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698 results for “Soybean”
Data from: Subtle responses of soil bacterial communities to corn-soybean-wheat rotation
Open the record for dataset details and reuse information.
Responses of agricultural weed community in a corn-soybean intercrop
This dataset was created as a part of an experiment which used a soybean-corn intercrop system for examining how the community structure of agricultural weeds changes with fertilization and with the identity of the crop species. The composition of the weed flora in intercrops was compared with the weed flora of the respective sole crops, with and without fertilization. The experiment was conducted at Kellogg Biological Station (KBS) in southwestern Michigan, USA, in 1993.
Local plant diversity and soybean biological control 2011 Harvest Measures:Biodiversity II: Effects of Plant Biodiversity on Population and Ecosystem Processes
Biodiversity II (E120) is designed to determine how the number of plant species affects the dynamics of ecological processes at the population, community, and ecosystem levels. By experimentally manipulating the number of species and the kinds of species, the amount of plant growth and the change from year to year, that result can be examined. Plots are large (9m x 9m actively maintained) and well-replicated, allowing responses of plant pathogens, insect herbivores, seed predators, soil parameters, invasive plant species and other variables to also be studied. Plots were seeded in May 1994 to have 1, 2, 4, 8, or 16 species, with roughly 30 replicates of each diversity level. The species composition of each plot was chosen by random draw from a pool of 18 grassland perennials that included four warm-season (C4) grasses, four cool-season (C3) grasses, four legumes, four non-legume forbs, and two woody species. All species occur in monoculture allowing comparison of responses of each species in monoculture to combinations of these same species. The experiment was established in 1994 by the lead investigators David Tilman, Peter Reich, Johannes Knops, and David Wedin. Experiment 120 is similar to Experiment 123, but it uses larger plots to provide a large capacity for long-term subexperiments.
Local plant diversity and soybean biological control 2012 Aphid Surveys:Biodiversity II: Effects of Plant Biodiversity on Population and Ecosystem Processes
Biodiversity II (E120) is designed to determine how the number of plant species affects the dynamics of ecological processes at the population, community, and ecosystem levels. By experimentally manipulating the number of species and the kinds of species, the amount of plant growth and the change from year to year, that result can be examined. Plots are large (9m x 9m actively maintained) and well-replicated, allowing responses of plant pathogens, insect herbivores, seed predators, soil parameters, invasive plant species and other variables to also be studied. Plots were seeded in May 1994 to have 1, 2, 4, 8, or 16 species, with roughly 30 replicates of each diversity level. The species composition of each plot was chosen by random draw from a pool of 18 grassland perennials that included four warm-season (C4) grasses, four cool-season (C3) grasses, four legumes, four non-legume forbs, and two woody species. All species occur in monoculture allowing comparison of responses of each species in monoculture to combinations of these same species. The experiment was established in 1994 by the lead investigators David Tilman, Peter Reich, Johannes Knops, and David Wedin. Experiment 120 is similar to Experiment 123, but it uses larger plots to provide a large capacity for long-term subexperiments.
