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698 results for “Soybean”
Fig. 1 in Feeding behavior of Aphis glycines (Hemiptera: Aphididae) on soybeans exhibiting antibiosis, antixenosis, and tolerance resistance
Fig. 1. Mean number of potential drops by Aphis glycines on soybean genotypes for a 15 h (900 min) period.
Fig. 2 in Feeding behavior of Aphis glycines (Hemiptera: Aphididae) on soybeans exhibiting antibiosis, antixenosis, and tolerance resistance
Fig. 2. Mean duration of sieve element phase by Aphis glycines on soybean genotypes for a 15 h (900 min) period.
Fig. 2 in External marking and behavior of early instar Helicoverpa armigera (Lepidoptera: Noctuidae) on soybean
Fig. 2. Percentage of Helicoverpa armigera larvae feeding on soybean tissues at different periods of the d. Botucatu, São Paulo, Brazil.
Fig. 1 in External marking and behavior of early instar Helicoverpa armigera (Lepidoptera: Noctuidae) on soybean
Fig. 1. Helicoverpa armigera second instar larvae marked with luminous powder red during diurnal evaluation (A), and its movement and visualization during nocturnal evaluation (B) on soybean plants.
Fig. 1 in Phenological stages of a soybean crop affect the number of mating pairs and egg load in Rhyssomatus nigerrimus (Coleoptera: Curculionidae) females under natural conditions
Fig. 1. Effect of soybean crop phenological stages on matings per linear meter of Rhyssomatus nigerrimus pairs.Estimated values are 95% confidence intervals ±SE.
Fig. 2 in Phenological stages of a soybean crop affect the number of mating pairs and egg load in Rhyssomatus nigerrimus (Coleoptera: Curculionidae) females under natural conditions
Fig. 2. Effect of time of day on matings per linear meter of Rhyssomatus nigerrimus copulating in the R7 phenological stage in a soybean crop. Estimated values are 95% confidence intervals ±SE.
Fig. 2 in Trapping soybean looper (Lepidoptera: Noctuidae) in the southeastern USA and implications for pheromone-based research and management
Fig. 2. Mean number of Ctenoplusia oxygramma male moths captured at each of 3 trial locations where they were recorded as present. Note: Bio Pseudoplusia lures were not used at the LA-Crowley location, were installed at the LA-Ben Hur location on 2 Aug 2019, and were installed at the FL-Jay location for the entire trial period.
Fig. 1 in Trapping soybean looper (Lepidoptera: Noctuidae) in the southeastern USA and implications for pheromone-based research and management
Fig. 1. Mean number of Chrysodeixis includens male moths captured at each of 5 trial locations. Note: Bio Pseudoplusia lures were not used at the LA-Crowley or MS-Kiln locations, were installed at the LA-Ben Hur location on 2 Aug 2019 and at the MS-Starkville location on 31 Jul 2019, and were installed at the FL-Jay location for the entire trial period.
RNA oxidation in soybean seedlings in optimal conditions and under Cd treatment
<p>The project aims at identification of oxidized (8-hydroxyguanosine, 8-OHG, 8-oxoG enriched) transcripts in soybean seedlings grown in control (optimal) conditions and exposed to Cd at the concentration 10 mg/l for 2 h. </p> <p>The transcripts were identified through Illumina sequencing. </p> <p>The raw data are submitted in the NCBI database under the proejct: <a href="https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1158244">PRJNA1158244</a>.</p> <p>The genes were annotated to soybean genome derived from Ensembl Plants (http://plants.ensembl.org/index.html) using featureCounts.</p> <p>The list of identified genes is included in the attached Excel file. </p> <p> </p>
Alphafold2 and AlphaFold-Multimer Predicted Interactions of Soybean Proteins with Macrophomina phaseolina Effectors reveals putative protease inhibitors and SUSS effectors.
