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608 results for “irrigation”
Evaluating country-scale irrigation demand through parsimonious agro-hydrological modeling
<p>WaterCROPv2 is an advanced agro-hydrological model designed to estimate irrigation<br>water demand at a national scale by effectively balancing hydrological accuracy with manageable data<br>requirements. Building on the original WaterCROPv1 model, WaterCROPv2 incorporates several<br>significant enhancements, including hourly computation, rainwater canopy interception, soil-dependent5<br>leakage dynamics, and daily evapotranspiration trends based on localized meteorological data.</p> <p>WaterCROPv2 was used to assess mean irrigation water demand for maize from 2005<br>to 2015, illustrating its potential as a decision-support tool for policymakers. </p>
The importance of determining the irrigated zone
<p>Determining the root zone area for any plant is very important to add adequate water to the roots with no losses where water is essential for all living plants. It is the vehicle that carries nutrients, such as fertilizer, from the surface through the soil to the roots and it is the medium through which other transport processes (i.e. Diffusion) take place. With both fertigation, and the more traditional methods of fertilizer application, the practical goal is to maintain adequate water and nutrients.</p>
Data from : Limited influence of irrigation on pre-monsoon heat stress in the Indo-Gangetic Plain
<p>The dataset contains WRF-CLM4 simulation post-processed output for three experiments named CTL, AGR, and MOD for pre-monsoon (April-May) from 2004-2016. Here, CTL represents WRF-CLM4 simulation with no irrigation, AGR represents WRF-CLM4 simulation with agricultural census-based irrigation data and MOD represents WRF-CLM4 simulation with model-estimated irrigation data. Here, C1, C2, and C3 represent different parameterization scheme combinations.</p> <ul> <li>C1:Mellor-Yamada Nakanishi and Niino Level 3 - WRF Double-Moment 6-class scheme - Tiedtke scheme (MYNN3-WDM6-Tiedtke)</li> <li>C2:Mellor-Yamada Nakanishi and Niino Level 3 - WRF Single-Moment 6-classscheme - KF scheme (MYNN3-WSM6-KF)</li> <li>C3:Mellor-Yamada Nakanishi and Niino Level 3 - WRF Single-Moment 6-class scheme - Grell3D (MYNN3-WSM6 Grell3D)</li> </ul> <p>The post-processed output contains the following variables:</p> <ol> <li>Land Surface Temperature</li> <li>Mean Air Temperature</li> <li>Maximum Air Temperature</li> <li>Wet-bulb Temperature</li> <li>Specific Humidity</li> <li>Latent Heat Flux</li> <li>Sensible Heat Flux</li> <li>Planet Boundary Layer Height</li> <li>Surface Pressure</li> <li>Relative Humidity</li> </ol> <p>The dataset also contains a spreadsheet that contains the FAO monthly calendar of the percentage of crop irrigation and pre-monsoon season crop calendar based on crop production data and annual reports “Agricultural Statistics At a Glance” from the Government of India. In addition, the file contains raw pre-monsoon data (area under the crop and crop irrigated area) from 2004 to 2016 for five crops (rice, maize, gram, sugarcane, and sunflower) over Indo-Gangetic Plain (Bihar, Uttar Pradesh, Haryana, Punjab, and Rajasthan). The pre-monsoon irrigation files for WRF contain irrigation data input for the WRF-CLM4 model.</p>
Salinity of irrigation water selects distinct bacterial communities associated with Date palm (Phoenix dactylifera L.) root
<p>Saline water irrigation has been used extensively in date palm (<em>Phoenix dactylifera </em>L.) agriculture as an alternative to non-saline freshwater due to water scarcity in hyper-arid environments. However, how saline water irrigation affects date palm root-associated bacterial communities is unknown. Here, we investigated the effect of irrigation sources (non-saline vs saline water) on date palm root-associated bacterial communities and their diversity using 16S rRNA gene metabarcoding. The bacterial richness, Shannon diversity and evenness didn’t differ significantly between the irrigation sources. Soil electrical conductivity (EC) and irrigation water pH were negatively related to Shannon diversity and evenness respectively, while soil organic matter displayed a positive correlation with Shannon diversity. Of total, 40.5% OTUs were unique to non-saline water irrigation and 26% to saline water irrigation. The multivariate analyses displayed strong structuring of bacterial communities according to irrigation sources, and both soil EC and irrigation water pH were the major factors affecting communities. The genera <em>Bacillus</em>, <em>Micromonospora</em> and <em>Mycobacterium</em> were dominated while saline water irrigation whereas contrasting pattern was observed for <em>Rhizobium</em>, <em>Streptomyces</em> and <em>Acidibacter</em>. This study suggests that while saline water irrigation date palm root select specific bacterial taxa, potentially playing a crucial role in alleviating salinity stress of the host.</p>
The implementation of systemic insecticides and increased irrigation against invasive species attacking Ficus trees in Hawai'i.
