Descriptions Excel Application Tool for Statewide Agricultural Water Use Data 2016 - 2020 Department of water resources, Water Use Efficiency Branch, Water Use Unit program, has developed an Excel application tool, which calculates annual estimates of irrigated crop area (ICA), crop evapotranspiration (ETc), effective precipitation (Ep), evapotranspiration of applied water (ETaw), consumed fraction (CF), and applied water (AW) for 20 crop categories by combinations of detailed analysis unit and county (DAUCo) over California. The 2016 – 2020 statewide agricultural water use data were developed by all 4 DWR’s Regional Offices (Northern Region Office, North Central Region Office, South Central Region Office, and Southern Region Office) using Cal_simetaw model for updating the information in the California Water Plan Updates-2023. Therefore, this current Excel application tool just covers agricultural water use data from the period of 2016 - 2020 water years. It should also be mentioned that there are 3 other similar Excel applications that cover 1998 - 2005 and 2006 – 2010, & 2011 - 2015 agricultural water use data for the California Water plan Updates 2005/2009, 2013, and 2018 respectively. Outputs data provided from this Excel application include ICA in acres, EP, both in unit values (Acre feet per acre) & volume (acre feet), ETc both in unit values (acre feet per acre), & volume (acre feet), ETaw, both in unit value (acre feet per acre), & volume (acre feet), AW, both in unit value (acre feet per acre) & volume (acre feet), CF (in percentage %) for WYs 2016 – 2020 at Detailed Analysis Unit by County (DAUCO), Detailed Analysis Unit (DAU), County, Planning Area (PA), Hydrological Region (HR), and Statewide spatial scales using the dropdown menu. Furthermore, throughout the whole process numerous computations and aggregation equations in various worksheets are included in this Excel application. And for obvious reasons all worksheets in this Excel application are hidden and password protected. So, accidentally they won’t be tampered with or changed/revised.

Following are definitions of terminology and listing of 20 crop categories used in this Excel application.

1. Study Area Maps
   The California Department of Water Resources (DWR) subdivided California into study areas for planning purposes. The largest study areas are the ten hydrologic regions (HR),
   The next level of delineation is the planning area (PAS), which are composed of multiple detailed analysis units (DAU). The DAUs are often split by county boundaries, so the smallest study areas used by DWR is DAU/County. Many planning studies begin at the Dau or PA level, and the results are aggregated into hydrologic regions for presentation.

2. Irrigated Crop Area (ICA) in acres
   The total amount of land irrigated for the purpose of growing a crop (includes multi-cropping acres)

3- Multi-cropping (MC) in acres
A section of land that has more than one crop grown on it in a year, this included one crop being planted more than once in a season in the same field.
Please note that there are no double cropping acreages for 2017. Because on a normal year when Regional Offices (RO) receive data from Land IQ, they were able to provide double cropping acreages. Since the 2017 land use data was derived from average crop acres between water years 2016 and 2018,2019, & 2020, they lost spatial and temporal data necessary to calculate double cropping.

1. Evapotranspiration (ET)
   Combination of soil evaporation and transpiration is referred to as evapotranspiration or ET. The rate of evapotranspiration from the plant-soil environment is primarily dependent on the energy available from solar radiation but is also dependent on relative humidity, temperature, cloud cover, and wind speed. It is an indication for how much your crops, lawn, garden, and trees need for healthy growth and productivity.

2. Reference Evapotranspiration (ETo)
   Reference evapotranspiration (ETo) is an estimate of the evapotranspiration of a 10-15 cm tall cool season grass and not lacking for water. The daily Standardized Reference Evapotranspiration for short canopies is calculated using the Penman-Monteith (PM) equation (Monteith, 1965) as presented in the United Nations FAO Irrigation and Drainage Paper (FAO 56) by Allen et al. (1988).

3. Penman-Monteith Equation (PM)
   Equation is used to estimate ETo when daily solar radiation, maximum and minimum air temperature, dew point temperature, and wind speed data are available. It is recommended by both the America Society of Civil Engineers and United Nations FAO for estimating ETo.

