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608 results for “irrigation”
UCSB SONGS Mitigation Monitoring: Wetland Process Study - Irrigation, Decompaction, Amendment, Planting and Seeding Experiment Plant Size
These data describe estimates of the condition and size of three species of salt marsh plants (Arthrocnemum subterminale, Frankenia salina, and Salicornia virginica) planted in experimental plots designed to evaluate the effectiveness of various soil treatments on increasing vegetation cover at the San Dieguito Wetlands (Del Mar, California). Plots established between 1.61 – 2.1 m MLLW were manipulated to test the effects of irrigation, decompaction, soil amendments, and planting versus seeding, whereas plots between 1.6 – 1.7 m MLLW tested the effects of planting versus seeding alone. Data collection was conducted from 2020 to 2022. During each survey, the length of the longest axis and maximum perpendicular width of each planted individual was measured.
Landsat-based maps of irrigated dry season cropping in Southeastern Anatolia, Turkey
<p><strong>Landsat-based maps of irrigated dry-season cropping in Southeastern Anatolia, Turkey</strong></p> <p>Long-term monitoring of the extent and intensity of irrigation systems is needed to track crop water consumption and to optimize land use in a changing climate. We mapped the expansion and land use intensity of irrigated dry season cropping in Turkey´s Southeastern Anatolia Project annually from 1990 to 2018 using Landsat time series and Google Earth Engine.</p> <p>This dataset includes multiple maps documenting the expansion and land use intensity of irrigated dry season cropping in Turkey´s largest irrigation scheme. We aggregated all Landsat imagery acquired during the July through September for the period 1990 to 2018 into spectral-temporal metrics and predicted dry season cropping annually using a machine learning classifier. We performed several post-processing steps to derive multiple map products for all areas with at least two dry season cropping cycles in the study period. The dataset comes in .zip format and includes the following map products:</p> <ul> <li>gap_dsc_fst.tif: first year of dry season cropping</li> <li>gap_dsc_yrs.tif: number of years with dry season cropping</li> <li>gap_dsc_frq.tif: dry season cropping frequency (% of years since first dry season cultivation):</li> <li>gap_dsc_trd.tif: five-year dry season cropping frequency trend magnitude</li> <li>gap_dsc_pvl.tif: significance level (p-values)</li> </ul> <p><strong>Spatial coverage</strong><br> The maps come in 30m spatial resolution and cover the Southeastern Anatolia Project (Güneydoğu Anadolu Projesi, GAP) region. The region consists of nine provinces which account for approximately 10% of the Turkish land area. </p> <p><strong>Temporal coverage</strong><br> The analyses cover the period 1990-2018. The first year of dry season cropping and the number of years with dry season cropping represent the time period 1990-2017. The temporal coverage of dry season cropping frequency varies on a pixel level, depending on the initial year of dry-season cultivation. The temporal coverage of the trend indicators also vary on a pixel level and furthermore have a constrained maximum temporal coverage of 1992-2012 due to the post-processing steps involved.</p> <p><strong>Data format</strong><br> The data are delivered as 16bit single layer GeoTIFFs in EPSG:3035 projection. The images are LZW compressed, and have NoData value 0.</p> <p><strong>Publication & further information</strong><br> Please see the publication for further information on the methodology and accuracy of the map products:</p> <p>Rufin, P.; Müller, D.; Schwieder, M.; Pflugmacher, D.; Hostert, P. (2020): Landsat time series reveal simultaneous expansion and intensification of irrigated dry season cropping in Southeastern Turkey. <em>Journal of Land Use Science. </em>DOI: http://dx.doi.org/10.1080/1747423X.2020.1858198</p> <p><strong>Acknowledgments</strong><br> This research contributes to the Landsat Science Team 2018-2023 (http://www.usgs.gov/land-resources/nli/landsat/landsat-science-teams) and the Global Land Programme (https://glp.earth/). We gratefully acknowledge the open cloud processing platform provided by Google. </p>
A new dataset of global irrigation areas from 2000 to 2015
