This project aims to analyze and predict rural economic indicators—employment, personal income, and poverty levels—based on corn and soybean production and sales data in the United States over approximately 15 years. By examining crop-related production and sales metrics, this analysis seeks to identify correlations between crop production trends and rural economic development. We will investigate whether corn and soybean production data can be used to forecast changes in rural economic indicators, assessing both current and future impacts of crop productivity and sales on rural communities. The goal is to explore the predictive potential of corn and soybean data for forecasting key economic outcomes in rural areas.
Goals
- Analyze the influence of corn and soybean production on rural economic indicators (employment, income, poverty).
- Identify correlations between corn and soybean production metrics (e.g., acres planted, yield, production volume, cash receipts) and rural economic development indicators.
- Forecast economic outcomes based on corn and soybean production trends (can corn or soybean production be used to predict employment, income, and poverty levels?).
Agricultural data was collected from USDA National Agricultural Statistics Service, economic data was collected from Bureau of Economic Analysis, and downloaded in .csv format. These datasets held the metrics of corn and soybean productions along with the economic factors corresponding with state and time. Steps were taken to standardize naming of columns and reshaping/merging datasets in order to conduct proper EDA and modeling on the metrics of corn and soybean production. Since the data was organized by state and time, we were able to identify which states did not record corn/soybean data and properly account for these missing values in our EDA/modeling. No missing values needed to be addressed in the economic data sets. Once we were able to clean the data sufficiently, we merged these datasets in our preprocessing stage, which allowed us to work on modeling in a much more succinct manner.
EDA
- Corn and soy farming are significant contributors to the U.S. economy, their economic influence is felt beyond just farming communities, raising incomes in corn-producing regions.
- Following a recession, the corn and soy industries are resilient and dependable due to their necessities in maintaining a stable country.
- Income and employment show positive correlations with corn and soybean production metrics.
Modeling
- Corn and soybean production data have strong predictive potential for employment and moderate potential for personal income in rural areas because they are likely closely tied to the agricultural sector, making crop production trends good indicators of rural employment and income.
- Poverty levels, seem to be influenced by more complex factors beyond crop productivity like policy, local economic conditions, and demographic factors.
- Future research could explore integrating other datasets like government assistance programs and local economic policies.
The following Python libraries were used to conduct our research:
- Pandas
- Numpy
- Scikit-learn
- Statsmodels
- Seaborn
- Matplotlib