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19 results for “Vegetable parameters”
Updated fruits and vegetable parameters for swat models
<p>Ensuring accurate crop yield simulations in ecohydrological models such as the Soil and Water Assessment Tool (SWAT) is crucial to improve our understanding of agricultural systems and productivity. This, in turn, can facilitate the development of more sustainable agricultural practices. In this study, we focused on validating the crop growth parameters of the SWAT model for 24 table food fruits and vegetables in Iowa, located in the western Corn Belt region of the United States.</p> <p>To estimate these parameters, we used five primary sources: a) existing parameters in the SWAT crop parameter database, b) alternative parameters in the Environmental Policy Integrated Climate (EPIC) and Agricultural Policy/Environmental eXtender (APEX) crop parameter databases, c) literature, d) PHU fraction for scheduling dates, and e) expert communication among modeling team members. Among the 24 crops tested, 15 initial parameter data sets were already available in the SWAT database, and the remaining crop types were added to this plant.dat file.</p>
Model output, drivers and parameters for Ecosystem Recovery from Disturbance is Constrained by N Cycle Openness, Vegetation-Soil N Distribution, Form of N Losses, and the Balance Between Vegetation and Soil-Microbial Processes
Files used to generate the data for figures in: Rastetter, EB, Kling, GW, Shaver, GR, Crump, BC, Gough, L. Ecosystem Recovery from Disturbance Is Constrained by N Cycle Openness, Vegetation-Soil N Distribution, Form of N Losses, and the Balance between Vegetation and Soil-Microbial Processes. Ecosystems (2020). https://doi.org/10.1007/s10021-020-00542-3. This paper present a framework for assessing biogeochemical recovery of terrestrial ecosystems from disturbance. We identify three recovery phases. In Phase 1, nitrogen is redistributed from soil organic matter to vegetation, but the ecosystem continues to lose nitrogen because the recovering vegetation cannot take up nitrogen as fast as it is released from soil. In Phase 2, the ecosystem begins re-accumulating nitrogen and converges on a quasi-steady state in which vegetation and soil-microbial processes are in balance. In Phase 3, vegetation and soil-microbial processes remain in balance and the ecosystem slowly re-accumulates the remaining nitrogen.
Dataset of host, habitat and vegetation parameters in relation to gastrointestinal parasite infections of small mammalian hosts in Madagascar
<p>The dataset contains information on the endoparasite infection status of 903 individuals of four small mammal species (<em>Microcebus murinus</em>, <em>M. ravelobensis</em>, <em>Eliurus myoxinus</em>, <em>Rattus rattus</em>) in relation to host-specific (sex, body condition, population density) and habitat-specific factors (degree of habitat fragmentation, fragment size, distance to the edge of the fragment, percentage of edge habitat, vegetation structure).</p>
Figure 3 in The determination some biological parameters of Phenacoccus madeirensis Green (Hemiptera: Pseudococcidae) on vegetable plants
Figure 3. Survival ratio and life table parameters of Phenacoccus madeirensis on Eggplant (Adana Topağı).
