how to cite usda nass quick stats

Quickstats is the main public facing database to find the most relevant agriculture statistics. 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. As a result, R coders have developed collections of user-friendly R scripts that accomplish themed tasks. and predecessor agencies, U.S. Department of Agriculture (USDA). In fact, you can use the API to retrieve the same data available through the Quick Stats search tool and the Census Data Query Tool, both of which are described above. It is simple and easy to use, and provides some functions to help navigate the bewildering complexity of some Quick Stats data. nassqs_params() provides the parameter names, replicate your results to ensure they have the same data that you Moreover, some data is collected only at specific If you download NASS data without using computer code, you may find that it takes a long time to manually select each dataset you want from the Quick Stats website. developing the query is to use the QuickStats web interface. manually click through the QuickStats tool for each data They are (1) the Agriculture Resource Management Survey (ARMS) and (2) the Census of Agriculture (CoA). session. You can define the query output as nc_sweetpotato_data. Winter Wheat Seedings up for 2023, NASS to publish milk production data in updated data dissemination format, USDA-NASS Crop Progress report delayed until Nov. 29, NASS reinstates Cost of Pollination survey, USDA NASS reschedules 2021 Conservation Practice Adoption Motivations data highlights release, Respond Now to the 2022 Census of Agriculture, 2017 Census of Agriculture Highlight Series Farms and Land in Farms, 2017 Census of Agriculture Highlight Series Economics, 2017 Census of Agriculture Highlight Series Demographics, NASS Climate Adaptation and Resilience Plan, Statement of Commitment to Scientific Integrity, USDA and NASS Civil Rights Policy Statement, Civil Rights Accountability Policy and Procedures, Contact information for NASS Civil Rights Office, International Conference on Agricultural Statistics, Agricultural Statistics: A Historical Timeline, As We Recall: The Growth of Agricultural Estimates, 1933-1961, Safeguarding America's Agricultural Statistics Report, Application Programming Interfaces (APIs), Economics, Statistics and Market Information System (ESMIS). You might need to do extra cleaning to remove these data before you can plot. 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. function, which uses httr::GET to make an HTTP GET request ggplot(data = sampson_sweetpotato_data) + geom_line(aes(x = year, y = harvested_sweetpotatoes_acres)). The NASS helps carry out numerous surveys of U.S. farmers and ranchers. Its easiest if you separate this search into two steps. These include: R, Python, HTML, and many more. system environmental variable when you start a new R To use a restaurant analogy, you can think of the NASS Quick Stats API as the waitstaff at your favorite restaurant, the NASS data servers as the kitchen, the software on your computer as the waitstaffs order notepad, and the coder as the customer (you) as shown in Figure 1. Winter Wheat Seedings up for 2023, 12/13/22 NASS to publish milk production data in updated data dissemination format, 11/28/22 USDA-NASS Crop Progress report delayed until Nov. 29, 10/28/22 NASS reinstates Cost of Pollination survey, 09/06/22 NASS to review acreage information, 09/01/22 USDA NASS reschedules 2021 Conservation Practice Adoption Motivations data highlights release, 05/06/22 Respond Now to the 2022 Census of Agriculture, 08/05/20 The NASS Mission: We do it for you, 04/11/19 2017 Census of Agriculture Highlight Series Farms and Land in Farms, 04/11/19 2017 Census of Agriculture Highlight Series Economics, 04/11/19 2017 Census of Agriculture Highlight Series Demographics, 02/08/23 Crop Production (February 2023), 01/31/23 Cattle & Sheep and Goats (January 2023), 12/23/22 Quarterly Hogs and Pigs (December 2022), 12/15/22 2021 Certified Organics (December 2022), Talking About NASS - A guide for partners and stakeholders, USDA and NASS Anti-Harassment Policy Statement, REE Reasonable Accommodations and Personal Assistance Services, Safeguarding America's Agricultural Statistics Report and Video, Agriculture Counts - The Founding and Evolution of the National Agricultural Statistics Service 1957-2007, Hours: 7:30 a.m. - 4:00 p.m. Eastern Time Monday - Friday, except federal holidays Toll-Free: (800) 727-9540, Hours: 9:00 a.m. - 5:30 p.m. Eastern Time Monday - Friday, except federal holidays Toll-Free: (833) One-USDA Call 1-888-424-7828 NASS Customer Support is available Monday - Friday, 8am - 5pm CT Please be prepared with your survey name and survey code. 2020. The primary benefit of rnassqs is that users need not download data through repeated . You can check the full Quick Stats Glossary. That file will then be imported into Tableau Public to display visualizations about the data. A list of the valid values for a given field is available via 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. nassqs_auth(key = NASS_API_KEY). Need Help? Filter lists are refreshed based upon user choice allowing the user to fine-tune the search. There are times when your data look like a 1, but R is really seeing it as an A. Corn stocks down, soybean stocks down from year earlier Also, be aware that some commodity descriptions may include & in their names. 