<p>This contains the data collected for the publication submitted to the Journal of Applied Entomology. </p>
Shadow Spaces for Water Stress Adaptation: Supplemental Irrigation Application in Rainfed Fig Production
<p>Data Sources is a SPSS file. Common descriptive statistics and inferential statistics are used.</p>
Irrigation Expansion Required to Mitigate Yield Losses Under Climate Change
<p>The "code" folder contains code and related data to (1) run regression to obtain crop yield sensitivities to temperature and precipitation, (2) conduct bootstrapping approach to resample crop data 1,000 times to derive regression coefficients, and (3) derive pixel-level crop yield changes and additional irrigation needs under 1.5°C and 3°C warming.</p> <p>The "crop_production_summary.xlsx" gives the global and country-level aggregated crop production (e.g., wheat, maize, rice and barley) (unit: 10^12 kcal) during Baseline periods (1996-2005), 1.5°C and 3°C warming above pre-industrial levels (1850-1900) using three irrigation adaptation scenarios: (i) without irrigation adaptation, which means using the historical irrigation extent around 2000 (ii) with full irrigation adaptation, which means applying 100% irrigation over all croplands and (iii) with sustainable irrigation adaptation, which selectively applies irrigation where irrigation practices do not deplete freshwater stocks and impair aquatic ecosystems.</p> <p>The "irr_need_summary.xlsx" gives the global and country-level sustainable and unsustainable irrigation area (unit: million hectares) of each crop (e.g., wheat, maize, rice and barley) in 2000 and additional irrigation area needed to offset warming-induced crop yield losses under 1.5°C and 3°C warming above pre-industrial levels (1850-1900).</p> <p>The "irrigation needed" folder contains pixel-level additional irrigation area fraction for each crop needed to offset warming-induced crop yield losses under 1.5°C and 3°C warming above pre-industrial levels (1850-1900).</p> <p>The "yield change" folder contains pixel-level crop yield change for each crop under 1.5°C and 3°C warming above pre-industrial levels (1850-1900).</p> <p>The "irrigation_sustainable" folder contains pixel-level data on irrigation water sustainability during the Baseline periods (1996-2005) and under 1.5°C and 3°C warming scenarios compared to pre-industrial levels (1850-1900). In this dataset, pixels with values <1 indicate that sustainable irrigation can be applied, while values >=1 indicate that irrigation will be unsustainable.</p> <p>For further details, please contact Liyin He (lhe@carnegiescience.edu) or Lorenzo Rosa (lrosa@carnegiescience.edu).</p>
Data, models, and outputs for an agent-based hydro-economic modeling study in an intensively irrigated region of the U.S. High Plains
<p>In this dataset, we include all the models developed for the study "An integrated modeling approach to simulate human-crop-groundwater interactions in intensively irrigated regions", which is published in Environmental Modelling & Software (<a href="https://doi.org/10.1016/j.envsoft.2024.106120">https://doi.org/10.1016/j.envsoft.2024.106120</a>). Additionally, we provide all the data used in this study. Below, you will find a description of the contents of each file:</p> <ul> <li>abm_modflow.zip: This file includes the agent-based hydro-economic model (ABM-MODFLOW), including model inputs and outputs, Python post-processing scripts, and the Windows batch script for the integration process.</li> <li>modflow.zip: This file contains the standalone MODFLOW model files. Each folder includes files for individual simulation periods, starting with a steady-state model for the predevelopment period, followed by seven transient models.