Soil map units are the basic geographic unit of the Soil Survey Geographic Database (SSURGO). The SSURGO dataset is a compilation of soils information collected over the last century by the Natural Resources Conservation Service (NRCS). Map units delineate the extent of different soils. Data for each map unit contains descriptions of the soil’s components, productivity, unique properties, and suitability interpretations. Each soil type has a unique combination of physical, chemical, nutrient and moisture properties. Soil type has ramifications for engineering and construction activities, natural hazards such as landslides, agricultural productivity, the distribution of native plant and animal life and hydrologic and other physical processes. Soil types in the context of climate and terrain can be used as a general indicator of engineering constraints, agriculture suitability, biological productivity and the natural distribution of plants and animals. Data from thegSSURGO databasewas used to create this layer. To download ready-to-use project packages of useful soil data derived from the SSURGO dataset, please visit the USA SSURGO Downloader app. Dataset Summary Phenomenon Mapped:Soils of the United States and associated territoriesGeographic Extent:The 50 United States, Puerto Rico, Guam, US Virgin Islands, Marshall Islands, Northern Marianas Islands, Palau, Federated States of Micronesia, and American SamoaCoordinate System:Web Mercator Auxiliary SphereVisible Scale:1:144,000 to 1:1,000Source:USDA Natural Resources Conservation Service Update Frequency:AnnualPublication Date:December 2024 What can you do with this layer?Feature layers work throughout the ArcGIS system. Generally your work flow with feature layers will begin in ArcGIS Online or ArcGIS Pro. Below are just a few of the things you can do with a feature service in Online and Pro.ArcGIS Online Add this layer to a map in the map viewer. The layer is limited to scales of approximately 1:144,000 or larger but avector tile layercreated from the same data can be used at smaller scales to produce awebmapthat displays across the full scale range. The layer or a map containing it can be used in an application.Change the layer’s transparency and set its visibility rangeOpen the layer’s attribute table and make selections and apply filters. Selections made in the map or table are reflected in the other. Center on selection allows you to zoom to features selected in the map or table and show selected records allows you to view the selected records in the table.Change the layer’s style and filter the data. For example, you could set a filter forFarmland Class= "All areas are prime farmland" to create a map of only prime farmland.Add labels and set their propertiesCustomize the pop-upArcGIS Pro Add this layer to a 2d or 3d map. The same scale limit as Online applies in ProUse as an input to geoprocessing. For example, copy features allows you to select then export portions of the data to a new feature class.Change the symbology and the attribute field used to symbolize the dataOpen table and make interactive selections with the mapModify the pop-upsApply Definition Queries to create sub-sets of the layerThis layer is part of theLiving Atlas of the Worldthat provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics. Data DictionaryAttributesKey fields from nine commonly used SSURGO tables were compiled to create the 173 attribute fields in this layer. Some fields were joined directly to the SSURGO Map Unit polygon feature class while others required summarization and other processing to create a 1:1 relationship between the attributes and polygons prior to joining the tables. Attributes of this layer are listed below in their order of occurrence in the attribute table and are organized by the SSURGO table they originated from and the processing methods used on them. Map Unit Polygon Feature Class Attribute TableThe fields in this table are from the attribute table of the Map Unit polygon feature class which provides the geographic extent of the map units. Area SymbolSpatial VersionMap Unit Symbol Map Unit TableThe fields in this table have a 1:1 relationship with the map unit polygons and were joined to the table using the Map Unit Key field. Map Unit NameMap Unit KindFarmland ClassInterpretive FocusIntensity of MappingIowa Corn Suitability Rating Legend TableThis table has 1:1 relationship with the Map Unit table and was joined using the Legend Key field. Project Scale Survey Area Catalog TableThe fields in this table have a 1:1 relationship with the polygons and were joined to the Map Unit table using the Survey Area Catalog Key and Legend Key fields. Survey Area VersionTabular Version Map Unit Aggregated Attribute TableThe fields in this table have a 1:1 relationship with the map unit polygons and were joined to the Map Unit attribute table using the Map Unit Key field. Slope Gradient - Dominant ComponentSlope Gradient - Weighted AverageBedrock Depth - MinimumWater Table Depth - Annual MinimumWater Table Depth - April to June MinimumFlooding Frequency - Dominant ConditionFlooding Frequency - MaximumPonding Frequency - PresenceAvailable Water Storage 0-25 cm - Weighted AverageAvailable Water Storage 0-50 cm - Weighted AverageAvailable Water Storage 0-100 cm - Weighted AverageAvailable Water Storage 0-150 cm - Weighted AverageDrainage Class - Dominant ConditionDrainage Class - WettestHydrologic Group - Dominant ConditionIrrigated Capability Class - Dominant ConditionIrrigated Capability Class - Proportion of Mapunit with Dominant ConditionNon-Irrigated Capability Class - Dominant ConditionNon-Irrigated Capability Class - Proportion of Mapunit with Dominant ConditionRating for Buildings without Basements - Dominant ConditionRating for Buildings with Basements - Dominant ConditionRating for Buildings with Basements - Least LimitingRating for Buildings with Basements - Most LimitingRating for Septic Tank Absorption Fields - Dominant ConditionRating for Septic Tank Absorption Fields - Least LimitingRating for Septic Tank Absorption Fields - Most LimitingRating for Sewage Lagoons - Dominant ConditionRating for Sewage Lagoons - Dominant ComponentRating for Roads and Streets - Dominant ConditionRating for Sand Source - Dominant ConditionRating for Sand Source - Most ProbableRating for Paths and Trails - Dominant ConditionRating for Paths and Trails - Weighted AverageErosion Hazard of Forest Roads and Trails - Dominant ComponentHydric Classification - Presence Rating for Manure and Food Processing Waste - Weighted Average Component Table – Dominant ComponentMap units have one or more components. To create a 1:1 join component data must be summarized by map unit. For these fields a custom script was used to select the component with the highest value for the Component Percentage Representative Value field (comppct_r). Ties were broken with the Slope Representative Value field (slope_r). Components with lower average slope were selected as dominant. If both soil order and slope were tied, the first value in the table was selected. Component Percentage - Low ValueComponent Percentage - Representative ValueComponent Percentage - High ValueComponent NameComponent KindOther Criteria Used to Identify ComponentsCriteria Used to Identify Components at the Local LevelRunoff ClassSoil loss tolerance factorWind Erodibility IndexWind Erodibility GroupErosion ClassEarth Cover 1Earth Cover 2Hydric ConditionHydric RatingAspect Range - Counter Clockwise LimitAspect - Representative ValueAspect Range - Clockwise LimitGeomorphic DescriptionNon-Irrigated Capability SubclassNon-Irrigated Unit Capability ClassIrrigated Capability SubclassIrrigated Unit Capability ClassConservation Tree Shrub GroupGrain Wildlife HabitatGrass Wildlife HabitatHerbaceous Wildlife HabitatShrub Wildlife HabitatConifer Wildlife HabitatHardwood Wildlife HabitatWetland Wildlife HabitatShallow Water Wildlife HabitatRangeland Wildlife HabitatOpenland Wildlife HabitatWoodland Wildlife HabitatWetland Wildlife HabitatSoil Slip PotentialSusceptibility to Frost HeavingConcrete CorrosionSteel CorrosionTaxonomic ClassTaxonomic OrderTaxonomic SuborderGreat GroupSubgroupParticle SizeParticle Size ModCation Exchange Activity ClassCarbonate ReactionTemperature ClassMoist SubclassSoil Temperature RegimeEdition of Keys to Soil Taxonomy Used to Classify SoilCalifornia Storie IndexComponent Key Component Table – Weighted AverageMap units may have one or more soil components. To create a 1:1 join, data from the Component table must be summarized by map unit. For these fields a custom script was used to calculate an average value for each map unit weighted by the Component Percentage Representative Value field (comppct_r). Slope Gradient - Low ValueSlope Gradient - Representative ValueSlope Gradient - High ValueSlope Length USLE - Low ValueSlope Length USLE - Representative ValueSlope Length USLE - High ValueElevation - Low ValueElevation - Representative ValueElevation - High ValueAlbedo - Low ValueAlbedo - Representative ValueAlbedo - High ValueMean Annual Air Temperature - Low ValueMean Annual Air Temperature - Representative ValueMean Annual Air Temperature - High ValueMean Annual Precipitation - Low ValueMean Annual Precipitation - Representative ValueMean Annual Precipitation - High ValueRelative Effective Annual Precipitation - Low ValueRelative Effective Annual Precipitation - Representative ValueRelative Effective Annual Precipitation - High ValueDays between Last and First Frost - Low ValueDays between Last and First Frost - Representative ValueDays between Last and First Frost - High ValueRange Forage Annual Potential Production - Low ValueRange Forage Annual Potential Production - Representative ValueRange Forage Annual Potential Production - High ValueInitial Subsidence - Low ValueInitial Subsidence - Representative ValueInitial Subsidence -