<pre>We provide global irrigation maps README FOR GLOBAL IRRIGATION MAPS --------------------------------- Prediction Maps --------------- v3b_combined_*.tif: GeoTIFF files with model predictions, from 2001 to 2015. 0=not irrigated, 1=low-to-medium irrigated, 2=highly irrigated. Two of these are available in PNG format as well: 2001, 2015 Difference Between 2001 and 2015 -------------------------------- diff2001vs2015.tif: 0=no difference, 1=large decrease, 2=decrease, 3=no change, 4=increase, 5=large increase Also available in PNG format. Dark green=large decrease, green=decrease, grey=no change, orange=increase, red=large increase Assessment Map -------------- assessment_map.tif: FN=false negatives, FP=false positives, TP=true positives, mask=cropland mask</pre>
Investigating the Utility of Potato (Solanum tuberosum L.) Canopy Temperature and Leaf Greenness Responses to Water-Restriction for the Improvement of Irrigation Management Data
<p><span>Traits that rapidly respond to stress in important agricultural crops have the potential to provide growers with actionable feedback. E.g., traits that respond to water-restriction could inform irrigation systems by identifying crop water status and requirements in real-time. This would be particularly useful for potato, which is extremely susceptible to drought. We conducted two pot experiments and one field experiment to evaluate the utility of two traits, canopy temperature and leaf greenness, for informing irrigation management in potatoes. We also evaluated the efficacy of Phenospex PlantEye F500 sensors for the remote sensing of leaf greenness. We found that canopy temperatures of the cvs. Maris Piper (Spring Pot Experiment, +0.8°C; Autumn Pot Experiment, +5.3°C) and Désirée (Autumn Pot Experiment, +2.5°C) increased with water-restriction and that the canopy temperatures of Maris Piper return to baseline within three days after the resumption of well-watered conditions. We also found that these responses varied between cultivars, with predictable outcomes based on reported and corroborated drought tolerance ratings. We found inconclusive evidence of leaf greenness increasing due to water-restriction (Spring Pot Experiment, +0.8°C; Autumn Pot Experiment, +5.3°C) and found no evidence that post-drought recovery periods return this trait to baseline. However, leaf greenness measurements from the Phenospex PlantEye F500 were moderately to strongly correlated with SPAD values, suggesting this tool might be useful in the screening of drought-tolerant cultivars in the future.</span></p>
CropSuite – Crop suitability assessment for 48 crops under rainfed and irrigated conditions for Africa
<p>Here, we provide all data and maps for the paper submitted to Geoscientific Model Development (GMD).</p> <p>The maps include the crop suitability, climate suitability, most limiting factor, potential for multiple cropping, the optimal sowing date and the suitable sowing days for 48 crops for Africa with and without the consideration of climate variability.</p> <div> <ol> <li>Alfalfa (Medicago sativa)</li> <li>Arabica Coffee (Coffea arabica)</li> <li>Avocado (Persea americana)</li> <li>Banana (Musea spp.)</li> <li>Barley (Hordeum vulgare)</li> <li>Beans (Phaseolus vulgaris)</li> <li>Cabbage (Brassica oleracca)</li> <li>Carrot (Daucus carota)</li> <li>Cashew (Anacardium occidentale)</li> <li>Cassava (Manihot esculenta)</li> <li>Castor Bean (Ricinus commuis)</li> <li>Chickpea (Cicer arietinum)</li> <li>Citrus (Citrus spp.)</li> <li>Cocoa (Theobroma cacao)</li> <li>Coconut (Cocos nucifera)</li> <li>Cotton (Gossypium hirsutum)</li> <li>Cowpea (Vigna unguiculata)</li> <li>Green Pepper (Capsium annuum)</li> <li>Groundut (Arachis hypogaea)</li> <li>Guava (Psidium guijava)</li> <li>Maize (Zea mais)</li> <li>Mango (Mangifera indica)</li> <li>Millets (Pennisetum americanum)</li> <li>Oil Palm (Elaeis guineensis)</li> <li>Olive (Olea europacae)</li> <li>Onion (Allium cepa)</li> <li>Papaya (Carica papaya)</li> <li>Pea (Pisum sativum)</li> <li>Pineapple (Ananas comosus)</li> <li>Potato (Solanum tuberosum)</li> <li>Rapeseed (Brassica napus)</li> <li>Rice (Oryza sativa)</li> <li>Robusta Coffee (Coffea canephora)</li> <li>Rubber tree (Hevea brasiliensis)</li> <li>Rye (Secale cereale)</li> <li>Safflower (Carthamus tinctorius)</li> <li>Sesame (Sesamum indicum)</li> <li>Sorghum (Sorghum bicolor)</li> <li>Soy (Glycine maximum)</li> <li>Sugar Cane (Saccharum officinarum)</li> <li>Sunflower (Helianthus annus)</li> <li>Sweet Potato (Ipomoea batatas)</li> <li>Tea (Camellia senesis)</li> <li>Tobacco (Nicotiana tabacum)</li> <li>Tomato (Solanum lycopersicum esculentum)</li> <li>Watermelon (Colocynthis citrullus)</li> <li>Wheat (Triticum aesticum)</li> <li>Yam (Dioscorea)</li> </ol> </div> <p>The maps are provided for each crop seperately for rainfed and irrigated conditions and combined using data on currently irrigated areas based on Maier et al. (2018).</p> <p>In addition, maps for the comparison between crop suitability and MapSPAM2020 are provided.</p> <p>Zabel, Knüttel, Poschlod (2024): CropSuite – A comprehensive open-source crop suitability model considering climate variability for climate impact assessment. Preprint. DOI: <a href="https://doi.org/10.5194/egusphere-2024-2526">https://doi.org/10.5194/egusphere-2024-2526</a>.</p>
