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608 results for “irrigation”
MADFORWATER: WP3: Adaptation of technologies for efficient water management and treated wastewater reuse in agriculture: Task3.1: Reduction of crop water requirement and tools for irrigation management with treated WW: Subtask 3.1.1: Plant Growth Promotion (PGP) bacteria to enhance crop resistance to water stress and salinity: Subset1
<p>This dataset contains the data underlying the following publication: Mouna Mahjoubi, Simone Cappello, Yasmine Souissi, Atef Jaouani and Ameur Cherif (February 7th 2018). Microbial Bioremediation of Petroleum Hydrocarbon– Contaminated Marine Environments, Recent Insights in Petroleum Science and Engineering Mansoor Zoveidavianpoor, IntechOpen, DOI: 10.5772/intechopen.72207</p> <p> </p>
Figure 5 in Ecology and life cycle of the filter-feeding Amphipsyche meridiana Ulmer 1902 (Trichoptera: Hydropsychidae) in an irrigation canal, central Thailand
Figure 5. The percentage of larval instars of Amphipsyche meridiana Ulmer 1902 at the sampling site was calculated using the distribution of head capsule width for each month.
Figure 8 in Ecology and life cycle of the filter-feeding Amphipsyche meridiana Ulmer 1902 (Trichoptera: Hydropsychidae) in an irrigation canal, central Thailand
Figure 8. Food item in Amphipsyche meridiana's digestive system under a bright field microscope (magnification x40).
Figure 7 in Ecology and life cycle of the filter-feeding Amphipsyche meridiana Ulmer 1902 (Trichoptera: Hydropsychidae) in an irrigation canal, central Thailand
Figure 7. Food item proportions in the gut contents of the larval instar of Amphipsyche meridiana in each month from the irrigation canal. The gut content of A. meridiana larvae (n = 120) from the study area. The presence (%) represents the percentage of larvae with guts containing this type of material.
Figure 4 in Ecology and life cycle of the filter-feeding Amphipsyche meridiana Ulmer 1902 (Trichoptera: Hydropsychidae) in an irrigation canal, central Thailand
Figure 4. The frequency distribution of larval instars of Amphipsyche meridiana Ulmer 1902 based on head capsule width (n = 12,513) from December 2021 to November 2022.
Figure 3 in Water quality assessment in an irrigation pond based on adult caddisfly (Insecta: Trichoptera) assemblages
Figure 3. Canonical Correspondence Analysis (CCA) showing correlation between caddisflies species and physicochemical variables. Abbreviations for taxonomy are shown in Table 2.
Figure 2 in Water quality assessment in an irrigation pond based on adult caddisfly (Insecta: Trichoptera) assemblages
Figure 2. The total number of species and individuals caught at an irrigation pond in the Kasetsart University, Thailand.
Figs 2, 3 in Aquatic Oligochaeta (Annelida: Clitellata) in wetlands and irrigated rice fields in the state of Rio Grande do Sul (Southern Brazil)
Figs 2, 3. Aquatic Oligochaeta in wetlands and irrigated rice fields in the state of Rio Grande do Sul, Brazil: 2, taxonomic richness; 3, species composition.
Fig. 1 in Aquatic Oligochaeta (Annelida: Clitellata) in wetlands and irrigated rice fields in the state of Rio Grande do Sul (Southern Brazil)
Fig. 1. Location of the study regions and study sites in Rio Grande do Sul state, Brazil. Abbreviations of the study regions: FV = 'Foz do Vacacaí' study region; SD = 'São Donato' study region; SG = 'southwestern' study region. Numbers indicate the study sites and follow Table I.
IRRIGATION AND MORDEN IRRIGATION TECHNOLOGY
<p>Irrigation, the artificial application of water to crops, is a fundamental agricultural practice that has been employed for centuries. While traditional methods have served their purpose, the increasing global population and the challenges posed by climate change have necessitated the development of more efficient and sustainable irrigation techniques. Modern irrigation technology, incorporating advanced engineering and automation, offers promising solutions to these challenges.</p>
European Long-term Irrigation Area Datasets version 1.0
<p>These files accompany the manuscript titled "<strong><em>Climate-Driven Interannual Variability in Subnational Irrigation Areas Across Europe</em></strong>" in the journal <strong><em>Communications Earth & Environment</em>.</strong></p> <p>We have developed the <strong>European Long-term Irrigation Area Dataset (ELIAD)</strong>, which offers annual subnational data on total irrigated and irrigable areas for 32 European regions from 1990 to 2020. For most countries, the data is at the NUTS2 level, except for the UK, Germany, and Ireland, where it is at the NUTS1 level.</p> <p>Supplementary A: Contains the supplementary figures and tables referenced in the manuscript.<br>Supplementary B: Provides detailed information on the irrigation reference periods for EU farm structure surveys and agricultural censuses.<br>Supplementary C: Describes the methodology used to generate the ELIAD dataset.<br>Supplementary D: Includes the ELIAD dataset itself.<br>Supplementary E: Details the remote sensing products used for comparison with ELIAD in the manuscript.<br>Supplementary F: Contains the data directly used in the figures and tables presented in the manuscript.</p>
Fig. 1 in Effects of irrigation method on pollination and pollinators (Hymenoptera: Apoidea) in an open-field tomato crop
Fig. 1. Fruit set (A) and weight of fruit (B) in relation to type of irrigation and type of pollination. OM = open + mechanical pollination.