description: The data sets consist of two files, an ESRI shape file with associated files and an ESRI export file, of a composite of soil maps for Miami-Dade County, Florida issued by the Soil Conservation Service in April, 1958. The data is at 1:40,000 scale. This update of the Miami-Dade County soils map released in 2001 includes an attribute table for the soils polygons included in the spatial data layer.; abstract: The data sets consist of two files, an ESRI shape file with associated files and an ESRI export file, of a composite of soil maps for Miami-Dade County, Florida issued by the Soil Conservation Service in April, 1958. The data is at 1:40,000 scale. This update of the Miami-Dade County soils map released in 2001 includes an attribute table for the soils polygons included in the spatial data layer.

This data set is a digital soil survey and generally is the most detailed level of soil geographic data developed by the National Cooperative Soil Survey. The information was prepared by digitizing maps, by compiling information onto a planimetric correct base and digitizing, or by revising digitized maps using remotely sensed and other information. This data set consists of georeferenced digital map data and computerized attribute data. The map data are in a soil survey area extent format and include a detailed, field verified inventory of soils and miscellaneous areas that normally occur in a repeatable pattern on the landscape and that can be cartographically shown at the scale mapped. A special soil features layer (point and line features) is optional. This layer displays the location of features too small to delineate at the mapping scale, but they are large enough and contrasting enough to significantly influence use and management. The soil map units are linked to attributes in the National Soil Information System relational database, which gives the proportionate extent of the component soils and their properties. Please email GIS.Librarian@FloridaDEP.gov for additional information.

The data sets consist of two files, an ESRI shape file with associated files and an ESRI export file, of a composite of soil maps for Collier County, Florida issued by the Soil Conservation Service in March, 1954. The data is at 1:40,000 scale.

This update of the Collier County soils map originally released in 2001 includes an attribute table for the soils polygons included in the spatial data layer.

This map service displays Soils of Tallahassee - Leon County. With scale dependent draw and labeling. Best used with an alpha setting of 0.7 or transparency of 30%.SSURGO (Soil Survey Geographic database) refers to digital soils data produced and distributed by the Natural Resources Conservation Service (NRCS) - National Cartography and Geospatial Center (NCGC).This data set is a digital soil survey and generally is the most detailed level of soil geographic data developed by the National Cooperative Soil Survey. The information was prepared by digitizing maps, by compiling information onto a planimetric correct base and digitizing, or by revising digitized maps using remotely sensed and other information. This data set consists of georeferenced digital map data and computerized attribute data. The map data are in a soil survey area extent format and include a detailed, field verified inventory of soils and miscellaneous areas that normally occur in a repeatable pattern on the landscape and that can be cartographically shown at the scale mapped. A special soil features layer (point and line features) is optional. This layer displays the location of features too small to delineate at the mapping scale, but they are large enough and contrasting enough to significantly influence use and management. The soil map units are linked to attributes in the National Soil Information System relational database, which gives the proportionate extent of the component soils and their properties.

Soil map units from 1990's era soil maps were re-classed into 12 categories based on the depth of the seasonal high-water table, soil morphological characteristics, and geographical location.The Natural Soils Landscape Position project was undertaken to simplify the complex, multi-county soil survey data and to promote a clearer understanding of the relationships that exist between soil, hydrology, and vegetation community. The resulting product is a seamless spatial soil landscape layer within the boundaries of the SFWMD as well as a methodology that can be applied to other soils data to produce a similar result. This project was a cooperative effort between SFWMD and the NRCS. Funding was provided as part of the United Satets Environmental Protection agency's grant to the SFWMD for the Comprehensive Conservation Permitting and Mitigation Strategy Wetland Strategies under the assistance agreement ID number X9948864-96-0 to the South Florida Wetlands Conservation Strategy. Associated Technical Publication WS-06 (2001):Soil Classification Database: Categorization of County Soil Survey Data within the SFWMD Including Natural Soils Landscape Positions

This project was initiated 1998 to examine the variation in percent soil moisture in relation to rainfall, vegetation type, gaps, and time-since-fire in upland habitats at Archbold Biological Station, in south-central Florida. Data were collected from 78 sampling points across four vegetation types (rosemary scrub, scrubby flatwoods, oak-hickory scrub and sandy roadsides) with different time-since-fires (2-3 years or >20 years post-fire). In January 2006, 30 additional sampling points were added to include a fourth vegetation type (southern ridge sandhill). Data were collected at three depths below the soil surface (10, 50 and 90 cm) weekly (1 October 1998 – 9 June 1999), then bi-weekly (23 June 1999 – 3 October 2001), monthly (17 October 2001 – 15 June 2011), and every other month thereafter until the project ended on 16 July 2018.