4. Crop Evapotranspiration (ETc), both in unit value (acre feet per acre), & volume (acre feet)
   Commonly known as potential evapotranspiration, which is the amount of water used by plants in transpiration and evaporation of water from adjacent plants and soil surfaces during a specific time period. ETc is computed
   as the product of reference evapotranspiration (ETo) and a crop coefficient (Kc) value, i.e., ETc = ETo x Kc.
   One Acre foot equals about 325851 gallons, or enough water to cover an acre of land about the size of a football field, one foot deep.

5. Crop Coefficient (Kc)
   Relates ET of a given crop at a specific time in its growth stage to a reference ET. Incorporates effects of crop growth state, crop density, and other cultural factors affecting ET. The reference condition has been termed "potential" and relates to grass. The main sources of Kc information are the FAO 24 (Doorenbos and Pruitt 1977) and FAO 56 (Allen et al. 1988) papers on evapotranspiration.

6. Effective Precipitation (Ep), both in unit value (acre feet per acre), & volume (acre feet)
   Fraction of rainfall effectively used by a crop, rather than mobilized as runoff or deep percolation

7. Evapotranspiration of Applied Water (ETaw), both in unit value (acre feet per acre), & volume (acre feet)
   Net amount of irrigation water needed to produce a crop (not including irrigation application efficiency). Soil characteristic data and crop information with precipitation and ETc data are used to generate hypothetical water balance irrigation schedules to determine ETaw.

8. Applied Water (AW), both in unit value (acre feet per acre), & volume (acre feet)
   Estimated as the ETaw divided by the mean seasonal irrigation system application efficiency.

9. Consumed Fraction (CF) in percentage (%)
   An estimate of how irrigation water is efficiently applied on fields to meet crop water, frost protection, and leaching requirements for a whole season or full year.

10. Crop category numbers and descriptions
    Crop Category Crop category description.

1 Grain (wheat, wheat_winter, wheat_spring, barley, oats, misc._grain & hay)
2 Rice (rice, rice_wild, rice_flooded, rice-upland)
3 Cotton
4 Sugar beet (sugar-beet, sugar_beet_late, sugar_beet_early)
5 Corn
6 Dry beans
7 Safflower
8 Other field crops (flax, hops, grain_sorghum, sudan,castor-beans, misc._field, sunflower, sorghum/sudan_hybrid, millet, sugarcane
9 Alfalfa (alfalfa, alfalfa_mixtures, alfalfa_cut, alfalfa_annual)
10 Pasture (pasture, clover, pasture_mixed, pasture_native, misc._grasses, turf_farm, pasture_bermuda, pasture_rye, klein_grass, pasture_fescue)
11 Tomato processing (tomato_processing, tomato_processing_drip, tomato_processing_sfc)
12 Tomato fresh (tomato_fresh, tomato_fresh_drip, tomato_fresh_sfc)
13 Cucurbits (cucurbits, melons, squash, cucumbers, cucumbers_fresh_market, cucumbers_machine-harvest, watermelon)
14 Onion & garlic (onion & garlic, onions, onions_dry, onions_green, garlic)
15 Potatoes (potatoes, potatoes_sweet)
16 Truck_Crops_misc (artichokes, truck_crops, asparagus, beans_green, carrots, celery, lettuce, peas, spinach, bus h_berries, strawberries, peppers, broccoli, cabbage, cauliflower)
17 Almond & pistachios
18 Other Deciduous (apples, apricots, walnuts, cherries, peaches, nectarines, pears, plums, prunes, figs, kiwis)
19 Citrus & subtropical (grapefruit, lemons, oranges, dates, avocados, olives, jojoba)
20 Vineyards (grape_table, grape_raisin, grape_wine)