</li> <li>modflow_rs.zip: This file contains the MODFLOW-RS model files. Given that remote sensing data is provided for years from 1984 onwards, only models for the post-1980 simulation periods are included. For model files corresponding to years prior to 1980, refer to modflow.zip.</li> <li>Figurers.zip: This file includes all data and Python scripts used to produce the figures in the main text.</li> <li>Tables.zip: This file contains all tables and the associated data included in the main text.</li> <li>Supporting_Info_Figures: This file includes all data and Python scripts used to produce the figures in the Supporting Information.</li> <li>Supporting_Info_Tables: This file contains all tables and the associated data included in the Supporting Information.</li> <li>Supporting_Info_Videos: This file stores Videos S1 and S2 of the Supporting Information, displaying the historical development of irrigation wells and groundwater-fed irrigated lands in the study area from 1946 to 2018.</li> </ul>
Regional irrigation expansion can support climate resilient crop production in post-invasion Ukraine
<p>This dataset contains results used to plot figure 1, 2, 3, 4 of the manucript "Regional irrigation expansion can support climate resilient crop production in post-invasion Ukraine" published in Nature Food. </p>
Data for irrigation impacts on urban heat stress in North America
<p>Includes all model simulation results, summaries by urban clusters, and evaluation results of the study. <br><br>The netcdf files are for various model variables and simulations (noURB for no urban simulation, IRR for irrigation simulation, and CTRL for the urban with no irrigation simulation, which is treated as the baseline), the csv files are the summaries for various cases (by urban cluster, and by world and climate zone for model evaluations), and the geotiff files are the raster images for key variables shown in the manuscript.<br><br>The python notebook has all the scripts to estimate the heat stress metrics from the netcdf files.</p>
A Path Model of the Intention to Adopt Variable Rate Irrigation in Northeast Italy (dataset)
<p>Data cleaned and used for the path analysis model estimated in the article</p> <h1>A Path Model of the Intention to Adopt Variable Rate Irrigation in Northeast Italy</h1> <p>https://doi.org/10.3390/su13041879</p>
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: Subset2
<p>This dataset contains the data underlying the following publication: Hassen W, Neifar M, Cherif H, Najjari A, Chouchane H, Driouich RC, Salah A, Naili F, Mosbah A, Souissi Y, Raddadi N, Ouzari HI, Fava F and Cherif A (2018) Pseudomonas rhizophila S211, a New Plant Growth-Promoting Rhizobacterium with Potential in Pesticide-Bioremediation. Front. Microbiol. 9:34. doi: 10.3389/fmicb.2018.00034</p>
Sensor data from Almeria and Barcelona for the implementation and optimisation of INCOVER's irrigation system (FINoT controller and scheduler).
<p>The purpose of the data is to help local irrigation communities, city's landscape gardeners and others that perform irrigation activities in INCOVER’s Demo Sites 1 and 2 to define site-specific thresholds that deficit irrigation can be achieved and set limits under which the automated irrigation profile can operate by optimising water consumption. These sensor values are associated with the sensor technology exploited by FINT in INCOVER (FDR). Moreover, sensor streams can also help technology modellers in the area of IoT to get an example of syntactic formulation of data services that are based on IoT networked devices.</p>
Potential command areas of irrigation dams