This dataset links to the Long Term Ecological Research (LTER) Florida Coastal Everglades (FCE) Core Research Data Table of Contents (DTOC). The DTOC contains links to 173 individual datasets, which may be queried from the DTOC page. FCE Core Research Data are long-term data sets that address FCE LTER objectives and hypotheses, and that are supported primarily by LTER funds. All data are provided with accompanying metadata. Metadata includes details about the data including how, when and by whom a particular set of data was collected, and information regarding the data format. The FCE practice of dataset versioning has been discontinued as of March 2013. All long-term data will have new data appended to the file and the accompanying metadata will be updated. FCE data may be freely downloaded with as few restrictions as possible. Consultation or collaboration with the original investigators is strongly encouraged. Please keep the dataset originator informed of any plans to use the dataset, and include the dataset's proper citation and Digital Object Identifier (DOI) found under the 'How to cite these data' on the dataset's summary table. Resources in this dataset:Resource Title: GeoData catalog record. File Name: Web Page, url: https://geodata.nal.usda.gov/geonetwork/srv/eng/catalog.search#/metadata/FlCoastalEverglades_eaa_2015_March_19_1527

Analyzing soil water dynamics is crucial for the advancement of sustainable agriculture, encompassing crop growth management, soil health preservation, and the strategic use of resources. This study assessed the impact of conventional and sod-based crop rotations on water dynamics within the Suwannee River Basin’s unique karst and sandy soil environment from 2019-2022. The study was conducted on a 16-hectare center pivot irrigated field, featuring two conventional crop rotations: one comprising maize (Zea mays) and peanut (Arachis hypogaea), and another including maize, carrot (Daucus carota), and peanut. In addition, two sod-based rotations were evaluated: one involving two years of bahiagrass (Paspalum notatum) followed by maize and peanut, and another comprising two years of bahiagrass succeeded by maize, carrot, and peanut. The results indicated that maize maintained consistent crop water productivity (WPC-ETc) at 2.6 kg m-3 across rotations, while WPC-ETc for peanuts ranged from 1.5 kg m-3 for conventional rotation and 1.1 kg m-3 for sod-based. The cumulative crop evapotranspiration (ETc) for maize throughout the years was on an average 477 mm (Standard Deviation (SD) = 44 mm), while peanuts showed an average cumulative ETc of 354 mm (SD = 39 mm). Carrots and bahiagrass recorded cumulative ETc values in the range of 225-259 mm and 1611 mm (over two years), respectively. Deep percolation accounted for 31% of total water input, which emphasizes the need for strategic water management in porous soils. Additionally, sod-based rotations increased field capacity by 32%, soil organic carbon (SOC) by 31% in the top 15 cm, and soil aggregate stability by 101%, enhancing soil structure and moisture conservation. This study highlights sod-based rotations as effective for soil and water conservation, offering practical strategies for water-efficient agriculture in similar ecosystems.

Mapping soil quality (nutrients, carbon quality, process dynamics) has recently been undertaken as part of an effort to characterize baseline conditions for performance assessment of Everglades recovery activities. While large scale maps are useful for characterization of spatial pattern, several layers of uncertainty limit use as measures of performance and restoration progress. In particular, large scale maps (Greater Everglades) make specific assumptions about short range variability that are not well quantified. Nugget variance (variability in space over short separation distances) directly confounds use of baseline map products because future spatial sampling will not, in practicality, be at identical locations. If nugget variability is high, then significant uncertainty about ecosystem change arises from not knowing if observed differences arise from intrinsic ecosystem processes or from responses to human management. Our primary objective is to determine the extent to which spatial variability and sampling uncertainty confound ecological change detection. We will use hierarchically nested sampling of soils to establish nugget variability so that change through time can be assigned as observational uncertainty or management response. A related issue for mapping soil nutrients is greatly improving our understanding of the role of ecosystems in regulating nutrient conditions. Previous mapping efforts regarded space as the primary co-variate with soil nutrients, when there are numerous reasons to expect that ecological type and status are more important descriptors (with spatial autocorrelation playing a secondary, though still significant role). Our second objective is to partition variability in soil nutrient conditions in space and time, by ecosystem type, ecological status, and proximity to canals. We anticipate sampling of soils on a fine scale which traverse ecological transitions will help develop predictive models of the role of ecosystem type, geographic setting and, eventually, spatial structuring in regulating soil processes.

