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59 results for “irrigation water”
Figures 10-11 from: Silva GL, Metzelthin MH, Da-Costa T, Rocha MS, Silva DE, Ferla NJ, Silva OS (2017) Responses of water mite assemblages (Acari) to environmental parameters at irrigated rice cultivation fields and native lakes. Zoologia 34: 1-8. https://doi.org/10.3897/zoologia.34.e19988
Figures 10-11 Abundance and richness of mites in rice areas cultivation and native lakes: (10) abundance adults (± SD) (Log10 X+1); (11) richness (± SD) (Log10 X+1). Different letters indicate significant differences, Tukey test, p < 0.05.
Figure 2 in Quantum yield, chlorophyll, and cell damage in yellow passion fruit under irrigation strategies with brackish water and potassium
Figure 2. Variable fluorescence – Fv of 'BRS GA1' yellow passion fruit plants as a function of the interaction between brackish water irrigation strategies and potassium doses at 360 days after transplanting.Vertical bars represent the standard error of the mean (n = 4). Means followed by the same lowercase letters indicate no significant difference between brackish water irrigation strategies by the Scott-Knott test (p≤0.05) for the same potassium dose; the same uppercase letter indicates no significant difference between potassium doses by the F test (p ≤0.05) for the same strategy. For details of BWIS see Table 4.
ORGANIZATION OF AUTOMATED TECHNOLOGY OF MIXING WATER FOR PREPARATION OF IRRIGATION WATER IN FIELD CONDITIONS
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Data for the paper 'A Novel Simulation Optimization Framework for Multi Scale Irrigation Water Distribution and Scheduling Considering Crop Growth Process' submitted to Water Resources Research, an AGU journal
<p>This data set contains the data for the paper 'A Novel Simulation Optimization Framework for Multi Scale Irrigation Water Distribution and Scheduling Considering Crop Growth Process' submitted to Water Resources Research, an AGU journal. <span>The settings and crop parameters are consist of maize</span><span><span>(</span></span><span><a title="Ran, 2018 #74" href="#_ENREF_44"><span>Ran et al., 2018</span></a></span><span>; </span><span><a title="Shirazi, 2021 #75" href="#_ENREF_48"><span>Shirazi et al., 2021</span></a></span><span>)</span><span></span><span>, flower</span><span><span>(</span></span><span><a title="Reyhaneh alsadat Mousavi Zadeh Mojarad, 2018 #83" href="#_ENREF_45"><span>Reyhaneh alsadat Mousavi Zadeh Mojarad, 2018</span></a></span><span>; </span><span><a title="Karimi Avargani, 2023 #80" href="#_ENREF_24"><span>Karimi Avargani et al., 2023</span></a></span><span>)</span><span></span><span> and wheat</span><span><span>(</span></span><span><a title="Iqbal, 2014 #76" href="#_ENREF_17"><span>Iqbal et al., 2014</span></a></span><span>; </span><span><a title="Huang, 2022 #79" href="#_ENREF_15"><span>Huang et al., 2022</span></a></span><span>; </span><span><a title="Lyu, 2022 #99" href="#_ENREF_38"><span>Lyu et al., 2022</span></a></span><span>; </span><span><a title="Karimi Avargani, 2023 #80" href="#_ENREF_24"><span>Karimi Avargani et al., 2023</span></a></span><span>)</span><span></span><span>,</span><span> which used for initializing AquaCrop-OS . </span></p>
Detection of the Effect of Irrigation Fluid on Extravascular Lung Water in Patients Undergoing TRUP Using Bedside Lung Ultrasound
ClinicalTrials.gov study NCT06220734. IPD Sharing: UNDECIDED. Countries: 0. Publications: 2.
Hot Water Irrigation in Posterior Epistaxis
ClinicalTrials.gov study NCT04151888. IPD Sharing: Not stated. Countries: 0. Publications: 1.
Figure 1 from: Silva GL, Metzelthin MH, Da-Costa T, Rocha MS, Silva DE, Ferla NJ, Silva OS (2017) Responses of water mite assemblages (Acari) to environmental parameters at irrigated rice cultivation fields and native lakes. Zoologia 34: 1-8. https://doi.org/10.3897/zoologia.34.e19988
Figure 1 - Schematic map of Brazil and Rio Grande do Sul State illustrating the study area.
Figure 1 in Water quality assessment in an irrigation pond based on adult caddisfly (Insecta: Trichoptera) assemblages
Figure 1. Sampling site for adult Trichoptera and water quality variables.