This data release provides 270-m resolution maps of hotspots of vulnerability to projected changes in land-use, water shortages, and climate from 2001-2061 for agricultural, domestic, and ecological communities in the Central Coast of California, USA, under five management scenarios. This data covers the counties of Santa Cruz, San Benito, Monterey, San Luis Obispo, and Santa Barbara counties, but only cover those areas overlying a groundwater basin (because these contain the overwhelming majority of regional anthropogenic land-uses). Data are provided as .zip compressed file packages containing geospatial raster surfaces (.tif format). Each map is the product of one of three types of exposure to change (land, water, or climate) and one of three types of sensitivity to that change (agricultural, domestic, ecological). The resulting vulnerability measures map hotspots of nine vulnerabilities, plus a tenth map that is the sum of all nine measures to identify hotspots of overall vulnerability. See Van Schmidt et al. (2023) in Ecology & Society (doi: TBD) for full methodological details. Briefly, exposure to future land-use change and water shortages were jointly forecast from 2001 to 2061 with the Land Use and Carbon + Water Simulator (LUCAS-W) based on historical empirical rates. Exposure to climate change was calculated from five model-averaged RCP 8.5 forecasts of the Basin Characterization Model (BCM), which estimated change in runoff as surface water, potential recharge to groundwater aquifers, and climatic water deficit (CWD), among other variables. Lastly, sensitivity for communities was obtained from diverse datasets including LUCAS-W cropland projections, crop water demand data, farmland importance rankings, 2017 census data, range maps for imperiled species and subspecies, and wildlife agency reports. Sensitivity and exposure layers were rescaled 0-1 to allow for comparison, and the final vulnerability measures therefore have a possible range from 0 (no vulnerability) up to a maximum of 1 (maximum exposure and maximum sensitivity). The nine measures are as follows: (1) Land-Agricultural: Loss of important farmland; (2) Land-Domestic: Lack of new development in areas with housing needs; (3) Land-Ecological: Loss of critical habitats for endangered species; (4) Water-Agricultural: Increased water demand that cannot be fallowed (orchards/vineyards); (5) Water-Domestic: Household vulnerability to increased water inaffordability; (6) Water-Ecological: Drying of groundwater-dependent habitats for endangered species; (7) Climate-Agricultural: Increased irrigation water needs of crops; (8) Climate-Domestic: Household vulnerability to heat-related health impacts; (9) Climate-Ecological: Loss of runoff & recharge that keeps streams, ponds, and vernal pools wet. Each .zip file is a compressed file package containing maps of each measure under five scenarios, which have different sets of management assumptions along two axes, Water management Low/Moderate/High intensity and Land use management Low/Moderate/High intensity: - MM (Moderate / Moderate management intensity): a scenario where water demand caps under the Sustainable Groundwater Management Act (SGMA) reduce development in overdrafted groundwater basins based on current total water supplies, and where prime farmland and groundwater recharge areas will be protected from urban sprawl (i.e., land use projections assuming development stabilizes at a level sustainable with current water supplies, and urban sprawl limits). The other four scenarios differ from the MM scenario by altering one of these management strategies, while keeping the second strategy at the "Moderate" level. -- WL (Water management Low intensity): a pre-SGMA "business-as-usual" scenario where water demand is uncoupled from land-use change and does not need to stabilize at sustainable levels. -- WH (Water management High intensity): a scenario that assumes that water demand caps, but with increased caps due to enhanced water supplies proposed under local groundwater agencies' Groundwater Sustainability Plans. -- LL (Land use management Low intensity): a scenario where prime farmland and groundwater recharge areas are not protected from urban sprawl. -- LH (Land use management High intensity): a scenario where almost all the state's priority habitats are preserved from urbanization or agricultural expansion.