Land resource areas used in the United States, Caribbean, and Pacific Basin Major Land Resource Areas (MLRA) Geographic Database serve as the geospatial expression of the map products presented and described in Agriculture Handbook 296 (2022). Land resource categories historically used at state and national levels are land resource units, major land resource areas, and land resource regions (National Soil Survey Handbook, Part 649; Land Resource Hierarchy). Although Agriculture Handbook 296 (AH 296) does not describe land resource units (LRUs) directly, they are the basic units from which major land resource areas are determined. They are also the basic units for state land resource maps. LRUs are commonly but not necessarily coextensive with state general soil map units. LRUs generally are several thousand acres in size. A unit can be one continuous area or several separate areas that are near each other. In 2005, these areas were designated as common resource areas (CRAs) within the Natural Resources Conservation Service (NRCS). Like LRUs, common resource areas are not described in AH 296 and are not shown on the national mapbut are mentioned for historical purposes. Major land resource areas are geographically associated land resource units at a broader scale and higher hierarchical level than LRUs. Land resource regions (LRR) are a group of geographically associated major land resource areasat the highest hierarchical level shown at the continental scale. Identification of these large areas is important in statewide agricultural planning and has value in interstate, regional, and national planning.In AH 296, major land resource areas are generally designated by numbers and identified by a descriptive geographic name. Examples are MLRA 1 (Northern Pacific Coast Range, Foothills, and Valleys), MLRA 154 (South-Central Florida Ridge), and MLRA 230 (Yukon-Kuskokwim Highlands). Some MLRAs are designated by a letter in addition to a number because a previously established MLRA had been divided into smaller, more homogeneous areas, for example, MLRAs 102A, 102B, and 102C. Other MLRAs, especially smaller ones approaching the LRU scale, have been recombined. The use of numbers and letters to identify the newly created MLRAs requires fewer changes in existing information in records and in databases. A few MLRAs consist of two or more parts separated for short distances by other land resource areas. In some places one of the parts is widely separated from the main body of the MLRA and is in an adjoining LRR. The description of the respective MLRA also applies to these outlying parts. The spatial illustration of the MLRAs has been smoothed for the contiguous United States and Alaska to better reflect the scale at which the MLRA resource attributes (climate, soils, land use, vegetation, geology, and physiography) were aggregated for delineation.

Map Direct focus to show Contamination locations. Please refer to https://prodapps.dep.state.fl.us/dep-clnup/ for more information. Originally created 04/26/2012, and moved to Map Direct Lite on 11/19/2014. Application URL updated 4/12/2024. Please contact GIS.Librarian@FloridaDEP.gov for more information.

