The document discusses the challenges and structure of crop insurance in India, emphasizing the prevalence of small farm holdings and the reliance on index insurance due to the lack of individual farm data. It outlines the workings of index insurance, recent innovations, and the associated risks such as basis risk and financial literacy. Key features include automated claims processes tied to weather events and the involvement of both public and private insurers, with government support playing a crucial role in funding and resolving disputes.

1. Crop Insurance in India
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2. The Agenda
Indian Agriculture & Risks
What’s Index Insurance?
Why Index Insurance?
Architecture of Indian Crop Insurance
Coverage: The Numbers
Index Insurance: Challenges
Weather Index: Recent Developments & Innovations

3. Indian Agriculture at a Glance
• Nearly 120 million farm holdings
• About 145 million hectares of cultivated area (~
190mha of gross cropped area)
• Small farm holding size (average of 1.2 ha)
• >80% small / marginal farmers, 62% own <1 ha
• About 50% of area is under cereals and millets

4. Indian Agriculture at a Glance
• 61% of Rural Households are Farming Households
• Provides 50% of the Employment
• Sustains 69% of Population
• Varied agricultural practices
• Predominantly Rain-fed Agriculture
• Large number of farmers produce for self consumption

5. Rainfall Variability
 Monsoons contribute 78% India’s annual rainfall
undergoes wide annual variations
 Large variations in distribution (10 to 1000cm)
 Disparity in the rainfall distribution is huge – leads to
simultaneous droughts and floods at different parts, same
place at different periods
 1/3rd of the country is under threat of drought
 1/6th of the country is prone to floods

6. Index Insurance
• Index insurance typically does not indemnify the pure loss,
but ex-ante agrees to make a payment upon the occurrence of
a triggering event
• ‘Homogenous area' approach based insurance is adopted
when reliable data of individual farmers is not available
• Area approach assumes that within an unit area annual yield
variability is similar for all farms thus forms a basic unit
• Area approach helps mitigate moral hazard of 'individual
approach' as all the insured in the unit area are treated at par

7. Index Insurance - Rationale
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Large number of Small sized Farm-holdings ( 120 million / 1.2 hectare)
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Non availability of individual farm level record of Yields, risk
management capabilities etc
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Low value of output per unit
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Collection of small premiums from large number of farmers
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Prohibitive cost of Manpower and Infrastructure
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Asymmetric Information
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Systemic nature of Agriculture risks

8. Crop Insurance System in India
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Predominantly Index based
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Credit linkage - presently compulsory, but need not be in future
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Cost of insurance is additionally financed by the credit institutions
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Insurance acts as collateral, lending agency has the first lien on claim
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Sum Insured is based on production cost – works as a safety-net
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Claims process is automated being ‘index’
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Multi-Agency Platform –convenient but insurer doesn’t have full control
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Insurance with social dimension as Government provides for about 2/3rd
cost of the program and has a larger say in dispute resolution
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Private insurers enjoy same level of support as public insurer

9. Modified NAIS: How it Works?
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Village-groups as Insurance unit for major crops for widespread losses
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Farm level assessment for hailstorm and landslide damages
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Farm level assessment for post harvest losses caused by cyclonic rains to
crop left for drying in ‘cut & spread’
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On-account payment up to 25% of likely claims in case of severe losses
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TY based on past seven years average yields excluding two years of
declared natural calamities
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Indemnity levels of 70%, 80% & 90%
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Actuarial Premium , Up-front premium subsidy by Government
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Uniform Seasonality discipline for Loanee & Non Loanee Farmers

10. Weather Index crop Insurance
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Reference Unit Area covered under a AWS
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Payout against deemed crop losses due to adverse weather incidence
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Peril covered are deficit rainfall, excess rainfall, Consecutive Dry/ Wet
Days, Heat / Frost & Mean Temp, Chilling Units, RH, Wind Speed,
Disease proxy
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Crops covered – all food crops, oilseeds, annual commercial & also
perennial horticulture crops like mango, apple, cashew, grapes ,orange
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Actuarial Premium rates with upfront premium subsidy from Govt.
Payouts based on pre-defined triggers on specified weather parameters

11. Major Challenges
• Product Basis Risk
• Spatial Basis Risk
• Financial Literacy
• Delivery Channels for Non-Borrowing Farmers
• Market Standards (vis-à-vis competition)
• Plethora of Government Support programs

12. Yield Index Insurance Basis Risk
• Challenges:
– Insurance Units are administrative, non-homogeneity
– Cost and Quality of Yield estimation surveys
• Solution:
– Lowering of the Insurance Unit
– Separate irrigated and rain-fed crop
– Satellite imagery (target sampling, yield modelling etc.)

13. Weather Index Insurance Basis Risk
• Challenges:
– Product Basis Risk
– Spatial Basis Risk
– Weather Station Infrastructure & maintenance
• Solution:
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Agronomic models

Low frequency & High Impact events (Catastrophe events)

Macro level Product
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Increased weather station density

Technologies to generate weather data at micro level (TOPS etc.)

14. Yield vs. Weather Index: Advantage/Challenges
Area Yield Index
Weather Based Index
All Risk Insurance – drought,
Multiple perils cover viz. rainfall –
flood, pest & diseases are
(excess & deficit), temperature (heat &
covered
frost), relative humidity, wind speed
Easy to design
Challenges in index design (peril, crop,
farming practices, agro-met zone etc.)
Low start-up costs
High start-up costs
High loss assessment costs
Low loss assessment costs
(CCEs)
Slow claims settlement
Faster claims settlement
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15. Recent Developments &
Innovations
• Index + (fruit crops)
• Loyalty Bonus for Non-Borrowing farmers
• Value added services (weather forecast etc.)
• Traditional Insurance at community level (using GPS enabled
HHDs)
• Double Trigger (Weather and Yield) Product (conceptual stage)
• Weather Secondary and Modelled outputs

16. Thanks!