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173 results for “sugarcane”
Sugarcane around Ribeirão Preto, São Paulo
<p>Photographs of sugarcane growing around Ribeirão Preto, São Paulo. From fieldnotes: The cane this trip was massive, some of it at least twelve feet tall, awaiting harvesting soon. I saw a few harvesting machines out in the distance, their tops poking up above the cane. I saw a truck hauling cut cane. The red soil was a strong contrast against the green. It really felt like the cane saturated the landscape.</p>
Sugarcane straw in the lab
<p>Photographs of bags of "palha em natura" (literally "natural straw") to be used in pretreatment experiments in a university lab. The sugarcane straw was received from a nearby research center that grew the sugarcane in the state of São Paulo. The straw comes already chopped up, and scientists then sort it into different sized pieces with sieves before carrying out experiments.</p>
Mapping sugarcane globally at 10 m resolution using GEDI and Sentinel-2
<p><strong>Dataset Abstract:</strong><br>Sugarcane is an important source of food, biofuel, and farmer income in many countries. At the same time, sugarcane is implicated in many social and environmental challenges, including water scarcity and nutrient pollution. Currently, few of the top sugar-producing countries generate reliable maps of where sugarcane is cultivated. To fill this gap, we introduce a dataset of detailed sugarcane maps for the top 13 producing countries in the world, comprising nearly 90% of global production. Maps were generated for the 2019-2022 period by combining data from the Global Ecosystem Dynamics Investigation (GEDI) and Sentinel-2 (S2). GEDI data were used to provide training data on where tall and short crops were growing each month, while S2 features were used to map tall crops for all cropland pixels each month. Sugarcane was then identified by leveraging the fact that sugar is typically the only tall crop growing for a substantial fraction of time during the study period. Comparisons with field data, pre-existing maps, and official government statistics all indicated high precision and recall of our maps. Agreement with field data at the pixel level exceeded 80% in most countries, and sub-national sugarcane areas from our maps were consistent with government statistics. Exceptions appeared mainly due to problems in underlying cropland masks, or to under-reporting of sugarcane area by governments. <br>The final maps should be useful in studying the various impacts of sugarcane cultivation and producing maps of related outcomes such as sugarcane yields.</p> <p><strong>USAGE: Users must mask the provided sugarcane map with the most appropriate crop mask from the ones provided. If none of the provided crop masks are suitable, users can use an external crop mask instead.</strong></p> <p>Validation results for the sugarcane maps are detailed in Section 4.3 of the paper. For Indonesia and Guatemala, no field-level data or raster datasets were available for validation of our sugarcane maps.</p> <p><br><strong>Dataset:</strong> <br>5 bands<br>b1: Number of tall months<br>b2: Sugarcane Map: 0 = non-sugarcane, 1 = sugarcane<br>b3: ESA crop mask: 0 = non-cropland, 1 = cropland<br>b4: ESRI crop mask: 0 = non-cropland, 1 = cropland<br>b5: GLAD crop mask: 0 = non-cropland, 1 = cropland</p> <p> </p> <p>The dataset can be accessed on Google Earth Engine (GEE) at <br><strong><a href="https://code.earthengine.google.com/?asset=projects/lobell-lab/gedi_sugarcane/maps/imgColl_10m_ESAESRIGLAD">https://code.earthengine.google.com/?asset=projects/lobell-lab/gedi_sugarcane/maps/imgColl_10m_ESAESRIGLAD</a><br></strong><br>Example GEE script for visualizing and masking the sugarcane maps by country available at:<br><strong><a href="https://code.earthengine.google.com/545a87ce9bc29f2b5ad180955d974f8c?asset=projects%2Flobell-lab%2Fgedi_sugarcane%2Fmaps%2FimgColl_10m_ESAESRIGLAD">https://code.earthengine.google.com/545a87ce9bc29f2b5ad180955d974f8c?asset=projects%2fl Bell-lab%2Fgedi_sugarcane%2 Maps%2FimgColl_10m_ESAESRIGLAD</a></strong></p>
Archive dataset for sugarcane simulations with the JULES model
<p>This dataset contains supporting information to simulate sugarcane growth and development with the JULES model. It provides a collection of csv files with crop responses to CO2, Temperature and Soil moisture conditions (CTW-response), comparison against field and regional observations (performance), and climate change projections (projections). The parameters' values and model configuration are provided in sub-folders "sim_db" and "jules_run". Model runs were carried out with <a href="https://github.com/Murilodsv/wpy-jules">wpy-jules</a>, whereas the scientific documentation is described in Vianna et al. (2022). </p> <p>This work was supported by the Newton Fund through the Met Office Climate Science for Service Partnership Brazil (CSSP Brazil). We acknowledge the use of the Monsoon HPC system, maintained through a strategic partnership between the Met Office and the Natural Environment Research Council.</p> <p><strong>Summary</strong>:</p> <p>- CTW-response: CSV files initiated with "C", "T" and "W"<br> - performance: CSV files with suffix "perf" as well as "yield_var"<br> - projections: CSV files with suffix "future"<br> - input: Folder "sim_db"<br> - configuration: Folder "jules_run"</p> <p><strong>References</strong></p> <p>Vianna et al. (2022). Improving the representation of sugarcane crop in the JULES model for climate impact assessment. Global Change Biology Bioenergy (<a href="https://doi.org/10.1111/gcbb.12989">https://doi.org/10.1111/gcbb.12989</a>).</p>
Figure 2 in Evaluation of root-knot nematode resistance assays for sugarcane accession lines in Australia
Figure 2: Regressions to show the relationship between root biomass and ln(eggs per g roots+1) in eight nematode trials.
