It can return data for the 2012 and 2017 censuses at the national, state, and local level for 77 different tables. You can also refer to these software programs as different coding languages because each uses a slightly different coding style (or grammar) to carry out a task. return the request object. 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. You dont need all of these columns, and some of the rows need to be cleaned up a little bit. To run the script, you click a button in the software program or use a keyboard stroke that tells your computer to start going through the script step by step. Accessing data with computer code comes in handy when you want to view data from multiple states, years, crops, and other categories. Where available, links to the electronic reports is provided. = 2012, but you may also want to query ranges of values. Corn production data goes back to 1866, just one year after the end of the American Civil War. replicate your results to ensure they have the same data that you In file run_usda_quick_stats.py create the parameters variable that contains parameter and value pairs to select data from the Quick Stats database. The sample Tableau dashboard is called U.S. # check the class of Value column Access Quick Stats Lite . For example, say you want to know which states have sweetpotato data available at the county level. An official website of the United States government. One of the main missions of organizations like the Comprehensive R Archive Network is to curate R packages and make sure their creators have met user-friendly documentation standards. For example, we discuss an R package for downloading datasets from the NASS Quick Stats API in Section 6. However, ERS has no copies of the original reports. Quickstats is the main public facing database to find the most relevant agriculture statistics. The surveys that NASS conducts collect information on virtually every facet of U.S. agricultural production. to automate running your script, since it will stop and ask you to This article will provide you with an overview of the data available on the NASS web pages. It allows you to customize your query by commodity, location, or time period. Language feature sets can be added at any time after you install Visual Studio. First, you will rename the column so it has more meaning to you. is needed if subsetting by geography. Once the Quick Stats. Note: You need to define the different NASS Quick Stats API parameters exactly as they are entered in the NASS Quick Stats API. 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. NC State University and NC 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. nassqs is a wrapper around the nassqs_GET For example, you equal to 2012. provide an api key. A script is like a collection of sentences that defines each step of a task. rnassqs (R NASS Quick Stats) rnassqs allows users to access the USDA's National Agricultural Statistics Service (NASS) Quick Stats data through their API. 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. head(nc_sweetpotato_data, n = 3). In this case, you can use the string of letters and numbers that represents your NASS Quick Stats API key to directly define the key parameter that the function needs to work. The Quick Stats Database is the most comprehensive tool for accessing agricultural data published by NASS. By setting statisticcat_desc = "AREA HARVESTED", you will get results for harvest acreage rather than planted acreage. Visit the NASS website for a full library of past and current reports . API makes it easier to download new data as it is released, and to fetch For example, you can write a script to access the NASS Quick Stats API and download data. method is that you dont have to think about the API key for the rest of national agricultural statistics service (NASS) at the USDA. and you risk forgetting to add it to .gitignore. As an example, you cannot run a non-R script using the R software program. A locked padlock Be sure to keep this key in a safe place because it is your personal key to the NASS Quick Stats API. Finally, it will explain how to use Tableau Public to visualize the data. 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. You can get an API Key here. Sign Up: https://rruntsch.medium.com/membership, install them through the IDEs menu by following these instructions from Microsoft, Year__GE = 1997 (all years greater than or equal to 1997). The NASS helps carry out numerous surveys of U.S. farmers and ranchers. The returned data includes all records with year greater than or You will need this to make an API request later. commitment to diversity. 2017 Ag Atlas Maps. 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. 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. The use of a callback function parameter, not shown in the example above, is beyond the scope of this article. Code is similar to the characters of the natural language, which can be combined to make a sentence. some functions that return parameter names and valid values for those There are at least two good reasons to do this: Reproducibility. Building a query often involves some trial and error. In this publication, the word variable refers to whatever is on the left side of the <- character combination. The next thing you might want to do is plot the results. Public domain information on the National Agricultural Statistics Service (NASS) Web pages may be freely downloaded and reproduced. Have a specific question for one of our subject experts? modify: In the above parameter list, year__GE is the The database allows custom extracts based on commodity, year, and selected counties within a State, or all counties in one or more States. many different sets of data, and in others your queries may be larger The API Usage page provides instructions for its use. You can also export the plots from RStudio by going to the toolbar > Plots > Save as Image. Note: When a line of R code starts with a #, R knows to read this # symbol as a comment and will skip over this line when you run your code. We also recommend that you download RStudio from the RStudio website. You can view the timing of these NASS surveys on the calendar and in a summary of these reports. 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. As mentioned in Section 1, you can visit the NASS Quick Stats website, click through the options, and download the data. A Medium publication sharing concepts, ideas and codes. The agency has the distinction of being known as The Fact Finders of U.S. Agriculture due to the abundance of . Many coders who use R also download and install RStudio along with it. Skip to 6. 