Long term irrigation experiment
<p>Climate change exposes ecosystems to strong and rapid changes in their environmental boundary conditions mainly due to the altered temperature and precipitation patterns. It is still poorly understood how fast interlinked ecosystem processes respond to altered environmental conditions, if these responses occur gradually or suddenly when thresholds are exceeded, and if the patterns of the responses will reach a stable state. We conducted an irrigation experiment in the Pfynwald, Switzerland from 2003-2018. A naturally dry Scots pine (<i>Pinus sylvestris </i>L.) forest was irrigated with amounts that doubled natural precipitation, thus releasing the forest stand from water limitation. The aim of this study was to provide a quantitative understanding on how different traits and functions of individual trees and the whole ecosystem responded to increased water availability, and how the patterns and magnitudes of these responses developed over time. We found that the response magnitude, the temporal trajectory of responses, and the length of initial lag period prior to significant response largely varied across traits. We detected rapid and stronger responses from above-ground tree traits (e.g., tree-ring width, needle length, and crown transparency) compared to below-ground tree traits (e.g., fine root biomass). The altered above-ground traits during the initial years of irrigation increased the water demand and trees adjusted by increasing root biomass during the later years of irrigation, resulting in an increased survival rate of Scots pine trees in irrigated plots. The irrigation also stimulated ecosystem-level foliar decomposition rate, fungal fruit body biomass, and regeneration abundances of broadleaved tree species. However, irrigation did not promote the regeneration of Scots pine trees which are reported to be vulnerable to extreme droughts. Our results provide extensive evidence that tree- and ecosystem-level responses were pervasive across a number of traits on long-term temporal scales. However, after reaching a peak, the magnitude of these responses either decreased or reached a new stable state, providing important insights into how resource alterations could change the system functioning and its boundary conditions.</p>
Global expansion of sustainable irrigation limited by water storage (Schmitt, Rosa, Daily - PNAS 2022)
<p>Global input and output data for "Global expansion of sustainable irrigation limited by water storage" by Rafael J. P. Schmitt, Lorenzo Rosa and Gretchen C. Daily, PNAS, 2022. See included Metadata file for more details. </p> <p></p>
Bacterial community composition of bulk soil from date palm (Phoenix dactylifera) farm depend on irrigation water salinity
<p>Non-saline and saline ground water irrigation is extensively used in the arid regions of United Arab Emirates (UAE) for date palm (<em>Phoenix</em> <em>dactylifera</em>) cultivation without knowing its effect on bulk soil bacterial communities. Bulk soil acts as a supply base for microbes and nutrients that are accessed by date palm roots. We collected soil samples from date farms across UAE and performed V3-V4 16s rRNA metabarcoding analysis to understand how bulk soil bacterial diversity and communities respond to irrigation water sources (non-saline and saline groundwater irrigation). There was no significant variation in bulk bacterial diversity (Shannon diversity, richness as well as evenness). But bulk bacterial communities differed between irrigation water sources and irrigation water electrical conductivity was the significant factor that explained a part of community variation. Out of total 5089 OTUs, saline bulk soil harbored only 21.3% of total OTUs compared to 31.5% OTUs in non-saline bulk soil, while 47.15% OTUs shared between both types of irrigation. Proteobacteria abundance was higher in saline bulk soil, while Actinobacteriota abundance was enhanced in non-saline bulk soil. Similar selection was observed at genus level, wherein saline bulk soil showed increase in abundance of <em>Subgroup_10, Nitrospira </em>and<em> Mycobacterium</em>, whereas <em>Microvirga, Ammoniphilus, Nitrospira</em> and <em>Lysinibacillus </em>were elevated in non-saline bulk soil. Saline (<em>Novibacillus</em> and <em>Bauldea</em>) and non-saline bulk soil (<em>Microvirga</em>, <em>Marmoricola</em>, <em>Domibacillus</em>, <em>Oceanobacillus</em>, <em>Bhargavaea</em> and <em>Solirubrobacter</em>) showed significant selection of indicator taxa (P < 0.05). This indicate that bacterial communities colonizing bulk soil differ depending on irrigation water source and it is affected by irrigation water EC.</p>