<p> </p> <ul> <li> <p><strong>Kunitz Monomer Prediction</strong>:</p> <ul> <li><strong>Data</strong>: Analysis of soybean Kunitz proteins.</li> <li><strong>Details</strong>: Detected on the apoplast at 3 days post-infection with <em>Macrophomina phaseolina</em>.</li> <li><strong>File</strong>: <code>KUNITZ_monomers_outputdir.zip</code></li> </ul> </li> <li> <p><strong>Uncharacterized M. phaseolina Protein Monomer Prediction</strong>:</p> <ul> <li><strong>Data</strong>: Predictions for uncharacterized proteins.</li> <li><strong>Details</strong>: Detected on the apoplast at 3 days post-infection.</li> <li><strong>File</strong>: <code>uncharacterised_proteins_SUSS_effectoroutputdir.zip</code></li> </ul> </li> </ul> <ul> <li> <p><strong>Positive Validation Set</strong>:</p> <ul> <li><strong>Data</strong>: Experimental verification of protein-inhibitor pairs.</li> <li><strong>Details</strong>: Pairs include experimentally verified interactions, specifically proteins and inhibitors, but lack resolved crystal structures.</li> <li><strong>File</strong>: <code>existing_non_existinpairs_Validation_outputdir.zip</code></li> </ul> </li> </ul> <ul> <li> <p><strong>Soybean Serine Protease-Kunitz Interaction</strong>:</p> <ul> <li><strong>Data</strong>: Interactions between soybean serine proteases and Kunitz proteins.</li> <li><strong>Details</strong>: Analyzed in the apoplastic space at 3 days post-infection.</li> <li><strong>File</strong>: <code>glycine max_Serine protease_Vs_Gmaxkunitz_outputdir.zip</code></li> </ul> </li> </ul> <ul> <li> <p><strong>Cysteine Protease without Pro-domain-MoErs-like effector Interaction</strong>:</p> <ul> <li><strong>Data</strong>: Interactions involving cysteine proteases.</li> <li><strong>Details</strong>: Rice RD21 and soybean cysteine proteases with pro-domains removed interacting with <em>MoErs1</em> and <em>MoErs1</em>-like M.phaseolina effectors.</li> <li><strong>File</strong>: <code>AF2-Multimer_RD21&GmaxCproteases_MoERS1_screening_outputdir.zip</code></li> </ul> </li> </ul> <ul> <li> <p><strong>Fungal Serine Protease-Kunitz Interaction</strong>:</p> <ul> <li><strong>Data</strong>: Interactions between <em>Macrophomina phaseolina</em> serine proteases and soybean Kunitz proteins.</li> <li><strong>Details</strong>: Evaluated in the apoplastic space at 3 days post-infection.</li> <li><strong>File</strong>: <code>fungalSerineprotease_Vs_Gmax_kunitzoutputdir.zip</code></li> </ul> </li> <li> <p><strong>Negative Validation Set</strong>:</p> <ul> <li><strong>Data</strong>: Known non-interacting pairs.</li> <li><strong>Details</strong>: Non-interacting pairs of serine proteases-chitinases that are not resolved as crystal structures</li> <li><strong>File</strong>: <code>Gmax_Serineprotease_Vs_Gmaxchitinases_Validation_outputdir.zip</code></li> </ul> </li> </ul>
30 m annual soybean area map in China from 2000 to 2022
<p><span>China is the world’s largest consumer and importer of soybeans, with rising domestic demand driving the expansion of production and</span><span> </span><span>cultivation areas since</span><span> 2000</span><span>. Knowing the spatial distribution of soybean, including the trends and interannual variability, is essential for yield estimation, agricultural planning, and </span><span>ensuring </span><span>national food security. However, a high-precision, long-term, national-scale spatial dataset for soybean cultivation area</span><span>s</span><span> </span><span>of China remain</span><span>s</span><span> </span><span>unavailable. To address this gap, we developed the China Soybean Area (ChinaSoyA30m) dataset at a 30-m resolution—covering the years 2000–2022 at the national scale, using Landsat imagery and a combined phenology-based and machine learning approach. We analyzed time-series phenological characteristics of major crops across nine major agricultural regions in China and automatically generated annual training samples for supervised classifiers through a GWCCI-derived unsupervised method. To enhance the accuracy of these samples, we applied gap statistics, <em>K</em>-means clustering, and spectral angle mapping techniques to minimize noise and improve classification reliability. The supervised classification was conducted using a multi-random forest fusion strategy on the Google Earth Engine (GEE) platform, leveraging dense Landsat-5/7/8/9 data to generate yearly soybean maps. Our ChinaSoyA30m dataset showed strong correlation with official statistics at provincial, prefectural, and county levels, with R<sup>2 </sup>values of 0.95, 0.89, and 0.80, respectively.</span></p>
Figure 1. Plant tissue-culture growth chamber Percival. A in Survivorship of soybean aphid biotypes (Hemiptera: Aphididae) on winter hosts, common and glossy buckthorn
Figure 1. Plant tissue-culture growth chamber Percival. A) Soybean plants maintained in a plant growth chamber for 21 days before placed Rhamnus cathartica. B) Leaf of R. cathartica infested with soybean aphid biotype 1. C) Leaf of Frangula alnus with soybean aphid biotype 4.
