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10 results for “sugar cane”
Fig. 2 in Population dynamics of pests and natural enemies on sugar cane grown in a subtropical region of Brazil
Fig. 2. Population dynamics of the natural enemies Harmonia axyridis, Doru lineare, and Billaea claripalpis in sugar cane from Feb 2013 to Jan 2015 in the municipality of Salto do Jacuí, Rio Grande do Sul State, Brazil. Arrows indicate the harvesting and budding periods.
Fig. 1 in Population dynamics of pests and natural enemies on sugar cane grown in a subtropical region of Brazil
Fig. 1. Population dynamics of sugar cane pests from Feb 2013 to Jan 2015 in the municipality of Salto do Jacuí, Rio Grande do Sul State, Brazil: (A) Means of internodes, attacked internodes, and number of Diatraea saccharalis larvae per culm; (B) Insects of Mahanarva fimbriolata per square m, and infested culms (%) by Saccharicoccus sacchari, and Melanaphis sacchari. Arrows indicate the harvesting and budding periods.
Fig. 2 in Host plant preference of Melanotus communis (Coleoptera: Elateridae) among weeds and sugar cane varieties found in Florida sugar cane fields
Fig. 2. Diagram of the larval host plant tests.
Fig. 1 in Host plant preference of Melanotus communis (Coleoptera: Elateridae) among weeds and sugar cane varieties found in Florida sugar cane fields
Fig. 1. Diagram of the adult host plant tests.
AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase 1 output data set: CLM-Crop sugar cane
<p>This is model output from CLM-Crop for sugar cane as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase 1 output data set.</p> <p>The data have been generated following the modeling protocol of Elliott et al. (2015) and has been used to evaluate the models (Müller et al., 2017). A data description paper has been published in Scientific Data (Müller et al. 2019).</p> <p>References:</p> <p>Elliott J, Müller C, Deryng D, Chryssanthacopoulos J, Boote KJ, Büchner M, Foster I, Glotter M, Heinke J, Iizumi T, Izaurralde RC, Mueller ND, Ray DK, Rosenzweig C, Ruane AC, and Sheffield J. 2015, The Global Gridded Crop Model intercomparison: data and modeling protocols for Phase 1 (v1.0). Geosci. Model Dev. 8, 261-277, doi:10.5194/gmd-8-261-2015</p> <p>Müller C, Elliott J, Chryssanthacopoulos J, Arneth A, Balkovic J, Ciais P, Deryng D, Folberth C, Glotter M, Hoek S, Iizumi T, Izaurralde RC, Jones C, Khabarov N, Lawrence P, Liu W, Olin S, Pugh TAM, Ray DK, Reddy A, Rosenzweig C, Ruane AC, Sakurai G, Schmid E, Skalsky R, Song CX, Wang X, de Wit A, and Yang H. 2017, Global gridded crop model evaluation: benchmarking, skills, deficiencies and implications, Geosci. Model Dev., 10, 1403-1422, doi: 10.5194/gmd-10-1403-2017</p> <p>Müller C, Elliott J, Kelly D, Arneth A, Balkovic J, Ciais P, Deryng D, Folberth C, Hoek S, Izaurralde RC, Jones CD, Khabarov N, Lawrence P, Liu W, Olin S, Pugh TAM, Reddy A, Rosenzweig C, Ruane AC, Sakurai G, Schmid E, Skalsky R, Wang X, de Wit A, and Yang H. 2019, The Global Gridded Crop Model Intercomparison phase 1 simulation dataset, Scientific Data, 6, 50, doi: 10.1038/s41597-019-0023-8</p>
AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase 1 output data set: LPJmL sugar cane
<p>This is model output from LPJmL for sugar cane as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase 1 output data set.</p> <p>The data have been generated following the modeling protocol of Elliott et al. (2015) and has been used to evaluate the models (Müller et al., 2017). A data description paper has been published in Scientific Data (Müller et al. 2019).</p> <p>References:</p> <p>Elliott J, Müller C, Deryng D, Chryssanthacopoulos J, Boote KJ, Büchner M, Foster I, Glotter M, Heinke J, Iizumi T, Izaurralde RC, Mueller ND, Ray DK, Rosenzweig C, Ruane AC, and Sheffield J. 2015, The Global Gridded Crop Model intercomparison: data and modeling protocols for Phase 1 (v1.0). Geosci. Model Dev. 8, 261-277, doi:10.5194/gmd-8-261-2015</p> <p>Müller C, Elliott J, Chryssanthacopoulos J, Arneth A, Balkovic J, Ciais P, Deryng D, Folberth C, Glotter M, Hoek S, Iizumi T, Izaurralde RC, Jones C, Khabarov N, Lawrence P, Liu W, Olin S, Pugh TAM, Ray DK, Reddy A, Rosenzweig C, Ruane AC, Sakurai G, Schmid E, Skalsky R, Song CX, Wang X, de Wit A, and Yang H. 2017, Global gridded crop model evaluation: benchmarking, skills, deficiencies and implications, Geosci. Model Dev., 10, 1403-1422, doi: 10.5194/gmd-10-1403-2017</p> <p>Müller C, Elliott J, Kelly D, Arneth A, Balkovic J, Ciais P, Deryng D, Folberth C, Hoek S, Izaurralde RC, Jones CD, Khabarov N, Lawrence P, Liu W, Olin S, Pugh TAM, Reddy A, Rosenzweig C, Ruane AC, Sakurai G, Schmid E, Skalsky R, Wang X, de Wit A, and Yang H. 2019, The Global Gridded Crop Model Intercomparison phase 1 simulation dataset, Scientific Data, 6, 50, doi: 10.1038/s41597-019-0023-8</p>
Sugar Cane
A piece of sugar cane, the main cash crop of th eWest Indies. Source: Objaverse 1.0 / Sketchfab
Sugar crop yield vs cane toads
<p>In 1935, cane toads (<i>Rhinella marina</i>) were brought to Australia to control insect pests. The devastating ecological impacts of that introduction have attracted extensive research, but the toads' impact on their original targets has never been evaluated. Our analyses confirm that sugar production did not increase significantly after the anurans were released, possibly because toads reduced rates of predation on beetle pests by consuming some of the native predators of those beetles (ants), fatally poisoning others (varanid lizards) and increasing abundances of crop-eating rodents (that can consume toads without ill-effect). In short, any direct benefit of toads on agricultural production (via consumption of insect pests) likely was outweighed by negative effects that were mediated via the toads' impacts on other taxa. Like the toad's impacts on native wildlife, indirect ecological effects of the invader may have outweighed direct effects of toads on crop production.</p>
Sugar crop yield vs cane toads
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NAET® Testing Devices in Detection of Hypersensitivity to Cane Sugar
ClinicalTrials.gov study NCT00292578. IPD Sharing: Not stated. Countries: 1. Publications: 0.
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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.
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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.
International Brain Laboratory public data
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.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.