Data produced for "Water Scarcity Challenges across Urban Regions with Expanding Irrigation"
<p>This archive includes the data and codes to produced results for the paper- <strong>Transition from Rain-fed to Irrigation-fed Agriculture in Rural Areas to Exacerbate Urban Water Scarcity</strong>. It contains 3 archive files:</p> <p><strong>1. H08_inputs_org: </strong>All the input files required for the simulation of the global hydrological model, H08 (/H08/map/org). It has the following files (for both scenarios S1, and S2) -</p> <p>a) AQUASTAT- industrial and domestic water use</p> <p>b) C05- GDP information for the countries</p> <p>c) FAO2009_Slop- data on slope</p> <p>d) GMIA5_S1- global map for irrigated areas version 5 for scenario 1</p> <p>e) GMIA5_S2- global map for irrigated areas version 5 for scenario 2</p> <p>f) GRanD- Global Reservoir and Dam (GRanD) database</p> <p>g) GSWP2_Albedo- albedo data</p> <p>h) GSWP23_SoilType- soil type data</p> <p>i) IGRAC- groundwater use for domestic and industrial sector</p> <p>j) IIASA_SSP- socio economic pathways data</p> <p>k) K14- explicit aqueduct data</p> <p>l) M08- crop distribution data</p> <p>m) OneGeology- geology units offshore</p> <p>n) R08- cropland and pastureland data</p> <p>o) WFDEI- flow direction </p> <p>p) DS02- irrigation data</p> <p><strong>2. Codes:</strong> Jupyter notebook consisting of the python codes for post-processing, and a folder named <em>files_req </em>with the required files for the notebook. </p> <p><strong>3. Outputs: </strong>This includes 4 archives</p> <p>a) arcgis_outputs- post-processing model results' shapefiles and rasters; ArcMap documents of CAD difference; and other outputs</p> <p>b) CAD_S1_S2- Raster files of monthly CAD for scenarios S1 (named as S12) and S2; raster files of total water abstraction from renewable water sources; and raster files of total water demand. </p> <p>c) diff_cad_21- Raster files of difference in CAD (CAD_S2 - CAD_S1) for with and without considering environmental flow requirements.</p> <p>e) urban_results_excel- Excel files of CAD comparison in S1 and S2; excel file of monthly water demand and abstraction in S1 and S2. </p> <p> </p>
Cold Water Irrigation Therapy as an Adjunct to Indomethacin for Post-Endoscopic Retrograde Cholangiopancreatography(ERCP) Pancreatitis
ClinicalTrials.gov study NCT07330284. IPD Sharing: NO. Countries: 1. Publications: 0.
Tap Water Versus Normal Saline for Wound Irrigation
ClinicalTrials.gov study NCT01564342. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Ozonated Water Irrigation as an Adjunct to Periodontal Therapy
ClinicalTrials.gov study NCT04556708. IPD Sharing: NO. Countries: 1. Publications: 0.
Water Versus Saline as Irrigation Fluid for Ureteroscopy
ClinicalTrials.gov study NCT03794102. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Urine Drug Levels Related to Source of Water for Irrigation for Vegetable Crops Among Healthy Israeli Volunteers
ClinicalTrials.gov study NCT02101801. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Comparison Between Drinking Water and Normal Saline in Irrigating Traumatic Wound
ClinicalTrials.gov study NCT06304272. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Transcriptomic effects of irrigation with magnetized water on tomato (Solanum lycopersicum L.) during plant development
GEO Series GSE232676. Solanum lycopersicum. 30 samples. Type: Expression profiling by high throughput sequencing.
Effects of strigolactones on tomato leaf transcriptome under irrigated and repeated water stress conditions
GEO Series GSE264066. Solanum lycopersicum. 21 samples. Type: Expression profiling by high throughput sequencing.
Molecular mechanism of negative pressure irrigation inhibiting root growth and improving water use efficiency in maize
GEO Series GSE180352. Zea mays. 6 samples. Type: Expression profiling by high throughput sequencing.
Coupmodel database (water and energy fluxes at a maize field under film mulching drip irrigation)
<p><strong>CoupModel databases (Water and energy fluxes in drip irrigation maize field under mulch)</strong></p> <p>Related data for submitted paper “Modelling water and energy fluxes for a maize cropland with the explicit representation of mulch-drip system". </p> <p> </p>
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International Brain Laboratory public data
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OpenNeuro
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