Figure 3 in Survivorship of soybean aphid biotypes (Hemiptera: Aphididae) on winter hosts, common and glossy buckthorn
Figure 3. Males of soybean aphid, Aphis glycines, biotype 3. A) Alate male. B) Apterous male with sclerites on thorax. C) Apterous male without sclerites on thorax. The slides mounted images were magnified to 64.3x.
Figure 2 in Survivorship of soybean aphid biotypes (Hemiptera: Aphididae) on winter hosts, common and glossy buckthorn
Figure 2. Adult morphs and eggs of soybean aphid, Aphis glycines, biotype 3 on Rhamnus cathartica. A) Gynopara. B) Ovipara. C) Dorsal view of apterous male. D) Ventral view of apterous male. E) Eggs on bud.
A multispectral UAV Imagery dataset of wheat, soybean, and barley crops in East Kazakhstan
<p>This study introduces a dataset of crop imagery captured during the 2022 growing season in the Eastern Kazakhstan region. The images were acquired using a multispectral camera mounted on an unmanned aerial vehicle (DJI Phantom 4). The agricultural land, encompassing 27 hectares and cultivated with wheat, barley, and soybean, was subjected to five aerial multispectral photography sessions throughout the growing season. This facilitated thorough monitoring of the most important phenological stages of crop development in the experimental design, which consisted of 27 plots, each covering one hectare. The collected imagery underwent enhancement and expansion, integrating a sixth band that embodies the normalized difference vegetation index (NDVI) values, in conjunction with the original five multispectral bands (Red, Green, Blue, Infrared, and Near Infrared). This amplification enables a more effective evaluation of vegetation health and growth, rendering the enriched dataset a valuable resource for the progression and validation of crop monitoring and yield prediction models, as well as for the exploration of precision agriculture methodologies.</p>
A multispectral UAV Imagery dataset of wheat, soybean, and barley crops in East Kazakhstan
<p>This study introduces a dataset of crop imagery captured during the 2022 growing season in the Eastern Kazakhstan region. The images were acquired using a multispectral camera mounted on an unmanned aerial vehicle (DJI Phantom 4). The agricultural land, encompassing 27 hectares and cultivated with wheat, barley, and soybean, was subjected to five aerial multispectral photography sessions throughout the growing season. This facilitated thorough monitoring of the most important phenological stages of crop development in the experimental design, which consisted of 27 plots, each covering one hectare. The collected imagery underwent enhancement and expansion, integrating a sixth band that embodies the normalized difference vegetation index (NDVI) values, in conjunction with the original five multispectral bands (Blue, Green, Red, Red Edge and Near Infrared Red). This amplification enables a more effective evaluation of vegetation health and growth, rendering the enriched dataset a valuable resource for the progression and validation of crop monitoring and yield prediction models, as well as for the exploration of precision agriculture methodologies.</p>
A multispectral UAV Imagery dataset of wheat, soybean, and barley crops in East Kazakhstan
<p>This study introduces a dataset of crop imagery captured during the 2022 growing season in the Eastern Kazakhstan region. The images were acquired using a multispectral camera mounted on an unmanned aerial vehicle (DJI Phantom 4). The agricultural land, encompassing 27 hectares and cultivated with wheat, barley, and soybean, was subjected to five aerial multispectral photography sessions throughout the growing season. This facilitated thorough monitoring of the most important phenological stages of crop development in the experimental design, which consisted of 27 plots, each covering one hectare. The collected imagery underwent enhancement and expansion, integrating a sixth band that embodies the normalized difference vegetation index (NDVI) values, in conjunction with the original five multispectral bands (Blue, Green, Red, Red Edge and Near Infrared Red). This amplification enables a more effective evaluation of vegetation health and growth, rendering the enriched dataset a valuable resource for the progression and validation of crop monitoring and yield prediction models, as well as for the exploration of precision agriculture methodologies.</p>
A multispectral UAV Imagery dataset of wheat, soybean, and barley crops in East Kazakhstan
