How to Visualize the Effect of Climate Change on Agricultural Output Using EDA

Climate change poses one of the most significant threats to global agriculture, affecting crop yields, food security, and farming practices. To understand and mitigate these impacts, it is essential to leverage data analysis tools. Exploratory Data Analysis (EDA) provides a framework for visualizing and uncovering hidden patterns within climate and agricultural datasets. Using EDA, researchers and policymakers can identify trends, assess risks, and guide decisions for more resilient agricultural systems.

Understanding the Relationship Between Climate and Agriculture

Agricultural output is influenced by various climatic variables such as temperature, precipitation, CO₂ concentration, and the frequency of extreme weather events. Climate change alters these parameters, resulting in potential shifts in growing seasons, crop suitability, pest and disease prevalence, and water availability. To visualize and interpret these effects using EDA, one must start with comprehensive and clean datasets covering both climate indicators and agricultural yields over time and space.

Data Collection and Preparation

Start by collecting the following datasets:

• Climate Data: Temperature, rainfall, humidity, CO₂ levels, solar radiation, and wind speed.

• Agricultural Output Data: Crop yields, planted area, harvest time, and production costs.

• Geographic and Temporal Coverage: Ensure data spans multiple years and various geographic regions to capture the variability induced by climate change.

Sources for these datasets include:

• FAOSTAT (Food and Agriculture Organization)

• World Bank Data

• NASA Earth Observations

• Local weather stations and agricultural departments

After collecting the data, cleaning is essential. Handle missing values, normalize scales (e.g., Celsius vs. Fahrenheit), and align time frames and geographic units. Convert data types appropriately and filter outliers to ensure reliable visualizations.

Exploratory Data Analysis Techniques

Once the data is prepared, apply EDA techniques to understand relationships and patterns:

1. Time Series Analysis

Use line plots to visualize how both temperature and crop yields change over time. For instance:

python
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import matplotlib.pyplot as plt

plt.figure(figsize=(12,6))
plt.plot(years, temperature, label='Average Temperature')
plt.plot(years, crop_yield, label='Crop Yield')
plt.legend()
plt.title('Temperature and Crop Yield Over Time')
plt.xlabel('Year')
plt.ylabel('Value')
plt.show()

This reveals if there’s a correlation between rising temperatures and declining agricultural productivity.

2. Scatter Plots and Correlation Matrices

Scatter plots can reveal direct relationships between two variables, such as rainfall and maize yield. Overlaying regression lines adds clarity.

Use correlation matrices (e.g., heatmaps) to assess how climate variables collectively impact output.

python
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import seaborn as sns
import pandas as pd

correlation_matrix = data.corr()
sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm')

3. Box Plots

Box plots help compare the distribution of crop yields under different climate conditions or across regions. This is effective for understanding variability and spotting anomalies.

4. Geospatial Visualization

Choropleth maps and geospatial heatmaps help visualize the geographic impact of climate change. For example, you can map wheat productivity across states or countries and overlay average temperature anomalies.

Use libraries like geopandas, folium, or plotly:

python
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import geopandas as gpd
import matplotlib.pyplot as plt

world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))
merged = world.merge(yield_data, on='country')
merged.plot(column='yield', cmap='YlGn', legend=True)
plt.title('Global Wheat Yield by Country')
plt.show()

5. Trend Decomposition

Decompose time series data to separate trend, seasonality, and residuals. This highlights whether declines in yield are gradual trends or driven by seasonal/weather noise.

python
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from statsmodels.tsa.seasonal import seasonal_decompose

result = seasonal_decompose(data['yield'], model='additive', period=12)
result.plot()
plt.show()

6. Anomaly Detection

Identify outlier years with unexpected crop output due to extreme weather events such as droughts or floods. Visualizing anomalies can help policymakers understand vulnerability and response needs.

7. Interactive Dashboards

Use tools like Tableau, Power BI, or Plotly Dash to create interactive dashboards that allow users to explore data dynamically—filtering by region, crop, or year.

Case Study Example: Visualizing Rice Yield vs. Rainfall in India

Assume you have annual rainfall and rice yield data for Indian states from 2000–2020.

1. Load and merge data on rainfall and rice yield.

2. Plot a scatter plot with rainfall on the x-axis and rice yield on the y-axis.

3. Use color-coding to differentiate years or states.

4. Overlay a regression line to assess the linear relationship.

5. Create a map showing state-wise average yield over time with rainfall intensity as an overlay.

Through this analysis, it might become evident that states with declining rainfall have also seen a dip in rice production, indicating the need for better irrigation or drought-resistant varieties.

Combining Climate Models and Historical Data

To enhance EDA insights, integrate projections from climate models (e.g., RCP 4.5 or RCP 8.5 scenarios) with historical agricultural data. This helps visualize future risks:

• Predict how a 2°C temperature rise might affect wheat yield in temperate regions.

• Model drought probability changes and visualize how they intersect with high-yield zones.

Use climate model outputs from CMIP6 or other sources, align them temporally and geographically with yield data, and simulate future scenarios using statistical or machine learning models.

Challenges and Considerations

• Data Granularity: Finer spatial and temporal resolution leads to better insights, but such data might not be available everywhere.

• Non-linear Relationships: Many climate-agriculture interactions are non-linear and require advanced modeling.

• Multiple Interacting Variables: Rainfall alone may not explain yield decline; temperature stress, soil quality, and pests could also play a role.

• Adaptation Measures: Policy interventions, irrigation, and genetically modified crops may mask or modify climate impact.

Conclusion

EDA is a powerful first step in visualizing and understanding the effects of climate change on agriculture. By employing a range of techniques—time series plots, correlation heatmaps, geospatial analysis, and anomaly detection—researchers can uncover critical patterns and generate insights that inform policy and resilience strategies. When augmented with climate projections and scenario analysis, EDA becomes a strategic tool for building climate-smart agriculture systems and ensuring food security in an uncertain future.