Research
Optimizing Agricultural Marketing and Pricing Decisions Through a Web-Based Decision Support System (DSS): A Data-Driven Approach for Farmers and Agribusinesses”
Key Focus Areas:
- Real-Time Market Price Tracking – Developing a web-based platform that aggregates and analyzes real-time agricultural commodity prices from multiple sources (e.g., government databases, e-NAM, AgriMarkNet, and private marketplaces).
- Demand-Supply Forecasting – Using predictive analytics and AI/ML models to forecast price trends based on historical data, weather patterns, and market demand.
- Farmers’ Access to Digital Marketplaces – Evaluating the impact of web-based platforms (e.g., mobile apps, online market linkages like e-Choupal, Kisan Suvidha) on reducing intermediaries and improving farmers’ profit margins.
- Blockchain for Transparent Pricing – Investigating how blockchain-based systems can enhance trust in agricultural supply chains by providing immutable transaction records.
- Personalized Pricing Recommendations – Implementing AI-driven advisory tools that suggest optimal selling times and locations based on crop type, local demand, and logistics costs.
- Policy & Government Initiatives Integration – Assessing the role of government-led web portals (e.g., AGMARKNET, mKisan) in improving price discovery and market access for smallholder farmers.
Potential Research Questions:
- How does a web-based agricultural pricing DSS improve farmers’ decision-making compared to traditional methods?
- What are the key challenges (digital literacy, internet access, data accuracy) in adopting such systems in rural areas?
- Can AI-based predictive pricing models reduce post-harvest losses by optimizing sales timing?
- How does blockchain ensure fair pricing and reduce exploitation by middlemen?