VICGlobal: soil and vegetation parameters for the Variable Infiltration Capacity hydrological model
<p>## VICGlobal: soil, vegetation, and elevation band input files for the VIC hydrological model</p> <p>Date updated: June 28, 2021</p> <p>Authors and affiliations: Jacob Schaperow (1), Dongyue Li (1,2)<br> 1. Department of Civil and Environmental Engineering, UCLA<br> 2. Department of Geography, UCLA<br> Author contact info: jschap@g.ucla.edu</p> <p>The current version, v1.6d improves upon v1.6c by splitting the image parameters by continent, reducing file sizes.</p> <p>v1.6c is the same as v1.6, except that the image mode parameters have been updated to reflect the changes made to the classic mode parameters (e.g. r0 and rmin are different, and albedo, fcanopy, and LAI are calculated based on snow-free values).</p> <p>## Overview</p> <p>VICGlobal is a dataset that can be used to run the Variable Infiltration Capacity (VIC) hydrological model over regional to continental scales. The dataset is at 1/16 degree resolution and has latitudinal coverage from -60 to 85 degrees. All files are referenced to the WGS84 ellipsoid and datum (EPSG code 4326).</p> <p>The vegetation parameter file uses the IGBP classification and use partial land use types. The vegetation parameter rooting depths and root fractions are based on the method of Zeng (2001). The vegetation library file is largely the same as that of Livneh et al. (2013; 2015); however, the monthly average LAI, canopy fraction, and albedo values for each land cover type are calculated based on MODIS observations from 2017, using the method of Bohn and Vivoni (2019).</p> <p>There are two vegetation libraries: one for the northern hemisphere, and one for the southern hemisphere, in order to account for the seasonality of LAI, canopy fraction, and albedo.</p> <p>WARNING: although it appears small in compressed form, the image driver parameter input file, VICGlobal_params.nc, is about 140 GB when unzipped. Users are encouraged to use the image mode parameters that are already split by continent. For example, the parameter file for Africa is about 19 GB.</p> <p>A data descriptor is in preparation for submission to Nature Scientific Data (https://www.nature.com/sdata/).</p> <p>Other VIC input datasets (coverage limited to North America):<br> * Bohn and Vivoni MOD-LSP dataset: https://zenodo.org/record/2559631</p> <p>## List of contents</p> <p>Inputs for VIC-4 or the VIC-5 Classic Driver<br> * Soil parameter file<br> * Vegetation parameter file<br> * Elevation band file<br> * Vegetation library files (one each for the northern and southern hemispheres)</p> <p>Inputs for the VIC-5 Image Driver<br> * Parameter file (global)<br> * Domain file (global)<br> * Parameter files for each continent<br> * Africa<br> * Australia<br> * Eurasia (except Kamchatka)<br> * Kamchatka<br> * North America<br> * Oceania (New Zealand and nearby islands)<br> * South America<br> * Domain files for each continent<br> * GeoTiffs with continent masks</p> <p>Matlab codes for subsetting the VICGlobal parameters to a region of interest are also provided.</p> <p>## References</p> <p>* Bohn and Vivoni (2019). MOD-LSP, MODIS-based parameters for hydrologic modeling of North American land cover change. https://www.nature.com/articles/s41597-019-0150-2</p> <p>* Livneh et al. (2015). A spatially comprehensive, hydrometeorological data set for Mexico, the U.S., and Southern Canada 1950–2013. https://www.nature.com/articles/sdata201542</p> <p>* Livneh, B., Rosenberg, E. A., Lin, C., Nijssen, B., Mishra, V., Andreadis, K. M., Maurer, E. P. and Lettenmaier, D. P.: A long-term hydrologically based dataset of land surface fluxes and states for the conterminous United States: Update and extensions, J. Clim., 26(23), 9384–9392, doi:10.1175/JCLI-D-12-00508.1, 2013.</p> <p>* Zeng (2001). Global Vegetation Root Distribution for Land Modeling. Journal of Hydrometeorology. https://doi.org/10.1175/1525-7541(2001)002<0525:GVRDFL>2.0.CO;2</p>
HeadwaterstreamSNevada: data on riparian vegetation and water parameters of headwater streams in Sierra Nevada, Spain
<p>Providing historical data on riparian plant biodiversity and physico-chemical parameters of stream water in Mediterranean mountains helps to assess the effects of climate change and other human stressors on these sensitive and critical ecosystems. This database collects data from the main natural headwater streams of the Sierra Nevada (southeastern Spain), a high mountain (up to 3,479 meters above sea level, m.a.s.l.) recognized as a biodiversity super hotspot (Arroyo et al., 2022) in the Mediterranean Basin. On this mountain, rivers and landscapes depend on snowmelt water, representing an excellent scenario for evaluating global change's impacts. This dataset covers first- to third-order headwater streams at 41 sites from 832 to 1,997 m.a.s.l., collected from December 2006 to July 2007. Our goal is to supply information on the vegetation associated with streambanks, the essential physico-chemical parameters of stream water, and the physiographic features of the subwatersheds. Riparian vegetation data correspond to six plots sampled at each site, including total canopy, individual number, height and DBH (diameter at breast height) in woody species, and cover percentage for herbs. Physico-chemical parameters were measured in situ (electric conductivity, pH, dissolved O2 concentration, stream discharge) and determined in the laboratory [alkalinity, soluble reactive phosphate-phosphorus (SRP), total phosphorus (TP), nitrate-nitrogen (NO3-–N), ammonium-nitrogen (NH4+–N), total nitrogen (TN)]. Watershed physiographic variables comprise drainage area, minimum altitude, maximum altitude, mean slope, orientation, stream order, stream length, and land cover surface percentage. We recorded 197 plant taxa (67 species, 28 subspecies and 2 hybrids), representing 8.4% of the Sierra Nevada vascular flora. Due to the botanical nomenclature used, the database can be linked to FloraSNevada database (Lorite et al., 2020), contributing to Sierra Nevada (Spain) as a laboratory of global processes.</p> <p>For more information about the data, see the metadata document (Metadata_HeadwaterstreamSNevada.docx).</p>
Figure 1 in The determination some biological parameters of Phenacoccus madeirensis Green (Hemiptera: Pseudococcidae) on vegetable plants
Figure 1. Survival ratio and life table parameters of Phenacoccus madeirensis on Tomato (Hazera).