<p>Irrigation will play a vital role in meeting future food demand. Irrigation reservoirs currently account for 40% of global surface water used for irrigation and play a critical role in agricultural production by providing water to overcome scarcity, buffering against climatic variability, and enabling enhanced yields and multi-cropping. Yet while there has been ongoing establishment of dams for irrigation across the globe, there is little understanding of the extent to which irrigation has actually expanded within their potential command areas (PCA) (i.e., [the area that can be irrigated using water from a particular irrigation source]) and whether some irrigation dams have not yet realized the full extent of irrigated agriculture that they can potentially support. To assess this, we combine georeferenced information on recently established dams (year 2000 onward) (including dam height and reservoir capacity) with elevation data to first delineate the extent of each recent major irrigation reservoir globally (N=441) and estimate the extent of each dam’s PCA, based on pumping capacities for multiple classes of irrigation pumps.</p>
Data and Models for Salt Transport in Desert, Urban, and Irrigated Landscapes
<p>Folders contain data and models (SWAT, SWAT-MODFLOW) for analysis and modeling of salt fate and transport in urban, desert, and irrigated landscapes. Study regions include the Arkansas River Basin and the South Platte River Basin (Colorado, USA). The Education folder contains documentation of a STEM kit created at Colorado State University for guiding middle school and high school students through a 2-hour lab on salt pollution, transport, and mitigation in a mixed irrigation-desert watershed landscape.</p>
Table 4 in Influence of Mycorrhizae and Irrigation on Growth and Mineral Uptake by Corn (Zea mays L.) Seedlings in a Calcareous Soil
<p>Table 4. Analysis of variance for micronutrient (Fe, Mn, B, Cu, and Zn) uptake by shoots of corn seedlings. Numbers are P values with F statistics in parentheses. Plants were grown in Guam cobbly clay soil, inoculated or not inoculated with <i>Glomus aggregatum</i> and provided one of four levels of water.</p><table><tbody><tr><th></th><th></th><th></th><th><b>Micronutrients</b></th><th></th><th></th></tr></tbody><tbody><tr><th><b>Source</b></th><td><i>df</i></td><td>Fe</td><td>Mn</td><td>B</td><td>Cu</td><td>Zn</td></tr><tr><th>Water (W) *</th><td>3</td><td>0.75 (0.36)</td><td><0.01 (9.57)</td><td>0.22 (1.67)</td><td>0.12 (0.89)</td><td><0.05 (7.23)</td></tr><tr><th>Inoculation (I)</th><td>1</td><td><0.001 (31.16)</td><td><0.001 (169.46)</td><td><0.001 (69.93)</td><td><0.001 (38.63)</td><td><0.001 (436.02)</td></tr><tr><th>W × I</th><td>3</td><td>0.88 (0.37)</td><td>0.18 (1.83)</td><td>0.65 (0.62)</td><td>0.59 (0.91)</td><td>0.24 (1.29)</td></tr></tbody></table><p>*Water treatment (W) was tested against main-plot error while both inoculation (I) and interaction (W × I) were tested against the sub-plot error.</p>
Table 5 in Influence of Mycorrhizae and Irrigation on Growth and Mineral Uptake by Corn (Zea mays L.) Seedlings in a Calcareous Soil
<p>Table 5. Correlation analysis of plant growth parameters (shoot biomass, root biomass, leaf width, SPAD chlorophyll reading) and uptake of macro- and microelements in leaf tissues.</p><table><tbody><tr><th></th><th></th><th><b>Macroelements</b></th><th><b>plant)</b></th><th></th><th></th><th><b>Microelements</b></th><th><b>plant)</b></th><th></th></tr></tbody><tbody><tr><th><b>Growth parameter</b></th><td>N</td><td>P</td><td>K</td><td>Mg</td><td>Ca</td><td>Fe</td><td>Mn</td><td>B</td><td>Cu</td><td>Zn</td></tr><tr><th>Shoot biomass (g)</th><td>0.8873</td><td>0.9521</td><td>0.9482</td><td>0.9746</td><td>0.9551</td><td>0.7587</td><td>0.9439</td><td>0.8959</td><td>0.8534</td><td>0.9321</td></tr><tr><th>Root biomass (g)</th><td>0.8258</td><td>0.9079</td><td>0.9150</td><td>0.9452</td><td>0.9497</td><td>0.7199</td><td>0.9112</td><td>0.9358</td><td>0.7390</td><td>0.8730</td></tr><tr><th>Leaf width (cm)</th><td>0.8569</td><td>0.9067</td><td>0.9394</td><td>0.9251</td><td>0.8954</td><td>0.8011</td><td>0.8957</td><td>0.8779</td><td>0.8478</td><td>0.9030</td></tr><tr><th>Chlorophyll (SPAD)</th><td>0.852</td><td>0.8211</td><td>0.8664</td><td>0.8104</td><td>0.7893</td><td>0.7284</td><td>0.7616</td><td>0.7377</td><td>0.7400</td><td>0.8377</td></tr></tbody></table><p>All correlations are highly significant at P <, Pearson.</p>