Figure 1 in Evaluation of root-knot nematode resistance assays for sugarcane accession lines in Australia
Figure 1: Examples of root-knot nematode gall ratings for sugarcane based on percentage of root system with galls. 1 = ≤1 to 2%, 2 = 2 to 25%, 3 = 25 to 50%, 4 = 51 to 75%, 5 ≥ 75% (modified from Shepherd 1979).
Figure 4 in Within-plant distribution and rapid assessment of sugarcane rust mite population on sugarcane canopy
Figure 4 Relationship between sugarcane rust mite density and counting speed of the imprinting technique.
Figure 3 in Within-plant distribution and rapid assessment of sugarcane rust mite population on sugarcane canopy
Figure 3 Within-plant distribution of sugarcane rust mite population based on the imprinting tech- nique (mean ± SEM). The numbers within brackets are the proportions of mite populations within plants. Means across leaves with the same capital letters are not significantly different and means with the same lower letters on a given leaf position are not significantly different (Tukey,P <0.05).
Figure 5 in Within-plant distribution and rapid assessment of sugarcane rust mite population on sugarcane canopy
Figure 5 Physiological parameters of sugarcane canopy (mean±SEM).A=photosynthetic rate, gsw =stomatal conductance,Ci =intercellular CO2, E=transpiration, WUE=water use efficiency.
Figure 2 in Effect of untreated and pretreated sugarcane molasses on growth performance of Haematococcus pluvialis microalgae in inorganic fertilizer and macrophyte extract culture media
Figure 2. Cell density and growth rate of Haematococcus pluvialis in two different culture media NPK and ME, in mixotrophic cultivation untreated (UN) and pretreated (PR) sugarcane molasses. Error bars express standard mean deviations.
Figure 3 in Effect of untreated and pretreated sugarcane molasses on growth performance of Haematococcus pluvialis microalgae in inorganic fertilizer and macrophyte extract culture media
Figure 3. Protein (P), lipids (L), carbon (C) and nitrogen (N) (% biomass dry weight) of Haematococcus pluvialis growth in two different culture media (NPK and ME) in mixotrophic cultivation untreated (UN) and pretreated (PR) sugarcane molasses.
Fig. 1 in Sugarcane stem borers of the Colombian Cauca River Valley: current pest status, biology, and control
Fig. 1. Male adults of 4 Diatraea species present in Colombia. A. D. saccharalis; B. D. indigenella; C. D. tabernella; D. D. busckella. In general, moths are difficult to distinguish, and clear species identification requires the dissection of male genitalia (photos L. A. Lastra).
Fig. 3. A in Sugarcane stem borers of the Colombian Cauca River Valley: current pest status, biology, and control
Fig. 3. A. "Dead heart" in sugarcane caused by Diatraea sp. (photo M. Rodríguez), and B. bored internode by Diatraea sp. can disrupt apical dominance and promote growth of multiple lateral shoots, diverting resources from sucrose synthesis to vegetative growth (photo AE Bustillo).
Fig. 2 in Sugarcane stem borers of the Colombian Cauca River Valley: current pest status, biology, and control
Fig. 2. Larvae of 4 Diatraea species present in Colombia. A. D. saccharalis; B. D. indigenella; C. D. tabernella; D. D. busckella. In general, larvae of D. saccharalis exhibit a well-sclerotized set of setal plates along their length, whereas the setal plates are ofen less distinguishable in D. indigenella due to dark, longitudinal dorsal stripes. Larvae of D. tabernella possess a distinctive set of blackish setal plates and adjacent purple spots that resemble transverse lines, which are absent in D. busckella (photos L. A. Lastra).
Fig. 2 in Diversity and abundance of edaphic arthropods associated with conventional and organic sugarcane crops in Brazil
Fig. 2. Principal component analysis of the arthropod communities in organic (ORG) and conventional (CON) sugarcane fields.
Fig. 1 in Diversity and abundance of edaphic arthropods associated with conventional and organic sugarcane crops in Brazil
Fig. 1. Curve estimating species richness of edaphic arthropods in conventional and organic sugarcane fields in Jaboticabal, São Paulo, Brazil. Error bars represent the standard deviation.
Fig. 1 in Efficacy of five insecticides targeting spring and fall populations of sugarcane beetle adults
Fig. 1. Adjusted percentage of mortality of fall and spring populations of sugarcane beetles by active ingredient at low label rate. Treatment means with different upper case letters are significantly different (P ≤ 0.05; ANOVA and LSD test) for the fall population. Treatment means with different lower case letters are significantly different (P ≤ 0.05; ANOVA and LSD test) for the spring population.
Fig. 2 in Efficacy of five insecticides targeting spring and fall populations of sugarcane beetle adults
Fig. 2. Adjusted percentage of mortality of spring sugarcane beetles by active ingredient at both low and high label rate. Treatment means with different upper case letters are significantly different (P ≤ 0.05; ANOVA and LSD test) at the low rate. Treatment means with different lower case letters are significantly different (P ≤ 0.05; ANOVA and LSD test) at the high rate.
Sugarcane Culturable microbiome prospection for plant growth promotion traits in Cynodon dactylon
<p>Data set of running experiments for the prospection of traits for plant growth promotion of bacterial communities from sugarcane tissues: rhizospheric soil, roots, stalks, and leaves. For this experiment, we are using a model plant: Cynodon dactylon known as Bermuda grass.</p>
Fig. 2 in Sharing of termites (Blattodea: Isoptera) between sugarcane matrices and Atlantic Forest fragments in Northeast Brazil
Fig. 2. Species richness (A) and number of termite encounters (B) per food group of two Atlantic Forest fragments and adjacent sugarcane fields of two plantations in Northeast Brazil.Us., Usina; AF, Atlantic Forest; S, sugarcane field; I, feeding group I; II, feeding group II; III, feeding group III; IV, feeding group IV (Donovan et al., 2001).
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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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.