Forecasting the Future: A Predictive Modeling Approach to Deciphering Climate Change’s Impact on US-Grown Soybeans and Estimating County-Level Crop Yields
JSHS · 2025
Overview
Understanding climate change’s impact on soybeans, a versatile crop, through the use of yield modeling is critical for future food insecurity issues. This study aimed to determine if various climate change factors contributed to soybean yields, hypothesizi ng their importance to the calculation of yields, and to create a predictive model to forecast county -level yields. This study separated NOAA weather and USDA fertilizer data from various soybean-yielding counties into 7 variable categories and grouped tho se variables into high, midrange, and low -yield scenarios to compare against each other through ANOVA tests. The statistically significant variables (p - value<0.05), including all temperature and fertilizer variables, were constructed into a multiple linear regression analysis comparing against ~50 -year historical county -level NASS soybean yields. Then, a new model was created with the variables that contributed statistically significantly to the yield's variance, which included days over 32.22 degrees Celsi us, potash usage, and phosphate usage, along with the past year's yields. This model yielded an R -squared value of 0.651, with a correlation of 0.782 against actual yields on a testing set, when predicting county - level yields, and 0.946 for yearly overall yields. Using the model, an easy -to-use website was created for instant soybean yield predictions. With this, the government and farmers can cheaply predict crop yields for better preparation. Overall, with rising temperatures from climate change, this study highlighted through the use of predictive models how an increasing number of days greater than 32.22 degrees Celcius will be detrimental to soybean yields.
Competition history
- JSHS 2025
Resources
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