data for "On the tuning and performance of Stand-Alone Large-Power PV irrigation systems"
<p>These data were used in article "On the tuning and performance of Stand-Alone Large-Power PV irrigation systems" ( <a href="https://doi.org/10.1016/j.ecmx.2021.100175">https://doi.org/10.1016/j.ecmx.2021.100175</a>)</p>
Net irrigation requirement under different climate scenarios using AquaCrop over Europe
<p>This repository contains the setup and data related to the peer-reviewed article "Net irrigation requirement under different climate scenarios using AquaCrop over Europe" accepted for HESS (https://hess.copernicus.org/preprints/hess-2021-631/).</p> <p>The README.txt file contains all information about the repository. Please contact Louise Busschaert (louise.busschaert@kuleuven.be) or Gabrielle De Lannoy (gabrielle.delannoy@kuleuven.be) for any further questions.</p>
Global Economic Limits of Groundwater used for Irrigation
<p>Input and output files of the analysis of the global economic limits of groundwater used for Irrigation</p>
Landsat-based maps of cropping practices in the irrigated drylands of the Aral Sea Basin (1987-2019)
<p><strong>Overview</strong></p> <p>A set of maps revealing agricultural land use patterns in the irrigated drylands of the Amu Darya and Syr Darya basin between 1987 and 2019. The maps were produced using time series of 30m Landsat TM, ETM+ and OLI Collection 1 surface reflectance products and a Random Forest classification model trained with ~30,000 samples from the years 1987, 1998, 2008, and 2018. All processing steps were conducted in Google Earth Engine. The target classes of this product are "wet season cropping", "dry season cropping", "double cropping including fodder crops", and "non-cropland". Mapping was conducted across nine provinces in Uzbekistan, two in Turkmenistan, and three in Tajikistan, and post-processing was used to constrain the study area to regions which were irrigated in at least two years in the study period, areas below 2,000 m above sea level, and regions/years with a sufficient number of cloud-free Landsat images (n>6). Annual maps were aggregated temporally and spatially, resulting in different datasets described below.</p> <p>We advise map users to read the <a href="https://doi.org/10.1088/1748-9326/ac8daa">open access paper</a> and the associated supplementary materials for detailed insights. In case of further questions please contact the lead author of the work.</p> <p><strong>Data</strong></p> <p>This download contains three folders:</p> <ul> <li><em>landuse_30m</em>: Land use layers with 30m spatial resolution, representing the percentage (0-100%) of the three key land use types ("wet season cropping", "dry season cropping", "double cropping including fodder crops") within two time frames (1987-2000 and 2001-2019). Years with insufficient cloud-free Landsat observations were excluded from the calculation of percentages.</li> <li><em>landuse_3km</em>: Land use metrics with 3km spatial resolution, representing - for each grid cell and year - the "percentage of cropland", "percentage of dry-season cultivation", and "cropping frequency". The layers in these datasets are ordered chronologically, with the layer 1 representing the year 1987, and layer 33 the year 2019. Gaps in the time series were filled using linear interpolation for subsequent trend calculation using <a href="https://github.com/morrowcj/remotePARTS/">remotePARTS</a>.</li> <li><em>mask</em>: A mask with 30m spatial resolution constraining the study area to regions below 2,000 m above sea level and regions which were irrigated at least twice in the study period.