Slower 2025 growth reflects a strategic move from direct farmer sales to government partnerships, which have longer conversion timelines. Climate events in some operating states also contributed.
Even bottom quartile farmers earn more with a greenhouse. This demonstrates the model's effectiveness for struggling farmers, not just top performers.
2024 peak ($287) reflects favorable monsoon conditions and strong vegetable prices. 2025 decline partly reflects climate events in some operating states and a strategic shift toward government partnerships.
States like Madhya Pradesh have stronger markets. Bihar and Jharkhand face different market challenges. Within-state consistency demonstrates model reliability in each context.
Retention stabilizes over time—Year 4→5 drop is only 3-8% vs. 17-21% for Year 1→2. Farmers who stay past Year 3 become long-term adopters.
Each row = cohort start year. Shows % retained from Year 1 baseline.
Note: 2019-2021 cohorts have incomplete early season records due to data collection starting mid-program.
The 2024 cohort's Year 2 drop to 66% (vs. 77-83% for other cohorts) reflects 2025 challenges including climate events in some operating states and the transition to government partnerships.
Pre-2024 cohorts only (n=1,893) — excludes recent joiners who haven't had time to return
For every 10 farmers who start, 8 return for season 2, 6 reach season 3, and 4 become long-term adopters (4+ seasons)—strong retention that validates the model.
Note: 2020-2021 data is incomplete due to data collection starting mid-program
Crop seasons completed grew 13x in 3 years (359→4,647 from 2022-2024), demonstrating rapid operational scaling while maintaining farmer engagement.
Farmers who expanded to 2+ greenhouses (cumulative)
51 farmers have expanded to multiple greenhouses — a small but growing segment that demonstrates the model's scalability potential.
Telangana and Jharkhand lead in crop seasons completed, reflecting Kheyti's early presence and strong farmer engagement in these states.
This retention factor analysis was generated with AI assistance. The correlations shown are statistically significant but the causal interpretations require validation by a qualified researcher. Please validate independently before drawing conclusions or making operational decisions.
% of farmers who return for season 2+ by first-season harvest outcome
First-season harvest success is the strongest predictor of retention (3.2x effect). Focus support resources on helping new farmers succeed in their first season.
% of farmers who return for season 2+ by income quartile
Higher first-season income has a modest effect on retention (+7pp between top and bottom quartile). Harvest success matters more than income level.
How farmers were acquired affects long-term engagement
Government-referred farmers show lower retention in early data. However, this channel opened recently and many of these farmers may not yet have had the opportunity to complete additional seasons. It is also possible that repeat season data was not fully captured at the time of the data pull. This is an emerging pattern worth monitoring as the government partnership matures and more data becomes available.
Median income per crop season by farmer's nth season
Per-season income remains stable across experience levels ($120-154), suggesting the model delivers reliable results from the start rather than requiring a long learning curve.
Despite scaling from ~300 to 6,300+ farmers, median income held steady — suggesting training quality, greenhouse standards, and support services kept pace with rapid expansion.
Village outreach has historically driven the majority of adoption. Going forward, Kheyti is moving fully toward a government partnership model, with the government as a partial payer, to address affordability constraints for individual farmers.
45% use drip irrigation, 33% use mulching — water-efficient technologies support climate resilience.
Mixed cropping leads adoption — farmers diversify risk across vegetables, nurseries, and high-value crops.
This climate analysis was generated with AI assistance using publicly available regional data and research. It is intended as inspiration for further investigation—not as conclusive findings. The methodology and interpretations have not been vetted by a qualified researcher. Please validate independently before drawing conclusions.
Income comparison: Monsoon (Jun-Sep) vs Non-Monsoon seasons
Open-field vegetable yields typically drop 30-83% during monsoon due to rain damage and disease.1 Research shows protected cultivation yields 25-37% more than traditional methods during monsoon, with income more than doubling.2
Greenhouse income during extreme weather events
The +40% is driven by crop mix (high-value nursery crops grown pre-monsoon) and state composition—not climate protection. Same-crop analysis shows cucumbers earn 15% less during extreme heat. Research shows heat stress (>38°C) causes up to 80% flower loss in open-field tomatoes.3
Median income by harvest month with precipitation overlay
Peak income (June) aligns with pre-monsoon harvests when greenhouse protection matters most. Monsoon months (Jul-Sep) show sustained income as protected crops thrive while open-field yields suffer.
Income vs climate conditions by state (bubble size = crop seasons)
Income correlates with market access, not rainfall. The model works across diverse climate zones from semi-arid Rajasthan to tropical Odisha.
Research Citations:
1 India Data Portal, "Crop-wise Area, Production, Yield" (2015-2018). Kharif vs Rabi yields for potato (-56%), dry chillies (-83%) in Kheyti operating states. ckandev.indiadataportal.com
2 "Enhancing Tomato Productivity in Assam, India: A Comparative Study of Rain Shelter and Traditional Cultivation Methods" (2019-2021). Rain shelter: 30,371-31,657 kg/ha vs traditional: 22,290-24,098 kg/ha. Net income ₹597K-621K vs ₹270K-289K. Research Square
3 "An overview of heat stress in tomato (Solanum lycopersicum L.)" Saudi Journal of Biological Sciences (2021). Heat stress at >38°C causes 80% flower loss; day temps above 25°C significantly decrease fruit numbers and weight. PMC/NCBI
Open Datasets Used:
• Weather data: Open-Meteo Historical Weather API. Daily temperature, precipitation, and extreme heat days (2022-2025) for 7 Indian states. Free, open-source weather data. open-meteo.com
• Crop yield data: India Data Portal, Ministry of Agriculture. District-level area, production, and yield statistics (1997-2021) for major crops across all Indian states. ckandev.indiadataportal.com
• Kheyti farmer data: Internal operational data provided by Kheyti. Crop season records (2020-2026), farmer demographics, and income metrics. kheyti.com
Impact Dashboard