is to develop a yield forecasting model based on the relationship between U.S. average soybean condition ratings and U.S. average soybean yields over 1986-2019 and then generate forecasts for two different dates in 2020. 1. To begin, go to the USDA Quick Stats website at http://quickstats.nass.usda.gov/. Download all available crop condition ratings data for soybeans over 1986-2019. Be careful to only request data for the United States and not for individual states. If you also ask for individual states the resulting file will be large and contain a huge amount of data you do not need. Here are the specific steps: Program: select "Survey" Sector: select "Crops Group: select "Field Crops" Commodity: select "Soybeans" Category select: "Condition" Data item: select: "Soybeans-Condition, Measured in Pct Good and Soybeanss - Condition, Measured in Pet Excellent" (hold down CTRL to select multiple items) Geographic level: select "NATIONAL" State select: "US Total" Select time: "1986-2019" Period type: select "WEEKLY" Period: select "WEEK # 19 to WEEK #46" Finally hit the "GET DATA" button at the bottom Upper right soybeanser: click on spreadsheet to download the data spreadsheet 2. Open the spreadsheet file downloaded from the Quick Stats site. You shave have data on all weekly crop conditions ratings for each week during the 1986-2019 growing seasons. Your next step is to separate out the last crop conditions ratings for each year between 1986 and 2019. Put these ratings in chronological order on a different worksheet. Next, add the ratings together for the "Good" and "Excellent" categories. This will be one of the independent variables in your yield forecasting models. Call this variable SUM. Note that you will have 33 observations for SUM, one for each year over 1986-2019. 3. Go to the farmdoc site at: https://farmdoc.illinois.edu/decision-tools/agricultural-supply- and-demand-database. Download the Excel file with supply and demand data and copy the US soybeans yields for 1986-2019 into the same spreadsheet with your SUM observations. 4. Now, regress the US soybeans yield for 1986-2019 on a linear trend variable (1 to 34). Show Excel the output and interpret the meaning of the slope coefficient and the R- squared.