Figure 2 in The determination some biological parameters of Phenacoccus madeirensis Green (Hemiptera: Pseudococcidae) on vegetable plants
Figure 2. Survival ratio and life table parameters of Phenacoccus madeirensis on Tomato (Torry).
Figure 4 in The determination some biological parameters of Phenacoccus madeirensis Green (Hemiptera: Pseudococcidae) on vegetable plants
Figure 4. Survival ratio and life table parameters of Phenacoccus madeirensis on Eggplant (Anamur).
Maps of relevant snow and frost parameters for the analysis of future vegetation changes in Swiss forests
<p>The latest climate scenarios for Switzerland (CH2018) predict significant changes in temperature and precipitation in the future. This dataset contains maps snow and frost parameters calculated from climate variables that might have significant influence on future forest development. The following factors were calculated for the reference period 1981 - 2010 and the future period 2070 - 2099: Mean first and mean last frost day of the year, date of freezing and thawing of the ground, start and end of constant snow layer, risk of drought caused by frozen ground, wet snow intensity, probability of tree damages because of wet snow. Three different results were calculated for the period 2070 - 2099, based on three different representative concentration pathways (RCP2.6, RCP4.5, RCP8.5, CH2018). Explanations concerning model structure, usability and uncertainties in the data sets can be found in the project report.</p>
Sensitivity of the landslide model LAPSUS_LS to vegetation and soil parameters - DATASET
<p>The dataset include the data used to run the model and perform the sensitivity of the LAPSUS_LS model to the effect of vegetation described in the paper: 'Sensitivity of the landslide model LAPSUS_LS to vegetation and soil parameters' http://dx.doi.org/10.1016/j.ecoleng.2017.08.010</p>
Land surface modeling over the Dry Chaco: the impact of model structures, and soil, vegetation and land cover parameters
<p>The datasets archived here include simulation results shown in the paper, “Land surface modeling over the Dry Chaco: the impact of model structures, and soil, vegetation and land cover parameters”, published in Hydrology and Earth System Sciences (Maertens et al., 2021). The simulations were conducted over the South-American Dry Chaco and output on the different water budget components was produced with three land surface models (CLM2.0, CLSM-F2.5, and Noah3.6) embedded in the NASA Land Information System (LIS). The default LIS parameters were revised with (i) improved soil parameters, (ii) satellite-based interannually varying vegetation indices (leaf area index and green vegetation fraction), and (iii) yearly land cover information. For each experiment described in the manuscript, we provide daily netcdf files (0.125° resolution, period Jan 1992 – Dec 2015). The conducted experiments are simulations with (i) default LIS parameters (BL), (ii) updated soil parameters (REV<sub>s</sub>) and (iii) interannualy varying vegetation and land cover parameters (REV<sub>SV</sub>).<br> For details, please refer to</p> <p>Maertens, M., De Lannoy, G. J. M., Apers, S., Kumar, S. V., and Mahanama, S. P. P.: Land Surface modeling over the Dry Chaco: the impact of model structures, and soil, vegetation and land cover parameters, Hydrology and Earth System Sciences.</p> <p>Please contact Michiel Maertens (michiel.maertens@kuleuven.be) or Gabriëlle De Lannoy (gabrielle.delannoy@kuleuven.be) for any questions.</p>
Vegetation growth and landscape genetics of Tillandsia lomas at its dry limits in the Atacama Desert shows fine-scale response to environmental parameters