Forest cover and proximity decrease herbivory and increase crop yield via enhanced natural enemies in soybean fields
<p><span>Non-crop habitats are essential for sustaining biodiversity of beneficial arthropods in agricultural landscapes, which can increase ecosystem services provision and crop yield. However, their effects on specific crop systems are less clear, such as soybean in South America, where the responses of pests and natural enemies to landscape structure have only recently been studied. </span></p> <p><span>Here, we analyzed how native forest fragments at local and landscape scales influenced arthropod communities, herbivory, and yield in soybean fields in central Argentina. To do this, we selected soybean fields located in agricultural landscapes with varying proportions of forest cover. At two distances (10 and 100m) from a focal forest fragment, we sampled natural enemy and herbivore arthropods, and measured soybean herbivory and yield. We focused on herbivore diversity, abundance of key soybean pests in the region (caterpillars and stink bugs), and their generalist and specialist natural enemies.</span></p> <p><span>Higher abundance of predators, lower herbivory rates, and increased yield were found near forests, while overall forest cover in the landscape was positively related with parasitoid and stink bug abundance, soybean yield, and negatively with herbivory. Moreover, yield was positively linked to richness and abundance of generalist and specialist enemies and independent of herbivory according to piecewise Structural Equation Models. </span></p> <p><span><i>Synthesis and applications. </i>Our results show positive effects of native forests on biodiversity and yield in soybean crops, highlighting the need for conservation of forest fragments in agricultural landscapes. Moreover, the relation between natural enemies and crop yield suggests that Chaco forests support a diverse and abundant community of natural enemies that can provide sustained levels of ecosystem services and result in positive effects for farmers.</span></p>
Satellite Data for Corn and Soybean Fields + Other Land Cover: Illinois, US, 2017-2019
<p>These are the datasets associated with the paper:<br> Hannah Kerner, Ritvik Sahajpal, Sergii Skakun, Inbal Becker-Reshef, Brian Barker, Mehdi Hosseini, Estefania Puricelli, and Patrick Gray. 2020. Resilient In-Season Crop Type Classification in Multispectral Satellite Observations using Growth Stage Normalization. In <em>KDD ’20: ACM Special Interest Group (SIG) on Knowledge Discovery and Data Mining Conference Workshops</em>, August 23–27, 2020, San Diego, CA. </p> <p>The code that uses these datasets can be found at: <a href="https://github.com/nasaharvest/croptype-mapping-gsn/tree">https://github.com/nasaharvest/croptype-mapping-gsn</a></p>
Feeding black soldier fly larva to replace soybean meal in growing pigs – responses in the amine metabolites in blood
<p>Insect meals from black soldier fly (<em>Hermetia illucens</em>; BSF) larvae as dietary protein source have the ability to deliver nutrients, particularly dietary amino acids (AA) and could provide functional properties that positively supports animal health and productivity. More knowledge, however, is needed to assess the impact of BSF based diet on gut and animal health. Sixteen male pigs with an average initial body weight of 34.9 ± 3.4 kg were randomly assigned to groups fed for three weeks with iso-caloric and iso-proteinaceous experimental diets prepared with either soybean meal (SBM) as reference protein source or with BSF, as single source of dietary protein. At the end of the feeding trial, blood plasma were collected to study the changes at systemic level in plasma amine metabolites as an effect of the experimental diet.</p>
Soybean Root Phenotype and Genotype Data from the Piney Purdue Agricultural Center (PPAC), Indiana
<p>This data repository contains records of root phenotypes collected in the Pinney Purdue Agricultural Center (PPAC) (Wanatah, Indiana, USA) on 24 soybean genotypes in 2022 along with their genotype information from the intersection of the BARCSoySNP6K and SoySNP50K assays.</p> <p>The repository contains the following files:<br>Bogati_soybean_root_phenotype_data1.xlsx</p> <p>6k_and_50k_geno.map</p> <p>6k_and_50k_geno.ped</p> <p>The map file contains chromosome number, SNP ID, Genetic Distance, and Base pair position.</p> <p>The ped file contains sample name (first two columns) with genotype data corresponding to the .map file beginning in column 7.</p> <p> </p> <p>Acknowledgements: To-Chia Ting, Luis Vargas, and Sajad Jamshidi assisted in the collection of root phenotype data. Chance Clark helped extract DNA for genotyping.</p> <p> </p>
Data on the soybean infiltration process utilizing LF-NMR
