The National Agricultural Statistics Service (NASS) is part of the United States Department of Agriculture. Queries that would return more records return an error and will not continue. Writer, photographer, cyclist, nature lover, data analyst, and software developer. queries subset by year if possible, and by geography if not. The USDA-NASS Quick Stats API has a graphic interface here: https://quickstats.nass.usda.gov. By setting prodn_practice_desc = "ALL PRODUCTION PRACTICES", you will get results for all production practices rather than those that specifically use irrigation, for example. Now that you have a basic understanding of the data available in the NASS database, you can learn how to reap its benefits in your projects with the NASS Quick Stats API. You can then visualize the data on a map, manipulate and export the results, or save a link for future use. . The Python program that calls the NASS Quick Stats API to retrieve agricultural data includes these two code modules (files): Scroll down to see the code from the two modules. commitment to diversity. While the Quick Stats database contains more than 52 million records, any call using GET /api/api_GET query is limited to a 50,000-record result set. a list of parameters is helpful. Now that youve cleaned and plotted the data, you can save them for future use or to share with others. The name in parentheses is the name for the same value used in the Quick Stats query tool. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely. You can view the timing of these NASS surveys on the calendar and in a summary of these reports. example, you can retrieve yields and acres with. Special Tabulations and Restricted Microdata, 02/15/23 Still time to respond to the 2022 Census of Agriculture, USDA to follow up with producers who have not yet responded, 02/15/23 Still time to respond to the 2022 Puerto Rico Census of Agriculture, USDA to follow-up with producers who have not yet responded (Puerto Rico - English), 01/31/23 United States cattle inventory down 3%, 01/30/23 2022 Census of Agriculture due next week Feb. 6, 01/12/23 Corn and soybean production down in 2022, USDA reports A script is like a collection of sentences that defines each step of a task. to automate running your script, since it will stop and ask you to NASS makes it easy for anyone to retrieve most of the data it captures through its Quick Stats database search web page. Its easiest if you separate this search into two steps. install.packages("rnassqs"). An API request occurs when you programmatically send a data query from software on your computer (for example, R, Section 4) to the API for some NASS survey data that you want. Columns for this particular dataset would include the year harvested, county identification number, crop type, harvested amount, the units of the harvested amount, and other categories. Production and supplies of food and fiber, prices paid and received by farmers, farm labor and wages, farm finances, chemical use, and changes in the demographics of U.S. producers are only a few examples. On the other hand, if that person asked you to add 1 and 2, you would know exactly what to do. NC State University and NC To submit, please register and login first. Combined with an assert from the National Agricultural Statistics Service (NASS) Quickstats can be found on their website. https://www.nass.usda.gov/Education_and_Outreach/Understanding_Statistics/index.php, https://www.nass.usda.gov/Surveys/Guide_to_NASS_Surveys/Census_of_Agriculture/index.php, https://project-open-data.cio.gov/v1.1/schema/catalog.jsonld, https://project-open-data.cio.gov/v1.1/schema, https://project-open-data.cio.gov/v1.1/schema/catalog.json, https://www.agcensus.usda.gov/Publications/2012/Full_Report/Volume_1,_Chapter_1_US/usappxa.pdf,https://www.agcensus.usda.gov/Publications/2007/Full_Report/Volume_1,_Chapter_1_US/usappxa.pdf, https://creativecommons.org/publicdomain/zero/1.0/, https://www.nass.usda.gov/Education_and_Outreach/Understanding_Statistics/index.php, https://www.nass.usda.gov/Surveys/Guide_to_NASS_Surveys/Census_of_Agriculture/index.php. The rnassqs package also has a 2020. list with c(). Then use the as.numeric( ) function to tell R each row is a number, not a character. you downloaded. The Census Data Query Tool (CDQT) is a web-based tool that is available to access and download table level data from the Census of Agriculture Volume 1 publication. class(nc_sweetpotato_data$harvested_sweetpotatoes_acres) We also recommend that you download RStudio from the RStudio website. Finally, it will explain how to use Tableau Public to visualize the data. rnassqs: Access the NASS 'Quick Stats' API. Once you have a Now that youve cleaned the data, you can display them in a plot. Parameters need not be specified in a list and need not be County level data are also available via Quick Stats. system environmental variable when you start a new R Programmatic access refers to the processes of using computer code to select and download data. This is why functions are an important part of R packages; they make coding easier for you. The types of agricultural data stored in the FDA Quick Stats database. for each field as above and iteratively build your query. Accessing data with computer code comes in handy when you want to view data from multiple states, years, crops, and other categories. When you are coding, its helpful to add comments so you will remember or so someone you share your script with knows what you were trying to do