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 Next, you need to tell your computer what R packages (Section 6) you plan to use in your R coding session. You can then visualize the data on a map, manipulate and export the results, or save a link for future use. Read our For example, if youd like data from both Corn stocks down, soybean stocks down from year earlier Any opinions, findings, conclusions, or recommendations expressed in this publication are those of the authors and do not necessarily reflect the view of the U.S. Department of Agriculture. Source: National Drought Mitigation Center, 2019-67021-29936 from the USDA National Institute of Food and Agriculture. Most of the information available from this site is within the public domain. time you begin an R session. Washington and Oregon, you can write state_alpha = c('WA', assertthat package, you can ensure that your queries are Please note that you will need to fill in your NASS Quick Stats API key surrounded by quotation marks. After you run this code, the output is not something you can see. Install. Say you want to plot the acres of sweetpotatoes harvested by year for each county in North Carolina. You can define the query output as nc_sweetpotato_data. It is simple and easy to use, and provides some functions to help navigate the bewildering complexity of some Quick Stats data. Secure .gov websites use HTTPSA The types of agricultural data stored in the FDA Quick Stats database. request. You can check by using the nassqs_param_values( ) function. If you have already installed the R package, you can skip to the next step (Section 7.2). That is an average of nearly 450 acres per farm operation. Website: https://ask.usda.gov/s/, June Turner, Director Email: / Phone: (202) 720-8257, Find contact information for Regional and State Field Offices. rnassqs: An R package to access agricultural data via the USDA National Agricultural Statistics Service (USDA-NASS) 'Quick Stats' API. geographies. Federal government websites often end in .gov or .mil. In the get_data() function of c_usd_quick_stats, create the full URL. You can change the value of the path name as you would like as well. Tip: Click on the images to view full-sized and readable versions. An introductory tutorial or how to use the National Agricultural Statistics Service (NASS) Quickstats tool can be found on their website. may want to collect the many different categories of acres for every 'OR'). rnassqs tries to help navigate query building with To demonstrate the use of the agricultural data obtained with the Quick Stats API, I have created a simple dashboard in Tableau Public. The <- character combination means the same as the = (that is, equals) character, and R will recognize this. Multiple values can be queried at once by including them in a simple U.S. Department of Agriculture, National Agricultural Statistics Service (NASS). The inputs to this function are 2 and 10 and the output is 12. https://data.nal.usda.gov/dataset/nass-quick-stats. Besides requesting a NASS Quick Stats API key, you will also need to make sure you have an up-to-date version of R. If not, you can download R from The Comprehensive R Archive Network. Alternatively, you can query values This is less easy because you have to enter (or copy-paste) the key each Thsi package is now on CRAN and can be installed through the typical method: install.packages ("usdarnass") Alternatively, the most up-to-date version of the package can be installed with the devtools package. 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. NASS conducts hundreds of surveys every year and prepares reports covering virtually every aspect of U.S. agriculture. Including parameter names in nassqs_params will return a 2019. Do this by right-clicking on the file name in Solution Explorer and then clicking [Set as Startup File] from the popup menu. ) or https:// means youve safely connected to Quick Stats is the National Agricultural Statistics Service's (NASS) online, self-service tool to access complete results from the 1997, 2002, 2007, and 2012 Censuses of Agriculture as well as the best source of NASS survey published estimates. value. I built the queries simply by selecting one or more items from each of a series of dynamic dropdown menus. 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. Based on this result, it looks like there are 47 states with sweetpotato data available at the county level, and North Carolina is one of them. 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. USDA National Agricultural Statistics Service Information. *In this Extension publication, we will only cover how to use the rnassqs R package. Share sensitive information only on official, ~ Providing Timely, Accurate and Useful Statistics in Service to U.S. Agriculture ~, County and District Geographic Boundaries, Crop Condition and Soil Moisture Analytics, Agricultural Statistics Board Corrections, Still time to respond to the 2022 Census of Agriculture, USDA to follow up with producers who have not yet responded, 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), 2022 Census of Agriculture due next week Feb. 6, Corn and soybean production down in 2022, USDA reports All sampled operations are mailed a questionnaire and given adequate time to respond by developing the query is to use the QuickStats web interface. nc_sweetpotato_data_sel <- select(nc_sweetpotato_data_raw, county_name, year, source_desc, Value) Skip to 3. Web Page Resources to the Quick Stats API. 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). Accessed online: 01 October 2020. This tool helps users obtain statistics on the database. You can use the ggplot( ) function along with your nc_sweetpotato_data variable to do this. Writer, photographer, cyclist, nature lover, data analyst, and software developer. Peng, R. D. 2020. Website: https://ask.usda.gov/s/, June Turner, Director Email: / Phone: (202) 720-8257, Find contact information for Regional and State Field Offices. nassqs_parse function that will process a request object To put its scale into perspective, in 2021, more than 2 million farms operated on more than 900 million acres (364 million hectares). It allows you to customize your query by commodity, location, or time period. Some care You can use the select( ) function to keep the following columns: Value (acres of sweetpotatoes harvested), county_name (the name of the county), source_desc (whether data are coming from the NASS census or NASS survey), and year (the year of the data). In some cases you may wish to collect 2020. returns a list of valid values for the source_desc year field with the __GE modifier attached to