This project includes a pdf capture of a webpage and the underlying data for the visualizations in both csv and Tableau formats.On July 28-30, 2017, the VA Office of Enterprise Integration and the University of California Agriculture and Natural Resources (UCANR) convened a 48-hour collaborative event at the Urban Hive in Sacramento, California, to encourage the development of innovative solutions to spark entrepreneurship and bring together the seemingly disparate worlds of software development, commercial farming, and Veterans.Data about Veteran farmers by county was also used at Tableau’s Student Data Hackathon on July 31, 2018, where Washington D.C. area college students, who are Veterans, learned about Tableau products using data from VA’s Open Data and the Bureau of Labor Statistics to build data analytics skills creating data visualizations.

Mexico (FDI) Foreign Direct Investment: Agricultural: Baja California Sur data was reported at 0.000 USD th in Mar 2018. This stayed constant from the previous number of 0.000 USD th for Dec 2017. Mexico (FDI) Foreign Direct Investment: Agricultural: Baja California Sur data is updated quarterly, averaging 0.310 USD th from Mar 1999 (Median) to Mar 2018, with 77 observations. The data reached an all-time high of 761.928 USD th in Jun 2000 and a record low of 0.000 USD th in Mar 2018. Mexico (FDI) Foreign Direct Investment: Agricultural: Baja California Sur data remains active status in CEIC and is reported by National Institute of Statistics and Geography. The data is categorized under Global Database’s Mexico – Table MX.O003: Foreign Direct Investments: by Sector.

Graph and download economic data for Chain-Type Quantity Index for Real GDP: Agriculture, Forestry, Fishing and Hunting (11) in California (CAAGRQGSP) from 1997 to 2024 about hunting, forestry, fishing, quantity index, agriculture, GSP, private industries, CA, private, industry, GDP, and USA.