description: The data set consists of 30-minute evapotranspiration measurements made at the U.S. Geological Survey Immokalee row crop climate station beginning September 22, 2008 and ending January 8, 2009. Daily evapotranspiration rates corrected to a near-surface energy-budget varied from 0.1 millimeter (9/28/2008) to 3.3 millimeters (9/24/2008). The eddy-covariance method was used, with high-frequency sensors installed above an experimental field planted in green peppers to measure sensible and latent heat fluxes. Ancillary meteorological data are also included in the data set: net radiation, soil temperature and moisture, air temperature, relative humidity, wind speed and direction, and ground-water level. Data were collected at 30-minute resolution, with evapotranspiration corrected to the near-surface energy-budget at that timescale. The study was conducted at an experimental field on the University of Florida Southwest Florida Research and Education Center (SWFREC) in Immokalee, Florida (Latitude 26 27 40 North Longitude 81 26 24 West, in degrees minutes seconds, North American Datum 83, Section 20, Township 46S, Range 29E) in collaboration with Dr. Sanjay Shukla and university staff and students, who maintain six drainage lysimeters within the field (Jaber and others, 2006; Shukla and others, 2006; Shukla and others, 2012; Shukla and Shrestha, 2015, Shrestha and Shukla, 2014). Peppers were planted in plastic-mulched raised beds, with drip irrigation beneath the mulch used to maintain soil moisture at field capacity. Areas between the beds were bare soil or sparsely covered. Dimensions of the field were about 117 meters (north-south) by 72 meters (east-west), or 0.83 hectare. During pepper seasons, the ET station was located in the center of the field between two sets of drainage lysimeters. References: Jaber, F. H., Shukla, S., and Srivastava, S., 2006, Recharge, upflux, and water table response for shallow water table conditions: Hydrological Processes, v. 20, p. 1895-1907. Shukla, S., and Shrestha, N., 2015, Evapotranspiration for plastic-mulched production system for gradually cooling and warming seasons: measurements and modeling: Irrigation Science v. 33, no. 5, p. 387-397. Shukla, S., Jaber, F.H., Goswami, D., and Srivastava, S., 2012, Evapotranspiration losses for pepper under plastic mulch and shallow water table conditions: Irrigation Science, DOI 10.1007/s00271-012-0327-3. Shukla, S., Srivastava, S., and Hardin, J.D., 2006, Design, construction, and installation of large drainage lysimeters for water quantity and quality studies: Applied Engineering in Agriculture v. 22, no. 4, p. 529-540. Shrestha, N. K. and Shukla, S., 2014, Evaporation and transpiration losses from erect and vine crops grown under plastic mulch in a sub-tropical region: Agricultural Water Management v. 143, no. 9, p. 29-37, DOI 10.1016. /j.agwat.2014.05.011; abstract: The data set consists of 30-minute evapotranspiration measurements made at the U.S. Geological Survey Immokalee row crop climate station beginning September 22, 2008 and ending January 8, 2009. Daily evapotranspiration rates corrected to a near-surface energy-budget varied from 0.1 millimeter (9/28/2008) to 3.3 millimeters (9/24/2008). The eddy-covariance method was used, with high-frequency sensors installed above an experimental field planted in green peppers to measure sensible and latent heat fluxes. Ancillary meteorological data are also included in the data set: net radiation, soil temperature and moisture, air temperature, relative humidity, wind speed and direction, and ground-water level. Data were collected at 30-minute resolution, with evapotranspiration corrected to the near-surface energy-budget at that timescale. The study was conducted at an experimental field on the University of Florida Southwest Florida Research and Education Center (SWFREC) in Immokalee, Florida (Latitude 26 27 40 North Longitude 81 26 24 West, in degrees minutes seconds, North American Datum 83, Section 20, Township 46S, Range 29E) in collaboration with Dr. Sanjay Shukla and university staff and students, who maintain six drainage lysimeters within the field (Jaber and others, 2006; Shukla and others, 2006; Shukla and others, 2012; Shukla and Shrestha, 2015, Shrestha and Shukla, 2014). Peppers were planted in plastic-mulched raised beds, with drip irrigation beneath the mulch used to maintain soil moisture at field capacity. Areas between the beds were bare soil or sparsely covered. Dimensions of the field were about 117 meters (north-south) by 72 meters (east-west), or 0.83 hectare. During pepper seasons, the ET station was located in the center of the field between two sets of drainage lysimeters. References: Jaber, F. H., Shukla, S., and Srivastava, S., 2006, Recharge, upflux, and water table response for shallow water table conditions: Hydrological Processes, v. 20, p. 1895-1907. Shukla, S., and Shrestha, N., 2015, Evapotranspiration for plastic-mulched production system for gradually cooling and warming seasons: measurements and modeling: Irrigation Science v. 33, no. 5, p. 387-397. Shukla, S., Jaber, F.H., Goswami, D., and Srivastava, S., 2012, Evapotranspiration losses for pepper under plastic mulch and shallow water table conditions: Irrigation Science, DOI 10.1007/s00271-012-0327-3. Shukla, S., Srivastava, S., and Hardin, J.D., 2006, Design, construction, and installation of large drainage lysimeters for water quantity and quality studies: Applied Engineering in Agriculture v. 22, no. 4, p. 529-540. Shrestha, N. K. and Shukla, S., 2014, Evaporation and transpiration losses from erect and vine crops grown under plastic mulch in a sub-tropical region: Agricultural Water Management v. 143, no. 9, p. 29-37, DOI 10.1016. /j.agwat.2014.05.011