The document discusses improving crop insurance through technology and big data, addressing challenges such as farmer dissatisfaction and the need for scalable, farmer-centric schemes. It outlines a framework for acquiring, assimilating, and analyzing data from various sources, along with methods for loss assessment and claim settlement. The authors emphasize the importance of stakeholder involvement and the necessity for more evidence to enhance the efficiency and effectiveness of crop insurance programs.

1. Technologies and Big Data for
Improved Crop Insurance
Pramod Aggarwal and Team
CGIAR Research Program on Climate Change, Agriculture and Food Security,
BISA, CIMMYT, New Delhi-110012, India

2. Challenges in Crop Insurance
• Everyone loves farmer-driven
insurance: several pilots,
hundreds of thousands, reduced
basis risks, but moral hazards,
transaction costs, agro-ecological
diversity, scalability
• Top down scheme reaching
millions: farmer’s dissatisfaction-
spatial basis risk, product design,
loss assessment, claim
settlement delays

3. Desired: A scalable farmer-centric insurance
scheme for large regions

4. Technologies and Big Data Provide New
Opportunities for Such a Scheme

5. Harnessing Technologies and Big Data for
Improved Crop Insurance
1. Acquiring data: Farmers practices
and crop growth (digital geo-referenced pictures)
crowdsourced by mobile Apps, sensors in farmer’s
fields, gridded data, VIs, satellite rainfall, UAVs
2. Assimilating data: models,
statistics, geospatial techniques
3. Analyzing data: Assessment models,
optimization, machine learning
4. Application: ICT, Mobile Apps, multiple
data for MRV, digital banking

6. A Tool for Harnessing Technologies and
Big Data for Improved Crop Insurance
Crowd sourced
information from
farmers: practices,
pests and diseases
+
Digital
knowledge:
satellites, UAVs,
cameras
+
Spatial databases:
weather, soils,
management,
historical yields
Loss assessment:
mid-season, end-
season, post-
harvest
Product design:
triggers, premiums,
loss ratio, farmer
satisfaction
Claim settlement:
immediate mobile
notification, bank
transfers
Models
and
analytics
on cloud

7. Pilot Evaluation for Soybean in India
Five stages of yield loss
assessment (all need
different approaches)
1. Prevented sowing
2. Sowing failure
3. Mid-season loss
estimates
4. End-season
5. Post-harvest

8. Prevented Sowing
Data Source: CHIRPS data from 1981 to 2016
Post-harvest Loss Assessment
Yield Loss Assessment
4 Day: 50 mm
Two day rain > 125 mm
Length of Dry Spell > 20 Days
Farmer participatory large-scale loss assessment of
soybean for insurance (work in progress)

9. Prevented Sowing
Sowing Failure
Data Source: CHIRPS data from 1981 to 2016
Post-harvest Loss Assessment
Yield Loss Assessment
4 Day: 50 mm
Two day rain > 125 mm
Length of Dry Spell > 20 Days
Farmer participatory large-scale loss assessment of
soybean for insurance (work in progress)

10. Prevented Sowing
Sowing Failure
Data Source: CHIRPS data from 1981 to 2016
Post-harvest Loss Assessment
Yield Loss Assessment
4 Day: 50 mm
Two day rain > 125 mm
Length of Dry Spell > 20 Days
Farmer participatory large-scale loss assessment of
soybean for insurance (work in progress)

11. Prevented Sowing
Sowing Failure
Data Source: CHIRPS data from 1981 to 2016
Post-harvest Loss Assessment
Yield Loss Assessment
4 Day: 50 mm
Two day rain > 125 mm
Length of Dry Spell > 20 Days
Farmer participatory large-scale loss assessment of
soybean for insurance (work in progress)

12. • Farmers satisfaction
index-payment
when due and in
right amount
• Industry: 70-80 %
claim ratio
• Government:
Premium subsidy
not to increase
Improved triggers for weather insurance:
win-win products for farmers, industry and government

13. Improved triggers for weather insurance:
win-win products for farmers, industry and government
(work done in collaboration with AIC, Aon Benfield and Govt of Maharashtra)
0
10
20
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50
60
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80
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100
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Existing Contract Satisfaction Index-farmer Proposed Contract
Claimfrequency,%
SatisfactionIndex,Claimsratio
Satisfaction Index Claim ratio claim frequency, %

14.  Technologies and Big
Data for increasing the
efficiency and efficacy of
crop insurance
 Interest of all
stakeholders crucial
 More evidence needed
 Insurance versus
comprehensive risk
management- bundling
Looking forward: Building evidence, Systematic Learning
and scaling of Crop Insurance

15. Address generic vulnerability issues simultaneously – poverty,
literacy, governance, etc., which limit adaptation even today and
will do so in future as well
And….