<div> <div> <div> <p>This paper employs low-field nuclear magnetic resonance (LF-NMR) technology to meticulously analyze and explore the intricate soybean infiltration process. The methodology involves immersing soybeans in distilled water, with periodic implementation of Carr-Purcell-Meiboom-Gill (CPMG) pulse sequence experiments conducted at intervals of 20 to 30 minutes to determine the relaxation time T<sub>2</sub>. Currently, magnetic resonance imaging (MRI) is conducted every 30 minutes. The analysis uncovers the existence of three distinct water phases during the soybean infiltration process: bound water denoted as T<sub>21</sub>, sub-bound water represented by T<sub>22,</sub> and free water indicated as T<sub>23</sub>. The evolution of these phases unfolds as follows: bound water T<sub>21</sub> displays a steady oscillation within the timeframe of 0 to 400 minutes; sub-bound water T<sub>22</sub> and free water T<sub>23</sub> exhibit a progressive pattern characterized by a rise-stable-rise trajectory. Upon scrutinizing the magnetic resonance images, it is discerned that the soybean infiltration commences at a gradual pace from the seed umbilicus. The employment of LF-NMR technology contributes significantly by affording an expeditious, non-destructive, and dynamic vantage point to observe the intricate motion of water migration during soybean infiltration. This dynamic insight into the movement of water elucidates the intricate mass transfer pathway within the soybean-water system, thus furnishing a robust scientific foundation for the optimization of processing techniques.</p> </div> </div> </div>
ChinaSoybean10:An Annual 10-m Soybean cropland Mapping Dataset in China from 2019 to 2022
<p>This dataset consists of soybean annual maps from 2019 to 2022 in China's main soybean producing areas with a pixel size of 10 m x 10 m, including Heilongjiang, Inner Mongolia, Jilin, Liaoning, Anhui, Henan, Shandong, Hubei, Jiangsu, and Sichuan.The maps use the ESPG: 4326 (WGS_1984) spatial reference system. The maps contain values of null and 1, representing non-crop land (including other crops) and soybeans. The maps are shown at the provincial administrative unit level, including the four prefecture-level cities in Eastern Inner Mongolia. You can analyze and visualize these maps with software like ArcGIS, QGIS, or similar applications.</p>
Integrated irrigation and nitrogen optimization is a resource-efficient adaptation strategy for US maize and soybean production
<p>These datasets were utilized to generate the figures and tables presented in the paper '<strong>Integrated irrigation and nitrogen optimization is a resource-efficient adaptation strategy for US maize and soybean production</strong><strong>'</strong>:</p> <p>(1) Fig.1.html: Code for generating Fig. 1.<br>(2) Fig.2.xlsx: Data for Fig. 2.<br>(3) Fig.3.xlsx: Data for Fig. 3.<br>(4) Fig.4.xlsx: Data for Fig. 4.<br>(5) Fig.5.xlsx: Data for Fig. 5.</p> <p>(6) Other .html files are Python code corresponding to different tables and figures.</p>
Data supporting "Can Long-Term Experiments Predict Real Field N and P Balance and System Sustainability? Results from Maize, Winter Wheat, and Soybean Trials Using Mineral and Organic Fertilisers"
<p>Data supporting "Can Long-Term Experiments Predict Real Field N and P Balance and System Sustainability? Results from Maize, Winter Wheat, and Soybean Trials Using Mineral and Organic Fertilisers" by Piccoli et al. (2021) Agronomy 2021, 11, 1472. https://doi.org/10.3390/agronomy11081472</p>
Data to accompany from: Effects of neonicotinoid seed treatments on wildbee populations and soybean and corn fields in eastern Ontario
<p><span>Neonicotinoid-coated corn and soybean seeds are a common crop in Canada and the US. A growing body of research is demonstrating that, through various exposure routes, neonicotinoids can impact a suite of non-target organisms including beneficial insects such as bees. However, to date, only a few studies have examined the effects of neonicotinoids in field settings. We assessed the relationship between agricultural crop soil neonicotinoid levels and wild bee abundance and diversity at 16 agricultural sites representing different soil neonicotinoid levels. We detected clothianidin at 11 sites, thiamethoxam at three sites; imidacloprid was not detected. Hedgerow and crop soils were consistent in terms of where clothianidin was detected; thiamethoxan was not detected in hedgerow soils. Based on model outcomes, fields with higher levels of soil neonicotinoids exhibited significantly lower wild bee abundance and diversity than those with low or no neonicotinoids detected. Crop soil neonicotinoid level, hedgerow floral resource abundance and crop type were consistent predictors of bee abundance across models; only neonicotinoid level and crop type were significant predictors of diversity. Our results are consistent with recent findings in the midwestern US, and underscore the potential risk of soil neonicotinoids to wild bee populations across regions and crop systems.</span></p>
