## Objective 2: To perform a Spatial Regression Analysis to Generate a Prediciton for Future Crop Yields

The predicted crop output was computed using a basic regression formula. Equation 1, was derived from information obtained from multiple previous studies investigating the relationships that precipitation and temperature have on agriculture output (Bannayan, et al 2010; Veron, et al 2015; Xuan, 2016). These previous studies contributed two main factors into the equation: that temperature has double the value of precipitation and that an increase in temperature has an exponential impact. Factoring in the exponential impact that temperature has alters the future formula to Equation 2 by accounting for the 1.5% increase in temperature from the past climate to the predicted climate. Using these two key ideas the equation was able to produce agricultural output using just the temperature and precipitation variables within 1% of the actual output. This was done by using the forumal shown above in excel with the temperature and precipitation average value for each specific latitude and longitude coordinate in each designated time period. This then gave each coordinate a crop output value that when summed with all crop output values for the time period would produce the average production of winter wheat for each year in the time frame.

**Equation 1 - Past & Present Crop Prediction Algorithm**

Output = 4*((Temperature*8)+(Precipitation*4))

**Equation 2 - Future Crop Prediction Algorithm**

Output = 4*((Temperature*(8*1.05))+(Precipitation*4))