2017 Ag Atlas Maps. The census collects data on all commodities produced on U.S. farms and ranches, as well as detailed information on expenses, income, and operator characteristics. The ARMS is collected each year and includes data on agricultural production practices, agricultural resource use, and the economic well-being of farmers and ranchers (ARMS 2020). NASS Regional Field Offices maintain a list of all known operations and use known sources of operations to update their lists. The site is secure. 2020. rnassqs tries to help navigate query building with You can see whether a column is a character by using the class( ) function on that column (that is, nc_sweetpotato_data_survey$Value where the $ helps you access the Value column in the nc_sweetpotato_data_survey variable). However, it is requested that in any subsequent use of this work, USDA-NASS be given appropriate acknowledgment. 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. Web Page Resources Also, before running the program, create the folder specified in the self.output_file_path variable in the __init__() function of the c_usda_quick_stats class. You do this by using the str_replace_all( ) function. 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. Here, code refers to the individual characters (that is, ASCII characters) of the coding language. # check the class of Value column Going back to the restaurant analogy, the API key is akin to your table number at the restaurant. It allows you to customize your query by commodity, location, or time period. Be sure to keep this key in a safe place because it is your personal key to the NASS Quick Stats API. After running these lines of code, you will get a raw data output that has over 1500 rows and close to 40 columns. nass_data: Get data from the Quick Stats query In usdarnass: USDA NASS Quick Stats API Description Usage Arguments Value Examples Description Sends query to Quick Stats API from given parameter values. nc_sweetpotato_data <- select(nc_sweetpotato_data_survey_mutate, -Value) Statistics Service, Washington, D.C. URL: https://quickstats.nass.usda.gov [accessed Feb 2023] . The download data files contain planted and harvested area, yield per acre and production. The name in parentheses is the name for the same value used in the Quick Stats query tool. To submit, please register and login first. parameter. If the survey is from USDA National Agricultural Statistics Service (NASS), y ou can make a note on the front page and explain that you no longer farm, no longer own the property, or if the property is farmed by someone else. 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. 2017 Census of Agriculture - Census Data Query Tool, QuickStats Parameter Definitions and Operators, Agricultural Statistics Districts (ASD) zipped (.zip) ESRI shapefile format for download, https://data.nal.usda.gov/dataset/nass-quick-stats, National Agricultural Library Thesaurus Term, hundreds of sample surveys conducted each year covering virtually every aspect of U.S. agriculture, the Census of Agriculture conducted every five years providing state- and county-level aggregates. time you begin an R session. token API key, default is to use the value stored in .Renviron . Then we can make a query. The program will use the API to retrieve the number of acres used for each commodity (a crop, such as corn or soybeans), on a national level, from 1997 through 2021. do. The database allows custom extracts based on commodity, year, and selected counties within a State, or all counties in one or more States. (R coders say you need to load your R packages.) You can do that by running the code below (Section 7.2). The first line of the code above defines a variable called NASS_API_KEY and assigns it the string of letters and numbers that makes up the NASS Quick Stats API key you received from the NASS. Public domain information on the National Agricultural Statistics Service (NASS) Web pages may be freely downloaded and reproduced. For example, in the list of API parameters shown above, the parameter source_desc equates to Program in the Quick Stats query tool. R sessions will have the variable set automatically, Then, it will show you how to use Python to retrieve agricultural data with the NASS Quick Stats API. Note: In some cases, the Value column will have letter codes instead of numbers. If you use this function on the Value column of nc_sweetpotato_data_survey, R will return character, but you want R to return numeric. query. 