This blueprint evaluates AI systems' ability to provide accurate, practical agricultural guidance based on Digital Green's pioneering video-based extension methodology. Digital Green, founded by Rikin Gandhi, revolutionizes agricultural education through hyperlocal videos featuring local farmers demonstrating best practices in their own dialects and contexts.

What This Blueprint Tests: The evaluation covers essential farming knowledge spanning seed treatment, pest management, cultivation techniques, irrigation practices, and crop-specific guidance. Each prompt is paired with citations to actual educational videos from Digital Green's extensive library, representing real-world agricultural challenges faced by smallholder farmers.

Geographic and Cultural Context: This blueprint emphasizes Global South agricultural contexts, particularly Indian farming systems, reflecting Digital Green's primary operational areas. The questions address practical challenges in subsistence and small-scale commercial farming, including resource constraints, climate adaptation, and traditional vs. modern agricultural practices.

Methodological Significance: Digital Green's community-mediated video approach has reached millions of farmers across India, Ethiopia, Niger, and other developing regions. Their methodology combines local knowledge with scientific farming practices, delivered through trusted community mediators. This blueprint tests whether AI systems can provide similarly contextual, practical, and culturally appropriate agricultural guidance.

Key Agricultural Domains Covered:

• Seed Treatment & Crop Establishment: Pre-planting preparation, germination optimization, early-stage crop management
• Integrated Pest Management: Biological controls, sustainable pesticide use, pest identification and treatment timing
• Cultivation Techniques: Planting densities, spacing, soil preparation, and crop-specific management practices
• Water Management: Irrigation timing, water conservation, drainage, and moisture management
• Harvest & Post-Harvest: Optimal harvest timing, storage, and processing techniques

Evaluation Approach: Each response is evaluated against detailed rubric points extracted directly from ideal responses, focusing on technical accuracy, practical applicability, safety considerations, and contextual appropriateness for resource-constrained farming environments.