The Data Science for the Public Good (DSPG) Young Scholars program is an immersive summer program that engages students from across Iowa to work together on projects that address local and state government challenges around critical social issues relevant in the world today. Learn more about the program here.
The Iowa State Farm Food and Enterprise Development (FEED) is frequently asked for benchmarks on pricing of products both in retail and wholesale spaces. This occurs both within feasibility studies for new farm and food businesses and market assessments, as well as appropriate price points for selling to schools and early care sites, hospitals, universities, and other institutions. There was a need for additional data and research on the potential sales point for these wholesale products when many of our specialty crop producers across the state are operating in direct-to-consumer retail spaces. While data is available from the AMS and USDA (including the Agricultural Census), there is limited aggregation of sales for these products at the local level.
The **goal of the project was to develop a process that provides more localized and up to date information on regional food systems and prices around local products.** For the Final Presentation click here
Grower price - price the grower (ex- farmer) receives
Wholesale price - price received by the wholesaler if there is one between the grower and the retailer
Retail price - price received by the store or retail outlet
* Average price is based on sample data points
Computed average value of each variable at monthly level for years 2016 - 2021. For example: price of a commodity is calculated by taking average of each month for mentioned years
1. Historical USDA retail price data of apples and tomatoes in the Midwest region (USDA Market News) 2. Historical precipitation and temperature data from ISU mesonet: https://mesonet.agron.iastate.edu/ 3. Drought data in Story County, Iowa (drought.gov) 4. 10 Year US Interest Rate (USO_US_Equity) 5. Gold Bullion historical price (GLD_US_equity) 6. Crude oil futures (WTI) 7. BCOM Index Bloomberg Commodity Index (BCOM_Index)
Data was obtained from various sources. The ‘Code Link’ column contains link to code that utilizes the data for cleaning/analysis.
|No.||Data Source||Website Link||Code Link|
|1||Walmart - Fresh Fruits||Link||Link|
|2||Walmart - Fresh Vegetables||Link||Link|
|3||Hy-Vee - Fresh Fruits||Link||Link|
|4||Hy-Vee - Fresh Vegetables||Link||Link|
|5||Iowa Food Coop||Link||Link|
|6||Park Slope Food Co-op||Link||-|
|7||Prudent Produce - Fruit||Link||-|
|8||Prudent Produce - Vegetable||Link||-|
|9||USDA - AMS (Apple)*||Link||-|
|10||USDA - AMS (Strawberries)*||Link||-|
|11||USDA - AMS (Pears)*||Link||-|
|12||USDA - AMS (Tomatoes)*||Link||-|
|13||USDA - AMS (Green Pepper)*||Link||-|
|15||Sysco - Wholesale Restaurant Food Distributor||Private|
|16||Iowa State University(ISU) Dining||Private|
|19||Iowa Environmental Mesonet||Link||-|
|20||National Integrated Drought Information System||Link||-|
* Retrieved report from 2016-2021
The indicators were computed using public datasets obtained from different sources/agencies. Number of available years and granularity of the data varied across sources. Data sources for few indicators were not identified, therefore the project team could not compute the corresponding indicators. Detailed information about sources used can be found here. [Private]
Both data scraping and model building was implemented in Python. The google trends analysis was done in R. Corresponding code is available on:
The team brought together backgrounds in Computer Science, Data Science, Economics, and Political Science, with interests in applications of technical skills to achieve meaningful impacts for decision making processes related to products at the local level.