<p>This study introduces a dataset of crop imagery captured during the 2022 growing season in the Eastern Kazakhstan region. The images were acquired using a multispectral camera mounted on an unmanned aerial vehicle (DJI Phantom 4). The agricultural land, encompassing 27 hectares and cultivated with wheat, barley, and soybean, was subjected to five aerial multispectral photography sessions throughout the growing season. This facilitated thorough monitoring of the most important phenological stages of crop development in the experimental design, which consisted of 27 plots, each covering one hectare. The collected imagery underwent enhancement and expansion, integrating a sixth band that embodies the normalized difference vegetation index (NDVI) values, in conjunction with the original five multispectral bands (Blue, Green, Red, Red Edge and Near Infrared Red). This amplification enables a more effective evaluation of vegetation health and growth, rendering the enriched dataset a valuable resource for the progression and validation of crop monitoring and yield prediction models, as well as for the exploration of precision agriculture methodologies.</p>
Data from: Subtle responses of soil bacterial communities to corn-soybean-wheat rotation
<p>Crop rotational diversity can improve crop productivity and soil health and boost soil microbial diversity. This research hypothesized that a three-year rotation of corn-soybean-wheat (CSW), compared to a two-year corn-soybean (CS) rotation, would result in a more diverse and more complex soil bacterial community, together with a greater abundance of beneficial bacteria. This was evaluated in a replicated experiment established in 2013 at two locations in Ohio (USA). The soil bacterial communities under soybean were compared between CS and CSW, at both studied sites, in 2018 and 2019, through 16S rDNA amplicon metabarcoding. </p>
Dataset for "Cover crop inclusion and residue retention improves soybean production and physiology in drought conditions"
<p>Data and code for "Cover crop inclusion and residue retention improves soybean production and physiology in drought conditions"</p> <p><strong>CONTEXT: </strong>Soybean (<em>Glycine max</em> (L.) Merr.) planting has increased in central and western North Dakota despite frequent drought occurrences that limit productivity. Soybean plants need high photosynthetic and transpiration rates to be productive, but they also need high water use efficiency when water is limited. Retaining crop residues and including cover crops in crop rotations are management strategies that could improve soybean drought resilience in the northern Great Plains. </p> <p><strong>OBJECTIVE</strong>: We aimed to examine how a management practice that included cover crops and residue retention impacts agronomic, ecosystem water and carbon dioxide flux, and canopy-scale physiological attributes of soybeans in the northern Great Plains under drought conditions. </p> <p><strong>METHODS</strong>: We compared two soybean fields over two years with business-as-usual and aspirational management that included residue retention and cover crops during a drought year. This comparison was based on yield, aboveground biomass, Phenocam images, and fluxes from eddy covariance and ancillary measurements. These measurements were used to derive meteorological, physical, and physiological attributes with the ‘big leaf’ framework. </p> <p><strong>RESULTS: </strong>Soybean yields were 29% higher under drought conditions in the field managed in a system that included cover crops and residue retention. This yield increase was caused by extending the maturity phenophase by 5 days, increasing agronomic and intrinsic water use efficiency by 27% and 33%, respectively, increasing water uptake, and increasing the rubisco-limited photosynthetic capacity (V<sub>cmax25</sub>) by 42%.</p> <p><strong>CONCLUSIONS:</strong> The inclusion of cover crops and residue retention into a cropping system improved soybean productivity because of differences in water use, phenology timing, and photosynthetic capacity.</p> <p><strong>IMPLICATIONS:</strong> These results suggest that farmers can improve soybean productivity and yield stability by incorporating cover crops and residue retention into their management practices because these practices allow soybean plants to shift to a more aggressive water uptake strategy.</p> <p><strong>Code:</strong></p> <p><strong>1_phenocam.rmd: </strong>Code to download Phenocam data and identify phenophase transition dates.</p> <p><strong>2_Daily_CO2_Water_Fluxes.Rmd: </strong>Code to analyze daily carbon and water fluxes (Figure 1, 2 3 and Table 2).</p> <p><strong>3_Inferring_LAI_and_Height.Rmd: </strong>Code to calculate the predicted LAI and height for each day. The output is used in the big-leaf framework.</p> <p><strong>4_Big_Leaf.Rmd: </strong>Code for the big-leaf ecophysiology estimates (Figure 4, 5 and 6; Table 3 and 4).</p> <p><strong>4_Data_Dictionary_Vairables:</strong> Code to identify the data dictionary variables. </p> <p> </p>
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