AuthorityIn the 1963 general session, the Utah State Legislature charged the Division of Water Resources with the responsibility of developing a State Water Plan. This plan is to coordinate and direct the activities of state and federal agencies concerned with Utah’s water resources. As a part of this objective, the Division of Water Resources collects water-related land use data for the entire state. This data includes the types and extent of irrigated crops as well as information concerning phreatophytes, wet/open water areas, dry land agriculture and urban areas.The data produced by the water-related land use program are used for various planning purposes. Some of these include: determining cropland water use, evaluating irrigated land losses and conversion to urban uses, planning for new water development, estimating irrigated acreages for any area, and developing water budgets. Additionally, the data are used by many other state and federal agencies.Previous MethodsThe land use inventory methods used by the division in conducting water-related land use studies have varied with regard to the procedures used and the precision obtained. During the 1960s and 70s, inventories were prepared using large format vertical-aerial photographs supplemented with field surveys to label boundaries, vegetation types, and other water use information.After identifying crops and labeling photographs, the information was transferred onto a base map and then planimetered or "dot-counted" to determine the acreage. Tables for individual townships and ranges were prepared showing the amount of land in each land use category within each section. Data were then available for use in preparing water budgets.In the early 1980s, the division began updating its methodology for collecting water-related land use data to take advantage of the rapidly growing fields of Remote Sensing and computerized Geographic Information Systems (GIS).For several years during the early 1980’s, the division contracted with the University of Utah Research Institute, Center for Remote Sensing and Cartography (CRSC), to prepare water-related land use inventories. During this period, water-related land use data was obtained by using high altitude color infrared photography and laboratory interpretation, with field checking.In March 1984, several division staff members visited the California Department of Water Resources to observe its methodology for collecting water-related land use data for state water planning purposes.Based on its review of the California methodology and its own experience, the division developed a water-related land use inventory program. This program included the use of 35mm slides, United States Geological Survey (USGS) 7-1/2 minute quadrangle maps, field-mapping using base maps produced from the 35mm photography and a computerized GIS to process, store and retrieve land use data.Areas for survey were first identified from previous land use studies and any other available information. The identified areas were then photographed using an aircraft carrying a high quality 35mm single lens reflex camera mounted to focus along a vertical axis to the earth. Photos were taken between 6,000 and 6,500 feet above the ground using a 24mm lens. This procedure allowed each slide to cover a little more than one square mile with approximately 30 percent overlap on the wide side of the slide and 5 percent on the slide's narrow side.The slides were then indexed according to a flight-line number, slide number, latitude and longitude. All 35mm slides were stored in files at the division offices and cataloged according to township, range and section, and quadrangle map location.Water-related land use areas were then transferred from the slide to USGS 7-1/2 minute quadrangle maps using a standard slide projector with a 100-200mm zoom lens. This step allowed the technician to project the slide onto the back of a quadrangle map. The image showing through the map was adjusted to the map scale with the zoom lens. Field boundaries and other water-use boundaries were then traced on the 7-1/2 minute quadrangle map.Next, a team was sent to use the map in the field to check the boundaries and current year land use field data on the 7-1/2 minute quadrangles.The final step was to digitize and process the field data using ARC/INFO software developed by Environmental Systems Research Institute (ESRI).Starting in 2000 with the land use survey of the Uintah Basin, the division further improved its land use program by using digital data for the purposes of outlining agricultural and other land cover boundaries. The division used satellite data, USGS Digital Orthophoto Quadrangles (DOQs), National Agricultural Imagery Program (NAIP), and other digital images in a heads-up digitizing mode for this process. This allowed the division to use multiple technicians for the digitizing process.Digitizing was done as line and polygon files using ArcView 3.2 with a satellite image, DOQ or NAIP image as a background with other layers added for reference. Boundary files were created in logical groups so that the process of edge-matching along quad lines was eliminated and precision increased. Subsequent inventories were digitized in the ArcMap 9.x software versions. Using the latest statewide NAIP Imagery and ArcGIS 10, all boundaries of individual agricultural fields, urban areas, and significant riparian areas are precisely digitized.Once the process of boundary digitizing is done, the polygons are loaded onto tablet PCs. Field crews are then sent to field check the crop and irrigation type for each agricultural polygon and label the shapefiles accordingly. Each tablet PC is attached to a GPS unit for real-time tracking to continuously update the field crew’s location during the field labeling process. This improved process has saved the division much time and money and even greater savings will be realized as the new statewide field boundaries are completed.Once processed and quality checked, the data is filed in the State Geographic Information Database (SGID) maintained by the State Automated Geographic Reference Center (AGRC). Once in the SGID, the data becomes available to the public. At this point, the data is also ready for use in preparing various planning studies.Present Methodology2017 marked the first year of using the CDL Method for the whole state of Utah. This method utilizes the Cropland Data Layer from the USDA's National Ag. Statistics Service which provides acreage estimates major commodities and to produce crop-specific geo-referenced products at 30m resolution. The CDL Method utilizes past line work digitized by the division and reconciles changes that may have occurred, including new crop types or ag-to-urban conversions.In conducting water-related land use inventories, the division attempts to inventory all lands or areas that consume or evaporate water other than natural precipitation. Areas not inventoried are mainly desert, rangeland and forested areas.Wet/open water areas and dry land agriculture areas are mapped if they are within or border irrigated lands. As a result, the numbers of acres of wet/open water areas and dry land agriculture reported by the division may not represent all such areas in a basin or county.During land use inventories, the division uses 11 hydrologic basins as the basic collection units. County data is obtained from the basin data. The water-related land use data collected statewide covers more than 4.3 million acres of dry and irrigated agricultural land. This represents about 8 percent of the total land area in the state.Due to changes in methodology, improvements in imagery, and upgrades in software and hardware, increasingly more refined inventories have been made in each succeeding year of the Water-Related Land Use Inventory. While this improves the data we report, it also makes comparisons to past years difficult. Making comparisons between datasets is still useful; however, increases or decreases in acres reported should not be construed to represent definite trends or total amounts of change up or down. To estimate such trends or change, more analysis is required.

