotic fungi. </span>This is an important first step in disentangling the dynamic inter-relationships between plant traits, endophytes, and pathogens, generating new questions on the role of amendments in sustainable pathogen management in biodiverse agroecosystems.</p> <p> </p>
Decomposing cover crops modify root-associated microbiome composition and disease tolerance of cash crop seedlings
<p>The assembly of root-associated microbes during the seedling stage has strong impact on subsequent performance of crops. Major factors influencing this assembly are crop species identity and composition of potential root-colonizing microbes in the bulk soil. The latter can be modified by soil management, such as organic amendments. The incorporation of residues of cover crops before the start of the growing season of cash crops presents an interesting option for steering of root-associated seedling microbiomes as there is a wide range of cover crops species with different properties available for farmers.</p> <p>In a greenhouse study, we examined the effect of soil amendments with milled shoot and root materials of seven cover crop species (niger seed, phacelia, rapeseed, radish, vetch, black oat and buckwheat) on the soil nitrogen and biomass of seedlings of four cash crop species (asparagus, carrot, onion and sugar beet) and their root-associated bacteria and fungi. Field-grown cover crops material used for the study was collected at two time points (before and after winter) which had strong impact on plant elemental composition. Since the soil used for the study was a mixture of sandy arable soils with a history of soil-borne fungal diseases (Fusarium and Rhizoctonia), we also examined whether decomposing cover crop residues had an influence on the severity of damping-off diseases.</p> <p>Within the context of a strong selection of root-associated microbes by cash crop species, we found significant modifying effects by cover crop materials. High-quality residues (with low C/N ratio) caused profound shifts within root-associated Proteobacteria and increases in relative abundance of certain microbial groups such as Bacillaceae and Mortierellomycetes. These changes coincided with differences in establishment and survival of cash crop seedlings. This indicates that fine-tuning of cover crops amendments for different cash crops is required to realize enhanced functioning of root microbiomes.</p>
DRAM raw annotations for "Cover Crop Root Exudates Impact Soil Microbiome Functional Trajectories in Agricultural Soils" Seitz et al 2024
<p>Additional File 5: <span>Raw DRAM MAG annotations. </span></p>
Decomposing cover crops modify root-associated microbiome composition and disease tolerance of cash crop seedlings
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Data from: No evidence of foliar disease impact on crop root functional strategies and soil microbial communities: What does this mean for organic coffee?
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Nitrogen fixation and crop productivity enhancements codriven by intercrop root exudates and key rhizosphere bacteria
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Quantitative phenotyping of plant roots using spectral electrical impedance tomography: a rhizotron study on maize and bean crops with optimized measurement design
<div> <h1>Data repository</h1> <br> <div>In this repository, the data accompanying the publication "Quantitative phenotyping of plant roots using spectral electrical impedance tomography: a rhizotron study on maize and bean crops with optimized measurement design", published in "Plant Methods", is stored.</div> <div> </div> <div>The publication can be found at https://doi.org/10.1186/s13007-024-01247-7</div> <br> <h2>Dataset structure</h2> <br> <div>The dataset is structured in the following directories:</div> <br> <h3>complex resistivity inversions</h3> <br> <div>The folder "new_filter_approach_v4_allfreqs", contains all inversion results for frequencies from 0.79 Hz to 1 kHz, sorted by plant name. The data are given as tomodirs (see https://github.com/geophysics-ubonn/crtomo_tools for importing tomodir data within a python framework).</div> <br> <div>In the folder "scatterdata_from_invs", electrical parameters from the inversions are given for x-y-locations in the grid.</div> <br> <h3>dd_fit_results_all_cells</h3> <br> <div>In this folder, the results of the Debye-Decomposition for all cells in the grid are given, together with the x-y-locations in the grid.</div> <br> <h3>dd_fit_results_only_plants</h3> <br> <div>In this folder, the results of the Debye-Decomposition for all cells within the root zone in the grid are given, together with the x-y-locations in the grid.</div> <br> <h3>grid</h3> <br> <div>Contains all files to construct the inversion grid using crtomo_tools.</div> <div> </div> <br> <h3>images_plants</h3> </div> <div> <div> </div> <div>Contains photos for all measured plants, labeled according to the datasets in the manuscript.</div> <br> <div> <h3>impedance_data</h3> </div> <br> <div>In the folder "uncorrected", the raw impedance measurements for all plants are saved (only the correction for the resistivity magnitude is applied to account for the mismatch of 2D inversion and 3D measurement). The folder "corrected" containst the imepdance data that was corrected with background water measurements. The file "md_data_impedance.csv" contains the contact resistances and leakage currents for all measurements.</div> <br> <h3>optimization</h3> <br> <div>This folder contains the results for the synthetic modelling using an optimized (OPT_full) and reduced (OPT_middle) scheme, as described in the manuscript. As for the experimental results, inversions are given in tomodir format. The file "exp.csv" contains the xy-coordinates of the outline of the synthetic polarization signature.</div> <br> <h3>plots</h3> <br> <div>Contains all plots that are shown in the manuscript.</div> <br> <h3>root_data</h3> <br> <div>In the folder "root_area_polygons", the xy-coordinates of the outline of each root system in the rhizotron experiments, extracted from images of the front of the rhizotron, are saved. The file "extracted_elec_params_from_area_v4.csv" contains electical parameters from the inversion results for a number of frequencies for all cells that lie within the root area polygons. The file "Root_traits_final.csv" contains the individually measured root traits via weighing and scanning.</div> <br> <h3>scripts</h3> <br> <div>The scripts "0_plot_methods.py", "1_plot_results.py", "2_plot_discussion.py" and "3_plot_supplementary.py" create all plots shown in the manuscript and save them in the "plots"-directory.</div> </div> <p> </p> <p> </p>
Data from: Cover crop root contributions to soil carbon in a no-till corn bioenergy cropping system
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