Future Farming in India

The report argues that while AI can address systemic challenges in Indian agriculture—such as low productivity, soil degradation, and price volatility—scaling these solutions requires a structured ecosystem. It proposes the IMPACT AI framework, which is organized into three pillars: 'Enable' (government-led strategy and infrastructure), 'Create' (industry-led innovation and validation), and 'Deliver' (last-mile integration through extension systems). The document details four primary AI use cases: macro crop planning, rapid soil-health analysis, pest prediction and control, and smart marketplaces, while identifying 15 critical datasets necessary to operationalize these technologies by 2030.

• AI has demonstrated immediate economic benefits for smallholder farmers in India. A pilot project in Telangana (Saagu Bagu) involving 7,000 chilli growers resulted in a 21% yield improvement, an 11% increase in unit prices, and a 9% reduction in fertilizer and pesticide use, increasing net profits by an average of $800 per acre per crop season.
• Significant barriers hinder the scaling of AI in Indian agriculture, including the low annual income of farmers (approximately $1,500), which limits their ability to pay for technology. Additionally, 85% of India's 150 million farmers are smallholders with an average landholding of 1.08 hectares, increasing the cost of rural delivery and pushing providers toward larger businesses.
• The report identifies four priority AI use cases to transform the agricultural value chain: AI-enabled macro crop planning to reduce price volatility; rapid soil-health analysis using spectroscopy to optimize nutrient use; AI-enabled pest prediction and control to reduce the estimated $36 billion annual loss to pests in India; and smart marketplaces to eliminate information asymmetry and reduce reliance on intermediaries.
• The proposed IMPACT AI framework is structured around three pillars: 'Enable', 'Create', and 'Deliver'. The 'Enable' pillar focuses on government-led digital public infrastructure and data-sharing policies; the 'Create' pillar emphasizes start-up innovation, academic collaboration, and the use of sandboxes for validation; the 'Deliver' pillar focuses on empowering the 200,000 agricultural extension officers to reach the last mile.
• To operationalize AI in agriculture by 2030, the report identifies 15 critical datasets. These include digital land records, crop calendars and yields, soil health data, satellite imagery, real-time mandi data, and annotated images of pests and diseases.
• The report emphasizes that AI adoption must be 'phygital', combining digital tools with physical on-the-ground interventions. It suggests integrating AI into existing platforms like Kisan call centres and the VISTAAR portal, and utilizing the Open Network for Digital Commerce (ONDC) as a marketplace gateway for AI services.

APA

World Economic Forum (2025). Future Farming in India. https://reports.weforum.org/docs/WEF_Future_Farming_in_India_A_Playbook_for_Scaling_Artificial_Intelligence_in_Agriculture_2025.pdf

Chicago

World Economic Forum. Future Farming in India. 2025. https://reports.weforum.org/docs/WEF_Future_Farming_in_India_A_Playbook_for_Scaling_Artificial_Intelligence_in_Agriculture_2025.pdf.

Wikipedia

{{cite report |author=World Economic Forum |title=Future Farming in India |date=August 2025 |url=https://reports.weforum.org/docs/WEF_Future_Farming_in_India_A_Playbook_for_Scaling_Artificial_Intelligence_in_Agriculture_2025.pdf |access-date=23 October 2026 |via=Climate Insights Directory}}

BibTeX

@techreport{worldeconomicforum2025future, author = {{World Economic Forum}}, title = {{Future Farming in India}}, institution = {World Economic Forum}, year = {2025}, month = aug, url = {https://reports.weforum.org/docs/WEF_Future_Farming_in_India_A_Playbook_for_Scaling_Artificial_Intelligence_in_Agriculture_2025.pdf}, urldate = {2026-10-23}, note = {Indexed by Climate Insights Directory} }