144 prioritized genes of flooding-tolerance (FTgenes) in soybean
<p><span>Soybean [</span><em>Glycine max</em><span> (L.) Merr.] is one of the most important legume crops abundant in edible protein and oil in the world. Due to the drastic climate change, flooding, drought and unevenly distributed rainfall have gradually increased in terms of the frequency and intensity worldwide. In particular, severe flooding has caused extensive losses to soybean production. In light of the harsh situation, there has been an urge to breed strong soybean seeds with high flooding tolerance. We collected and integrated genetic data that relevant to flooding-tolerant responses in soybean from </span>multiple dimensional data sources. A step-function adjusted factor prioritization algorithm was proposed to prioritize these integrated genetic data. A total of 144 candidate genes of flooding-tolerance (FTgenes) in soybean were <span>prioritized, </span>using a cut-off threshold of combined score of 42,<span> from 36,705 test genes that collected from multidimensional genomic features linking to soybean flooding tolerance</span>. <span>Several validation results using independent samples from SoyNet, GWAS, SoyBase, </span><span>GO database</span><span> and transcriptome databases all exhibited excellent agreement, suggesting these 144 FTgenes were significantly superior than others.</span><span> </span><span>Our results provide valuable information, meaningful insight, and contribution to varieties selection of soybean</span><span>. The FTgenes demonstrated the potential for uncovering important insights underlying flooding-tolerant response in soybean in systems biology stuydies.</span></p>
Data for: An advanced systems biology framework of feature engineering for cold tolerance genes discovery from integrated omics and non-omics data in soybean
<p><span>Soybean [<em>Glycine max (L.) Merr.</em>] </span><span>serves as one of the most economically valuable crops globally, but it is sensitive to low temperatures during the crop growing season. Currently, agriculture around the world has faced more serious abiotic stresses due to climate change, so there is an urgent need to breed cold-tolerant cultivars to resist the changing environment. The cold-tolerant trait is a complex and quantitative trait controlled by multiple genes, environmental factors, and their interaction. A total of 56 soybean samples were used, including 28 resistant varieties and 28 susceptible varieties, in the field experiments. We selected 55 SNPs (which were mapped to 39 CTgenes) from the CTgenes for distinguishing cold-tolerant lines from cold-susceptible lines. The SNP data can be applied for soybean's cold-tolerant experiment, such as soybean marker-assisted selection, soybean varieties clustering, the systems biology analysis, and further validation. </span></p>
Figure 6 in Potential neuroprotective of trans-resveratrol a promising agent tempeh and soybean seed coats-derived against beta-amyloid neurotoxicity on primary culture of nerve cells induced by 2-methoxyethanol
Figure 6. Treatment Group with resveratrol standard after induced by Beta-Amyloid (10 x10).
Figure 3 in Potential neuroprotective of trans-resveratrol a promising agent tempeh and soybean seed coats-derived against beta-amyloid neurotoxicity on primary culture of nerve cells induced by 2-methoxyethanol
Figure 3. Treatment group: 2- ME + Resveratrol isolated from Tempeh (10 x10).
Figure 1 in Use of open-source object detection algorithms to detect Palmer amaranth (Amoronthus polmeri) in soybean
Figure 1. Examples of images collected for soybean in the VE-VC (A) and R2 (B) growth stages.
Raw Data from Cepea on Sugar, Fed Cattle, Coffee, and Soybean Price Indexes
<p>Data for the full series of the Sugar, Fed Cattle, Coffee, and Soybean Price Indexes were extracted from the Cepea Database, covering their series beginnings until 06-28-2023. Reproduction of the data in non-commercial circumstances is free. For commercial purposes, arrangements must be made with Cepea: <a rel="noreferrer">cepea@usp.br</a>.</p>
Fig 3 in Survival, growth, and biomass of brine shrimp (Artemia franciscana) fed with spirulina powder and soybean flour
Fig 3: The biomass of Artemia fed with different feeds at 21 days of rearing
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
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The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
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