How to Visualize the Effects of Subsidies on Agricultural Production Using EDA

To visualize the effects of subsidies on agricultural production using Exploratory Data Analysis (EDA), it’s crucial to understand the data you are working with, define the variables of interest, and then apply various visualization techniques. Below is a step-by-step approach you can follow:

1. Understanding the Data

Before diving into any visualizations, you need to have a good understanding of the dataset. Key variables to consider would typically include:

• Agricultural production data: This can include production values for crops, livestock, or overall agricultural output. It could be measured in tons, hectares, or monetary terms.

• Subsidy data: This can include direct subsidies provided to farmers, such as per-hectare subsidies, price support programs, or any other financial assistance given by the government.

• Time variable: The data might span across several years, so it is important to account for the temporal aspect of both subsidies and production.

Having a clear understanding of the dataset is critical as it helps in selecting appropriate visualizations and identifying patterns.

2. Data Cleaning and Preprocessing

• Missing Values: Handle any missing data by either filling or dropping the missing values.

• Data Types: Ensure that the data types (e.g., numeric, categorical) are correctly set.

• Normalization/Scaling: If subsidies and agricultural production have different scales (e.g., subsidies in millions vs. production in tons), normalization might be needed for better comparison.

3. Descriptive Statistics

• Summary Statistics: Start by generating summary statistics such as mean, median, standard deviation, etc., to understand the overall trends in agricultural production and subsidies.

• Correlation Analysis: Compute correlations between subsidies and agricultural production to check if there’s any significant linear relationship.

4. Visualization Techniques

a. Time Series Visualization

If your dataset includes temporal data (e.g., years), you can visualize the trend of agricultural production over time, with subsidies overlaid to see how changes in subsidies might correlate with production changes.

• Line Plot: Plot the agricultural production and subsidy data over time (e.g., years) to observe the trends. Use two y-axes if the scales of the two variables differ substantially.

python
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import matplotlib.pyplot as plt

# Assuming df is your dataframe with 'year', 'subsidy', and 'production' columns
fig, ax1 = plt.subplots()

ax1.plot(df['year'], df['production'], 'b-', label='Production', color='blue')
ax1.set_xlabel('Year')
ax1.set_ylabel('Production', color='blue')
ax1.tick_params(axis='y', labelcolor='blue')

ax2 = ax1.twinx()
ax2.plot(df['year'], df['subsidy'], 'r-', label='Subsidy', color='red')
ax2.set_ylabel('Subsidy', color='red')
ax2.tick_params(axis='y', labelcolor='red')

plt.title('Agricultural Production and Subsidies Over Time')
plt.show()

This visualization will give a clear understanding of how subsidies might be influencing agricultural production year over year.

b. Scatter Plot with Trend Line

Use a scatter plot to examine the relationship between subsidies and agricultural production. You can plot individual data points for each observation (e.g., year or region) and fit a regression line to visually show the trend.

python
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import seaborn as sns

sns.regplot(x='subsidy', y='production', data=df, scatter_kws={'color': 'blue'}, line_kws={'color': 'red'})
plt.title('Subsidy vs Agricultural Production')
plt.show()

This scatter plot will show if there’s a linear relationship between subsidies and production. The regression line will help understand the direction and strength of the relationship.

c. Box Plot or Violin Plot

If you want to understand how agricultural production varies with different levels of subsidies (for instance, low, medium, and high subsidy regions or countries), you can use a box plot or violin plot to visualize the distribution of agricultural production in these different subsidy categories.

python
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sns.boxplot(x='subsidy_category', y='production', data=df)
plt.title('Distribution of Agricultural Production by Subsidy Category')
plt.show()

The subsidy_category could be a categorical variable (e.g., “Low”, “Medium”, “High”) based on thresholds you define for subsidies.

d. Heatmap of Correlation Matrix

If you have multiple variables like subsidy type, geographical location, and production, a heatmap of the correlation matrix can help visualize how different factors interact with each other.

python
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import seaborn as sns
import numpy as np

correlation_matrix = df.corr()
sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', fmt='.2f')
plt.title('Correlation Matrix of Agricultural Production and Subsidies')
plt.show()

A heatmap will help you identify which variables are highly correlated and could potentially be important factors influencing agricultural production.

e. Bar Plot for Subsidies and Production by Region

If the dataset includes regional data, you can visualize how subsidies and production differ across various regions.

python
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sns.barplot(x='region', y='production', data=df, hue='subsidy')
plt.title('Agricultural Production by Region and Subsidy')
plt.show()

This will help you compare agricultural production across regions and how different levels of subsidy are affecting production.

f. Pair Plot

If you have multiple numerical features (e.g., different types of subsidies, agricultural outputs), a pair plot (or scatterplot matrix) will allow you to visualize all the relationships between them in a grid.

python
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sns.pairplot(df[['subsidy', 'production', 'land_area', 'crop_yield']])
plt.show()

This is helpful for quickly spotting any interesting relationships or correlations between multiple variables at once.

5. Interpreting the Results

Once you’ve visualized the data, it’s essential to analyze the insights:

• Does agricultural production increase with higher subsidies? This could be evident from the scatter plot or line graph with time.

• What is the variation in production across regions with different subsidy levels? This can be seen from box plots or bar charts.

• Are there non-linear patterns? Check if a simple linear regression line fits the data or if a more complex relationship exists.

• Is there a lag effect? Sometimes subsidies may take time to affect production, and this can be spotted in time series analysis.

6. Advanced Techniques (Optional)

If you wish to dig deeper, you can explore machine learning models to predict agricultural production based on subsidy levels and other factors:

• Regression Models: Fit linear, polynomial, or non-linear regression models.

• Time Series Forecasting: If your data spans many years, you could apply time series forecasting techniques like ARIMA to forecast future agricultural production under different subsidy scenarios.

You could also use tools like Shapley Values (from machine learning models) to explain how much each factor, including subsidies, affects the agricultural production.

Conclusion

Visualizing the effects of subsidies on agricultural production using EDA requires careful selection of techniques and understanding of the dataset. By leveraging tools such as line plots, scatter plots, box plots, heatmaps, and time series analyses, you can gain meaningful insights into how subsidies impact production patterns.