and why. By setting domain_desc = TOTAL, you will get the total acreage of sweetpotatoes in the county as opposed to the acreage of sweetpotates in the county grown by operators or producers of specific demographic groups that contribute to the total acreage of harvested sweetpotatoes in the county. time you begin an R session. R sessions will have the variable set automatically, To submit, please register and login first. install.packages("tidyverse") nassqs_auth(key = "ADD YOUR NASS API KEY HERE"). nassqs is a wrapper around the nassqs_GET Griffin, T. W., and J. K. Ward. There are R packages to do linear modeling (such as the lm R package), make pretty plots (such as the ggplot2 R package), and many more. Here is the most recent United States Summary and State Data (PDF, 27.9 MB), a statistical summary of the Census of Agriculture. The returned data includes all records with year greater than or key, you can use it in any of the following ways: In your home directory create or edit the .Renviron For example, in the list of API parameters shown above, the parameter source_desc equates to Program in the Quick Stats query tool. The .gov means its official. nassqs_params() provides the parameter names, Often 'county', 'state', or 'national', but can include other levels as well", #> [2] "source_desc: Data source. Plus, in manually selecting and downloading data using the Quick Stats website, you could introduce human error by accidentally clicking the wrong buttons and selecting data that you do not actually want. Read our Here we request the number of farm operators While Quick Stats and Quick Stats Lite retrieve agricultural survey data (collected annually) and census data (collected every five years), the Census Data Query Tool is easier to use but retrieves only census data. object generated by the GET call, you can use nassqs_GET to DSFW_Peanuts: Analysis of peanut DSFW from USDA-NASS databases. Accessed: 01 October 2020. A locked padlock For example, you will get an error if you write commodity_desc = SWEET POTATO (that is, dropping the ES) or write commodity_desc = sweetpotatoes (that is, with no space and all lowercase letters). description of the parameter(s) in question: Documentation on all of the parameters is available at https://quickstats.nass.usda.gov/api#param_define. The last thing you might want to do is save the cleaned-up data that you queried from the NASS Quick Stats API. For example, a (D) value denotes data that are being withheld to avoid disclosing data for individual operations according to the creators of the NASS Quick Stats API. Depending on what agency your survey is from, you will need to contact that agency to update your record. You can define the query output as nc_sweetpotato_data. # plot the data The use of a callback function parameter, not shown in the example above, is beyond the scope of this article. 1987. 2022. Copy BibTeX Tags API reproducibility agriculture economics Altmetrics Markdown badge to quickly and easily download new data. All of these reports were produced by Economic Research Service (ERS. You know you want commodity_desc = SWEET POTATOES, agg_level_desc = COUNTY, unit_desc = ACRES, domain_desc = TOTAL, statisticcat_desc = "AREA HARVESTED", and prodn_practice_desc = "ALL PRODUCTION PRACTICES". The United States is blessed with fertile soil and a huge agricultural industry. Web Page Resources which at the time of this writing are. NASS collects and manages diverse types of agricultural data at the national, state, and county levels. Here, tidy has a specific meaning: all observations are represented as rows, and all the data categories associated with that observation are represented as columns. Skip to 6. nc_sweetpotato_data_survey_mutate <- mutate(nc_sweetpotato_data_survey, harvested_sweetpotatoes_acres = as.numeric(str_replace_all(string = Value, pattern = ",", replacement = ""))) Quick Stats. Beginning in May 2010, NASS agricultural chemical use data are published to the Quick Stats 2.0 database only (full-text publications have been discontinued), and can be found under the NASS Chemical Usage Program. There is no description for this organization, National Agricultural Statistics Service, Department of Agriculture. http://quickstats.nass.usda.gov/api/api_GET/?key=PASTE_YOUR_API_KEY_HERE&source_desc=SURVEY§or_desc%3DFARMS%20%26%20LANDS%20%26%20ASSETS&commodity_desc%3DFARM%20OPERATIONS&statisticcat_desc%3DAREA%20OPERATED&unit_desc=ACRES&freq_desc=ANNUAL&reference_period_desc=YEAR&year__GE=1997&agg_level_desc=NATIONAL&state_name%3DUS%20TOTAL&format=CSV. With the Quick Stats application programming interface (API), you can use a programming language, such as Python, to retrieve data from the Quick Stats database. However, beware that this will be a development version: # install.packages ("devtools") devtools :: install_github ("rdinter . The following are some of the types of data it stores and makes available: NASS makes data available through CSV and PDF files, charts and maps, a searchable database, pre-defined queries, and the Quick Stats API. Quick Stats Lite provides a more structured approach to get commonly requested statistics from our online database. replicate your results to ensure they have the same data that you Corn stocks down, soybean stocks down from year earlier The CDL is a crop-specific land cover classification product of more than 100 crop categories grown in the United States. There are thousands of R packages available online (CRAN 2020). If you think back