The existing long-term Total Nitrogen (TN) and Total Phosphorus (TP) steady-state models, originally developed on a regional scale for the Pacific Region of the United States (Wise, 2019), were utilized to estimate TN and TP loads for the Clear Lake watershed. To support this analysis, input data for the Pacific Region model were updated to reflect climate, land use, and agricultural activities for the 2016-2024 period. Following these updates, the models were run in prediction mode, using model coefficients from the 2012 Pacific Region TN and TP SPARROW models. Model data set updates included incorporating the 2019 National Land Cover Data (NLCD), 2017 county-level applied agricultural fertilizer and manure data, updated streamflow data representing 2016-2024 conditions, and adjustments for areas affected by the 2017 and 2018 wildfires in the Clear Lake watershed. The revised Pacific Region SPARROW models were then run in prediction mode, and the resulting TN and TP load estimate ...

This dataset contains data and scripts that supplement the publication

Lichtenberg et al. (2017) A global synthesis of the effects of diversified farming systems on arthropod diversity within fields and across agricultural landscapes. Global Change Biology. DOI: 10.1111/gcb.13714

Please cite the above article if you use any of the included data or code.

Files are described in README.md.

These data are from soil salinity surveys conducted on California irrigated farmland between 1991 and 2017. The data consist of: (i.) geospatial field survey measurements of bulk soil electrical conductivity (ECa) and (ii.) laboratory determinations of soil salinity (ECe) and saturation percentage (SP) made on soil core sections extracted from the surveyed fields. The data consist of 277,624 ECa measurements and 8,575 ECe and SP determinations. Soil bulk electrical conductivity (ECa) is relatively easy to measure in agricultural fields using electromagnetic induction (EMI) instrumentation. EMI instruments are readily mobilized and thus can be used to characterize in detail the spatial variability of ECa within fields (Corwin, 2005; 2008). ECa is a useful property because it often correlates with difficult-to-measure soil physical and chemical properties that affect crop production, including soil water content, clay percentage, bulk density, PH, and especially soil salinity. The standard quantitative measure of soil salinity is defined to be the electrical conductivity of the soil saturation paste extract, or ECe (U.S. Salinity Laboratory Staff, 1954). Saturation percentage (SP) is the dry-weight moisture percentage of the saturation paste. The data can be used to test and explore model relationships between ECe, SP, and ECa (EMv and EMh), as well as their spatial variability. In particular, the data may be useful for comparing and testing modeling approaches that account for both deterministic and random components of soil spatial variability at single-field and multi-field scales, and to support high-resolution digital soil mapping studies across irrigated lands. Data Files Data are stored column-wise in two comma-delimited text files, ECe_USDA_ARS_USSL_v01.csv and ECa_USDA_ARS_USSL_v01.csv. Joining the files on the 'ID' column returns data for geolocations at which field measurements of ECa and laboratory determinations of ECe and SP both exist. For example: ECe

Abstract Sulfur (S) is widely used in agriculture, yet little is known about its fates within upland watersheds, particularly in combination with disturbances like wildfire. This dataset includes samples collected within the Napa River Watershed, California, U.S., where high S applications to vineyards are common, and ~20% of the watershed burned in October 2017. The data package includes soil, soil leachate, and stream chemistry data from sites representing a combination of land use (vineyard agriculture and grasslands) and burn (burned and unburned). Bulk soil chemical measurements include total sulfur and carbon concentrations and sulfur stable isotopes. We then used a laboratory rainfall experiment to simulate a wet season of precipitation in order to compare unburned and low severity burned vineyard and grassland soil leachate chemistry. Soil leachate measurements include total dissolved sulfur, sulfate, and dissolved organic carbon concentrations, sulfate-sulfur stable isotopes, and the specific ultraviolet absorbance at 254 nm (SUVA254), an index strongly correlated with DOC aromaticity. We compared soil leachate chemistry to stream samples draining sub-catchments with differing land use and degrees of burn and severity to understand combined effects at broader spatial scales. Soil and stream chemistry are provided in separate data tables, and data from the laboratory rainfall experiment is included in the leachingexpts (leaching experimental record), leachingexpchem (chemistry), and leachingexpisotopes (sulfur stable isotopes) data tables.