Saline groundwater irrigation affects the date palm bulk soil associated fungal guild community and enhances the pathotroph abundance
<p><strong><em> Exploring the Influence of Saline Groundwater Irrigation on Soil Fungal Biodiversity in Date Palm (Phoenix dactylifera) Bulk Soil</em></strong></p>
Figure 1 in Influence of Mycorrhizae and Irrigation on Growth and Mineral Uptake by Corn (Zea mays L.) Seedlings in a Calcareous Soil
Figure 1. Average of shoot and root biomass (g/plant), leaf width (cm), and chlorophyll SPAD reading of corn seedlings grown in Guam cobbly clay soil, either inoculated (■) or not inoculated (♦) with Glomus aggregatum, and provided one of four volumes of water: W1=7200 mL, W2=3600 mL, W3=1800 mL, and W4=900 mL during a 3-week experiment. Crossbars represent standard deviations of means with four replications.
Dataset for the paper "EXPANDING SMALLHOLDER IRRIGATION IN CENTRAL KENYA DEMONSTRATES THE IMPORTANCE OF PROTECTING GRASSLAND LANDSCAPES"
<p>This dataset contains the labels created for the cropland mapping task.</p> <p>2912 labels (polygons) are created using the June 2022 satellite imagery extracted from the <a title="Norway's International Climate and Forests Initiative (NICFI)" href="https://www.planet.com/nicfi/#:~:text=Through%20Norway%E2%80%99s%20International%20Climate%20&%20Forests%20Initiative%20(NICFI)," target="_blank" rel="noopener">Norway's International Climate and Forests Initiative (NICFI)</a> Satellite Data Program and accessible using Planet's API.</p> <p>The dataset is a geojson file with the following fields:</p> <p> </p> <ol> <li>quad: This column references a specific quadrant or tile. Each quadrant is identified by a unique code, such as "L15-1238E-1025N", provided by Planet.</li> <li>land_type: This column indicates the type of land use for the polygon area. There are two main categories of agriculture ("Smallholder agriculture" and "Largeholder agriculture") and a category for "Other vegetation". In the paper "Smallholder agriculture" and "Largeholder agriculture" are merged into a single label "cropland" while "Other vegetation" is considered "non-cropland."</li> <li>geometry: This column contains the geometrical data defining each polygon in the form of a list of vertex coordinates. For example, [ [ 37.79092, 0.19152 ], [ 37.791563, 0.190876 ], [ 37.790544, 0.190152 ], [ 37.7899, 0.190823 ], ….] defines a polygon through a sequence of longitude and latitude pairs, which enclose a specific area of land.</li> </ol>
HELGA: Economic Limit of Groundwater-Fed Irrigation
<p>This dataset provides all the model output and links to the model scripts and input files of the HELGA model (Hydro-Economic Limits as a Global Analysis) as pertaining to the article <em>HELGA: a global hydro-economic model of groundwater-fed irrigation from a farmer’s perspective</em></p>
Figure 4 in Relative abundance of oribatid mites (Sarcoptiformes: Oribatida) in two tillage systems of irrigated and rain-fed wheat farms of Khodabandeh County, Iran
Figure 4. Means comparison of shannon-wiener index of oribatid mites in four systems (Different letters on the top of the bars indicate significant difference at P <0.05 by Student Newman-Keuls test).
Figure 3 in Relative abundance of oribatid mites (Sarcoptiformes: Oribatida) in two tillage systems of irrigated and rain-fed wheat farms of Khodabandeh County, Iran
Figure 3. Means comparison of diversity of oribatid mites in sampling times (Different letters on the top of the bars indicate significant difference at P <0.05 by Student Newman-Keuls test).
Figure 2 in Relative abundance of oribatid mites (Sarcoptiformes: Oribatida) in two tillage systems of irrigated and rain-fed wheat farms of Khodabandeh County, Iran
Figure 2. Means comparison of species richness of oribatid mites in four systems (Different letters on the top of the bars indicate significant difference at P <0.05 by Student Newman.
Figure 1 in Relative abundance of oribatid mites (Sarcoptiformes: Oribatida) in two tillage systems of irrigated and rain-fed wheat farms of Khodabandeh County, Iran
Figure 1. Means comparison of species richness of oribatid mites in sampling times (Different letters on the top of the bars indicate significant difference at P <0.05 by Student Newman–Keuls test).
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Allen Brain Atlas
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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.