to algebra class, you might remember writing x = 1. Accessed online: 01 October 2020. The USDA Economics, Statistics and Market Information System (ESMIS) contains over 2,100 publications from five agencies of the . For example, commodity_desc refers to the commodity description information available in the NASS Quick Stats API and agg_level_desc refers to the aggregate level description of NASS Quick Stats API data. It accepts a combination of what, where, and when parameters to search for and retrieve the data of interest. RStudio is another open-source software that makes it easier to code in R. The latest version of RStudio is available at the RStudio website. head(nc_sweetpotato_data, n = 3). Quick Stats Lite provides a more structured approach to get commonly requested statistics from our online database. Then we can make a query. Now you have a dataset that is easier to work with. Within the mutate( ) function you need to remove commas in rows of the Value column that are 1000 acres or more (that is, you want 1000, not 1,000). Section 207(f)(2) of the E-Government Act of 2002 requires federal agencies to develop an inventory of information to be published on their Web sites, establish a schedule for publishing information, make those schedules available for public comment, and post the schedules and priorities on the Web site. USDA National Agricultural Statistics Service Information. The USDAs National Agricultural Statistics Service (NASS) makes the departments farm agricultural data available to the public on its website through reports, maps, search tools, and its NASS Quick Stats API. Many coders who use R also download and install RStudio along with it. Here are the pairs of parameters and values that it will submit in the API call to retrieve that data: Following is the full encoded URL that the program below creates and sends with the Quick Stats API. In the example program, the value for api key will be replaced with my API key. Multiple values can be queried at once by including them in a simple sampson_sweetpotato_data <- filter(nc_sweetpotato_data, county_name == "SAMPSON") Install. A Medium publication sharing concepts, ideas and codes. Need Help? Any person using products listed in . it. Data are currently available in the following areas: Pre-defined queries are provided for your convenience. Access Quick Stats (searchable database) The Quick Stats Database is the most comprehensive tool for accessing agricultural data published by NASS. want say all county cash rents on irrigated land for every year since A list of the valid values for a given field is available via The API request is the customers (your) food order, which the waitstaff wrote down on the order notepad. Using rnassqs Nicholas A Potter 2022-03-10. rnassqs is a package to access the QuickStats API from national agricultural statistics service (NASS) at the USDA. In this case, youre wondering about the states with data, so set param = state_alpha. You can also export the plots from RStudio by going to the toolbar > Plots > Save as Image. If you use it, be sure to install its Python Application support. following: Subsetting by geography works similarly, looping over the geography api key is in a file, you can use it like this: If you dont want to add the API key to a file or store it in your ggplot(data = nc_sweetpotato_data) + geom_line(aes(x = year, y = harvested_sweetpotatoes_acres)) + facet_wrap(~ county_name) Where available, links to the electronic reports is provided. NASS develops these estimates from data collected through: Dynamic drill-down filtered search by Commodity, Location, and Date range, (dataset) USDA National Agricultural Statistics Service (2017). variable (usually state_alpha or county_code The second line of code above uses the nassqs_auth( ) function (Section 4) and takes your NASS_API_KEY variable as the input for the parameter key. In this publication, the word parameter refers to a variable that is defined within a function. Each language has its own unique way of representing meaning, using these characters and its own grammatical rules for combining these characters. The Census Data Query Tool (CDQT) is a web based tool that is available to access and download table level data from the Census of Agriculture Volume 1 publication. The USDA NASS Quick Stats API provides direct access to the statistical information in the Quick Stats database. To use a baking analogy, you can think of the script as a recipe for your favorite dessert. This article will show you how to use Python to retrieve agricultural data with the NASS Quick Stats API. The author. In the example shown below, I selected census table 1 Historical Highlights for the state of Minnesota from the 2017 Census of Agriculture. The primary benefit of rnassqs is that users need not download data through repeated . NASS conducts hundreds of surveys every year and prepares reports covering virtually every aspect of U.S. agriculture. Once in the tool please make your selection based on the program, sector, group, and commodity. do. This publication printed on: March 04, 2023, Getting Data from the National Agricultural Statistics Service (NASS) Using R. Skip to 1. One way of multiple variables, geographies, or time frames without having to Next, you can use the select( ) function again to drop the old Value column. As an example, one year of corn harvest data for a particular county in the United States would represent one row, and a second year would represent another row.