Table 3 in Influence of Mycorrhizae and Irrigation on Growth and Mineral Uptake by Corn (Zea mays L.) Seedlings in a Calcareous Soil
<p>Table 3. Analysis of variance for macronutrient (N, P, K, Mg and Ca) uptake by shoots of corn seedlings. Numbers are P values with F statistics in parentheses. Plants were grown in Guam cobbly clay soil, inoculated or not inoculated with <i>Glomus aggregatum</i>, and provided one of four levels of water.</p><table><tbody><tr><th></th><th></th><th></th><th></th><th><b>Macronutrients</b></th><th></th><th></th></tr></tbody><tbody><tr><th><b>Source</b></th><td><i>df</i></td><td>N</td><td>P</td><td>K</td><td>Mg</td><td>Ca</td></tr><tr><th>Water (W)*</th><td>3</td><td><0.05 (4.19)</td><td><0.05 (6.06)</td><td><0.01 (6.23)</td><td><0.01 (8.03)</td><td><0.01 (6.75)</td></tr><tr><th>Inoculation (I) 1</th><td><0.001 (159.51)</td><td><0.001 (437.94)</td><td><0.001 (816.51)</td><td><0.001 (364.49)</td><td><0.001 (116.39)</td></tr><tr><th>W × I</th><td>3</td><td>0.27 (2.30)</td><td>0.07 (2.70)</td><td>0.37 (0.95)</td><td>0.08 (2.44)</td><td>0.18 (2.27)</td></tr></tbody></table><p>*Water treatment (W) was tested against main-plot error while both inoculation (I) and interaction (W × I) were tested against the sub-plot error.</p>
Table 2 in Influence of Mycorrhizae and Irrigation on Growth and Mineral Uptake by Corn (Zea mays L.) Seedlings in a Calcareous Soil
<p>Table 2. Analysis of variance for shoot biomass, root biomass, leaf width, and SPAD chlorophyll reading of corn seedlings. Numbers are P values with F statistics in parentheses. Plants were grown in Guam cobbly clay soil, inoculated or not inoculated with <i>Glomus aggregatum</i>, and provided one of four water treatments.</p><table><tbody><tr><th></th><th></th><th></th><th><b>Growth parameters</b></th><th></th></tr></tbody><tbody><tr><th></th><td></td><td>Shoot biomass</td><td>Root biomass</td><td>Leaf width</td><td>SPAD chlorophyll</td></tr><tr><th><b>Source</b></th><td><i>df</i></td><td>(g/plant)</td><td>(g/plant)</td><td>(cm)</td><td>reading</td></tr><tr><th>Water (W)*</th><td>3</td><td><0.01 (12.52)</td><td><0.01 (11.73)</td><td><0.01 (10.19)</td><td>0.62 (3.77)</td></tr><tr><th>Inoculation (I)</th><td>1</td><td><0.001 (406.63)</td><td><0.001 (272.41)</td><td><0.001 (195.20)</td><td><0.001 (234.03)</td></tr><tr><th>W × I</th><td>3</td><td><0.01 (6.06)</td><td><0.05 (4.09)</td><td>0.44 (0.97)</td><td><0.01 (1.68)</td></tr></tbody></table><p>*Water treatment (W) was tested against main-plot error while both inoculation (I) and interaction (W × I)</p><p>were tested against the sub-plot error.</p>
Table 1 in Influence of Mycorrhizae and Irrigation on Growth and Mineral Uptake by Corn (Zea mays L.) Seedlings in a Calcareous Soil
<p>Table 1. Chemical characteristics of Guam cobbly clay soil used in the experiment.</p><table><tbody><tr><th><b>Parameter</b></th><th><b>Unit</b></th><th><b>Value</b></th></tr></tbody><tbody><tr><th>pH</th><td></td><td>7.0</td></tr><tr><th>Organic Matter</th><td>g kg-1</td><td>6.4</td></tr><tr><th>P</th><td>mg kg-1</td><td>25</td></tr><tr><th>K</th><td>mg kg-1</td><td>56</td></tr><tr><th>Ca</th><td>mg kg-1</td><td>5678</td></tr><tr><th>Mg</th><td>mg kg-1</td><td>123</td></tr><tr><th>Mn</th><td>mg kg-1</td><td>5.6</td></tr><tr><th>Fe</th><td>mg kg-1</td><td>63</td></tr><tr><th>Zn</th><td>mg kg-1</td><td>0.9</td></tr><tr><th>Cu</th><td>mg kg-1</td><td>0.7</td></tr></tbody></table>
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