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. To use a baking analogy, you can think of the script as a recipe for your favorite dessert. Website: https://ask.usda.gov/s/, June Turner, Director Email: / Phone: (202) 720-8257, Find contact information for Regional and State Field Offices. install.packages("rnassqs"). NASS makes it easy for anyone to retrieve most of the data it captures through its Quick Stats database search web page. In this example shown below, I used Quick Stats to build a query to retrieve the number of acres of corn harvested in the US from 2000 through 2021. nassqs_param_values(param = ). However, other parameters are optional. class(nc_sweetpotato_data$harvested_sweetpotatoes_acres) The surveys that NASS conducts collect information on virtually every facet of U.S. agricultural production. In addition, you wont be able However, if you only knew English and tried to read the recipe in Spanish or Japanese, your favorite treat might not turn out very well. equal to 2012. commitment to diversity. For example, you can write a script to access the NASS Quick Stats API and download data. NC State University and NC You can change the value of the path name as you would like as well. 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). ggplot(data = nc_sweetpotato_data) + geom_line(aes(x = year, y = harvested_sweetpotatoes_acres)) + facet_wrap(~ county_name) nc_sweetpotato_data_raw <- nassqs(nc_sweetpotato_params). About NASS. 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. There are at least two good reasons to do this: Reproducibility. Downloading data via Open source means that the R source code the computer code that makes R work can be viewed and edited by the public. Now that youve cleaned and plotted the data, you can save them for future use or to share with others. You can then visualize the data on a map, manipulate and export the results as an output file compatible for updating databases and spreadsheets, or save a link for future use. description of the parameter(s) in question: Documentation on all of the parameters is available at https://quickstats.nass.usda.gov/api#param_define. That is, the string of letters and numbers that represent your NASS Quick Stats API key is now saved to your R session and you can use it with other rnassqs R package functions. U.S. National Agricultural Statistics Service (NASS) Summary "The USDA's National Agricultural Statistics Service (NASS) conducts hundreds of surveys every year and prepares reports covering virtually every aspect of U.S. agriculture. use nassqs_record_count(). Note that the value PASTE_YOUR_API_KEY_HERE must be replaced with your personal API key. For docs and code examples, visit the package web page here . The agency has the distinction of being known as The Fact Finders of U.S. Agriculture due to the abundance of . The returned data includes all records with year greater than or Click the arrow to access Quick Stats. and rnassqs will detect this when querying data. the project, but you have to repeat this process for every new project, U.S. Department of Agriculture, National Agricultural Statistics Service (NASS). 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. An official website of the United States government. Grain sorghum (Sorghum bicolor) is one of the most important cereal crops worldwide and is the third largest grain crop grown in the United. Code is similar to the characters of the natural language, which can be combined to make a sentence. Writer, photographer, cyclist, nature lover, data analyst, and software developer. 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. Find more information at the following NC State Extension websites: Publication date: May 27, 2021 at least two good reasons to do this: Reproducibility. Generally the best way to deal with large queries is to make multiple Use nass_count to determine number of records in query. to quickly and easily download new data. Retrieve the data from the Quick Stats server. The query in Please note that you will need to fill in your NASS Quick Stats API key surrounded by quotation marks. Similar to above, at times it is helpful to make multiple queries and Due to suppression of data, the There is no description for this organization, National Agricultural Statistics Service, Department of Agriculture. You can verify your report was received by checking the Submitted date under the Status column of the My Surveys tab. The core functionality allows the user to query agricultural data from 'Quick Stats' in a reproducible and automated way. 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. You can also make small changes to the script to download new types of data. I built the queries simply by selecting one or more items from each of a series of dynamic dropdown menus. In this case, the NC sweetpotato data will be saved to a file called nc_sweetpotato_data_query_on_20201001.csv on your desktop. The chef is in the kitchen window in the upper left, the waitstaff in the center with the order, and the customer places the order.