<p>1. Ecosystems at its dry limits have been studied while focusing on species biology, fitness and interactions of biotic and abiotic parameters; however, the interactive effects of these parameters remain under-explored and, therefore, information is often lacking about the putative effect of global climate change on these ecosystems.</p> <p>2. Here, we conducted an analysis of the interplay of fine-scale landscape genetics and biotic and abiotic factors of terrestrial <i>Tillandsia</i> lomas in the hyperarid Atacama Desert, characterized by a fog-dependent vegetation type consisting of almost one single vascular plant species only.</p> <p>3. We showed that metapopulations of <i>Tillandsia landbeckii</i> are genetically connected over many hundreds of squarekilometres, and despite a large potential to propagate clonally genetic diversity is structured, regionally and locally. On landscape-level genetic diversity correlates well with fitness parameters such as growth, flowering and vegetation density, and we observed also fine-scaled correlation with a 3-D landscape model indicating a positive feedback with saisonal fog occurrence and availability. The various interdependies of biotic and abiotic factors also result in regular linear banding patterns of vegetation arranged orthogonally towards landscape slope. <i>Ex-situ</i> growth experiments indicate that <i>T. landbeckii</i> growths at optimal rates within this extreme, hyperarid environment, and we can extrapolate mean biomass production for this particular ecosystem.</p> <p>4. <i>Synthesis.</i> Our results suggest that the unique ecosystem of terrestrial <i>Tillandsia</i> lomas in the hyperarid Atacama Desert is an evolutionary balanced and fine-scaled system. Vegetation itself is built up by long-lived and persistent modules. We developed a descriptive model of the various interacting factors, thereby highlighting also a severe threat caused by global climate change.</p>
Vegetation growth and landscape genetics of Tillandsia lomas at its dry limits in the Atacama Desert shows fine-scale response to environmental parameters
Open the record for dataset details and reuse information.
MODIS-derived Vegetation and Albedo Parameters for Agroecosystem-Climate Modeling
This dataset provides MODIS-derived leaf area index (LAI), stem area index (SAI), vegetation area fraction, dominant landcover category, and albedo parameters for the continental US (CONUS), parts of southern Canada, and Mexico at 30 km resolution. The data cover the period 2003-2010 and were developed to be used as surface input data for regional agroecosystem-climate models. MODIS Collection 5 products used to derive these parameters included the Terra yearly water mask, vegetation continuous field products, the combined Terra and Aqua yearly land-cover category (LCC) (MCD12Q1), 8-day composites for LAI (MCD15A2), and albedo parameter (MCD43B1) products. Please note that the MODIS Version 5 land data products used in this dataset have been superseded by Version 6 data products.
Figure 7 in The determination some biological parameters of Phenacoccus madeirensis Green (Hemiptera: Pseudococcidae) on vegetable plants
Figure 7. Survival ratio and life table parameters of Phenacoccus madeirensis on Pepper (Zafer).
Figure 6 in The determination some biological parameters of Phenacoccus madeirensis Green (Hemiptera: Pseudococcidae) on vegetable plants
Figure 6. Survival ratio and life table parameters of Phenacoccus madeirensis on Pepper (Safran).
Figure 5 in The determination some biological parameters of Phenacoccus madeirensis Green (Hemiptera: Pseudococcidae) on vegetable plants
Figure 5. Survival ratio and life table parameters of Phenacoccus madeirensis on Pepino (Miski).
Effect of Vegetable and Fruit Enzyme Supplementation on Muscle Activation, Aerobic Capacity, and Blood Biochemical Parameters After High-Intensity Interval Exercise: Feasibility Study of Exercise Desi
ClinicalTrials.gov study NCT06466408. IPD Sharing: NO. Countries: 1. Publications: 0.
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