Teaching Guide - Crop Residue Management Case
This case demonstrates:
1. Acquire - Get the data
2. Parse - Structure the data properly
3. Filter - Remove noise, keep useful data
4. Mine - Examine hidden patterns
5. Represent - Simple visuals for complex relationships
6. Refine - Improve the representation
7. Interact - Provide impactful information to stakeholders
Tip
For Pradhan’s challenge: How can he effectively present farmer survey data to convince funding agencies?
Key Questions to Explore:
Start Simple: Use descriptive statistics, histograms, box plots, and scatter plots to understand distributions and outliers.
Temporal Comparison
Geographic Analysis
Impact Assessment
Tool & Equipment
Maps & Butterfly Charts - Best for temporal comparison
2019 Baseline:
2020 Progress:
Major Achievement
Sustainable practice adoption nearly doubled from 46% to 89% in just one year!
Comparing Benefits: 2019 vs 2020
| Impact Area | 2019 (Decreased) | 2020 (Decreased) | Trend |
|---|---|---|---|
| Water Consumption | 69% | 71% | ↗️ Improving |
| Fertilizer Consumption | 60% | 80% | ⬆️ Major improvement |
| Weed Infestation | 33% | 53% | ⬆️ Much better |
| Pest Infestation | 31% | 31% | ➡️ Stable |
Note
69-71% of farmers report reduced water consumption - critical for water-scarce Punjab and Haryana!
Most Popular Tools for CRM (2020):
Warning
Challenge: Tool availability doesn’t match efficiency. Super-SMS takes only 1.1 hrs/acre but few farmers can access it!
For CRM Data: Use Drill Down, Factors, and Contrasts story types
(Limited time series makes Change Over Time and Intersection less useful)
Start Broad → Get Specific
Level 1: Overall
89% sustainable adoption across both states
Level 2: By State
Level 3: By District
This reveals that different districts prefer different methods - important for customizing interventions!
What influences CRM method adoption?
Selected Metric: CRM Practice Adopted
↓
Contributing Factors:
├── Tools Available
│ ├── Type (Rotavator, Super Seeder, Mulcher)
│ ├── Source (Cooperative, Ownership, Rental)
│ └── Time Efficiency (hrs/acre)
├── Soil Conditions
│ └── Suitability for in-situ methods
└── Access to Markets
└── For ex-situ (collection/baling)Tip
Insight: Rotavator dominates not because it’s fastest, but because it’s most accessible through cooperatives!
2019 vs 2020 - What Changed?
Geographic Expansion:
Method Distribution:
Benefits Realization:
Tool Access:
For Pradhan’s Presentation to Funders:
Structure your narrative in 5-7 slides:
Design Principle
Each slide should tell ONE key insight. Use data visualization to support, not overwhelm.
Choosing the Right Chart:
Remember: Maximize data-ink ratio - remove unnecessary elements!
Engage your audience with:
Note
Tableau/Power BI Tip: Interactive dashboards let stakeholders explore data at their own pace and ask their own questions.
For Class Discussion:
Data Quality: What biases might exist in self-reported farmer surveys?
Causality: Can we prove the initiative caused the adoption increase, or are there confounding factors?
Sustainability: The data shows adoption, but will farmers continue these practices long-term?
Scalability: What evidence suggests this model will work in other states?
Economic Viability: Do the benefits outweigh costs for individual farmers?
In Data Analysis:
In Visualization:
To Convince Funding Agencies:
For Monitoring & Evaluation:
Adoption Metrics:
Impact Metrics:
Important
Combine survey data with objective measures (satellite imagery, AQI readings) for credibility.
What the data tells policymakers:
Bottom line: Stubble burning is a behavioral issue requiring financial support AND long-term policy framework.
Based on data insights:
What’s Working:
What’s Needed:
Students should learn:
For students to try:
Using the CRM dataset, create a 3-slide presentation for:
Tools: Tableau, Power BI, or R/Python
Time: 20-30 minutes
Present: 3-minute pitch to class
Books:
Articles:
Tools:
“Numbers have an important story to tell. They rely on you to give them a clear and convincing voice.”
— Stephen Few
The CRM case shows:
For saving the planet, we need both:
✅ Good data + ✅ Good storytelling = Real impact
Open Discussion Topics:
What visualization resonated most with you from the CRM case?
How would you present this data to different audiences (farmers vs policymakers vs corporates)?
What other data would strengthen the story?
How can we ensure data visualization leads to action, not just awareness?
Key Message:
Data visualization and storytelling are essential skills for communicating insights and driving change.
Practice: The more you work with data and tell stories, the better you become.
Remember: Your audience doesn’t care about your data - they care about what your data means for them.
Next Steps
Apply these principles to your own projects and case studies!