</li> </ul> <p><strong>Map accuracy</strong></p> <p>We conducted an area-adjusted accuracy assessment based on a stratified random sample (n = 2,784 per year), which yielded important insights regarding accuracies and error types. The median area-adjusted overall accuracy of the maps across the study period is 91.4%, but class-specific user´s and producer´s accuracies vary substantially. Users should consult the supplementary materials of the article for details on accuracies, confusion matrices, and the most important error types.</p> <p><strong>Further resources</strong><br> The production of this map was made possible through the Landsat Program of the United States Geological Survey (USGS) and the Google Earth Engine cloud computing platform for preprocessing of the satellite data and classification. The code for preprocessing the Landsat time series is based on the Google Earth Engine Python API and made available at <a href="https://github.com/philipperufin/eepypr/">https://github.com/philipperufin/eepypr/</a>.</p>
Rising agriculture water scarcity in China is driven by expansion of irrigated cropland in water-scarce regions
<p>The datasets contain original data from the article titled" Rising agriculture water scarcity in China is driven by expansion of irrigated cropland in water-scarce regions "</p>
Рис. 2. А – Тусингайское водохранилиЩе; В – оросительный канал Дустлик у г. Гулистан. Фото Н. РуЗикуловой, 2020 г. Fig. 2. A – Tusingay water reservoir; B – irrigation channel Dustlik near the Gulistan Town. Photo by N. Ruzikulova, 2020. in Patterns of ecology and life cycles of aquatic molluscs from Central Asia
Рис. 2. А – Тусингайское водохранилиЩе; В – оросительный канал Дустлик у г. Гулистан. Фото Н. РуЗикуловой, 2020 г. Fig. 2. A – Tusingay water reservoir; B – irrigation channel Dustlik near the Gulistan Town. Photo by N. Ruzikulova, 2020.
Figure 2 in Influence of Mycorrhizae and Irrigation on Growth and Mineral Uptake by Corn (Zea mays L.) Seedlings in a Calcareous Soil
Figure 2. Average of macronutrients N, P, K, Mg, and Ca uptake (mg/plant) by shoots 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 the 3-week experiment. Crossbars represent standard deviations of means of four replications.
Figure 3 in Influence of Mycorrhizae and Irrigation on Growth and Mineral Uptake by Corn (Zea mays L.) Seedlings in a Calcareous Soil
Figure 3. Average of micronutrients Fe, Mn, B, Cu, and Zn uptake (mg/plant) by shoots 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 the 3-week experiment. Crossbars represent standard deviations of means of four replications.
US Irrigation Emissions from Multiple Sources
<div>This repository includes the data and code required to reproduce the analysis for the following manuscript:</div> <div> </div> <div>Driscoll, A.W., L.T. Marston, S. Ogle, N.J. Planavsky, M.A.B. Siddik, S. Spencer, S. Zhang, N.D. Mueller. Hotspots of irrigation-related US greenhouse gas emissions from multiple pathways. Nature Water (2024). </div> <div> </div>
Figure 2. Passion fruit species under different irrigation intervals. A. Passiflora gibertii with a 4 in Development and physiological aspects of three species of passion fruit submitted to water stress
Figure 2. Passion fruit species under different irrigation intervals. A. Passiflora gibertii with a 4-day interval; B. P. gibertii with a 8-day interval; C. P. gibertii with a 12-day interval; D. P. foetida with a 4-day interval; E. P. foetida with a 8-day interval; F. P. foetida with a 12-day interval; G. P. edulis with a 4-day interval; H. P. edulis with a 8-day interval; I. P. edulis with a 12-day interval. Bar = 30 cm.
Figure 1 in Quantum yield, chlorophyll, and cell damage in yellow passion fruit under irrigation strategies with brackish water and potassium
Figure 1. Data of precipitation, maximum and minimum air temperatures, and relative humidity of air observed during the experimental period.
Figure 3 in Quantum yield, chlorophyll, and cell damage in yellow passion fruit under irrigation strategies with brackish water and potassium
Figure 3. Electrolyte leakage - % IEL (A) and relative water content - RWC (B) of yellow passion fruit plants 'BRS GA1' as a function of the interaction between the use of brackish water irrigation strategies and potassium doses at 445 and 360 days after transplanting, respectively. Vertical bars represent the standard error of mean (n = 4). Means followed by the same lowercase letters indicate no significant difference between management strategies by the Scott-Knott test (p≤0.05) for the same potassium dose, and the same uppercase letters indicate no significant difference between potassium doses by the Tukey test (p ≤ 0.05) for the same strategy. For details of BWIS see Table 4.
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