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Addressing Basis Risk Through Technologies

Srinivasa Rao Gattineni

eeMAUSAM Weather Risk Management Solutions

Early efforts

• Rainfall Insurance Scheme of 1920

• Various new schemes proposed during 1950s

• Crop Insurance Bill and Model scheme during 1960s

• Experimental schemes during 1970s

History of Crop Insurance in India

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History of Crop Insurance in India cont.. Area based schemes

• Pilot Crop Insurance Scheme (PCIS): 1979–84

• The Comprehensive Crop Insurance Scheme (CCIS): 1985–1999

• The Experimental Crop Insurance Scheme (ECIS): 1997–1998

• The Pilot Scheme on Seed Crop Insurance (PSSCI): 1999-2000

• The Pilot Project on Farm Income Insurance Scheme (FIIS):

2003-2004

Weather / Area Based Schemes

• National Agriculture Insurance Scheme (NAIS): 1999 – 2013

• National Crop Insurance Program (NCIP): 2013 – 2014

• Modified National Agricultural Insurance Scheme (MNAIS):

2010 -2014

• Weather Based Crop Insurance Scheme (WBCIS):

Since 2004

• Pradhan Mantri Fasal Bhima Yojana (PMFBY): Since

2016

History of Crop Insurance in India cont..

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Pradhan Mantri Fasal Bima Yojana (PMFBY) • Provide comprehensive insurance coverage against crop loss on

account of non-preventable natural risks

• Plan is to reach 50% of the farmers in the next three years from 27%

• Risk coverage includes – pre-sowing to post-harvest losses

• Provision of claims upto 25% of sum insured for prevented sowing

• Area approach for settlement of claims for widespread damage;

while individual farm level assessment for localized calamity and post

harvest lossess

• Cluster approach will be adopted under which a group of districts

with variable risk profile will be allotted to an insurance company

through bidding for a duration upto 3 years to bring about

uniformity in premium rates

Pradhan Mantri Fasal Bima Yojana (PMFBY) cont..

• Participation from all provate insurance companies

• Farmer premium is at 2% for all “kharif” crops and 1.5% for all “rabi”

crops; while 5% for commercial and horticulture crops

• Total expected premium subsidy: 176 billion INR ($ 2.6 Billion)

• Premium would be shared by the central and state Govt. on 50:50

• Modern technology (satellite / Smartphones / Drones) will be

used for quick estimation of crop losses and settlement of claims

• Unified Package Insurance Scheme (UPIS) on pilot basis in 45

districts to cover farm machinery, life, accident, house and student-

safety for farmers along with their notified crops.

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Basis risk is the potential mismatch between contract

payouts and the actual loss experienced.

o Geographic basis risk (AWS Density)

o Product basis risk (Product Design)

o Idiosyncratic basis risk (Highly localized)

Basis Risk?

• Rainfall variability is dominant during the season

• Monsoon contribute 78% India’s annual rainfall

• Large variations in rainfall distribution (<10 cm in western desert to >1000 cm in northeast)

• Droughts and floods occur at different parts of the country at the same period and in the same place at different periods

Rainfall variability

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Farmers remain highly vulnerable to the weather in India

33

35

24

8

Total Very high rainfall

(>2000mm pa)

Low rainfall

(<750mm pa)

Medium rainfall

(750-1125mm pa)

159m ha

High rainfall

(1125-2000mm pa)

100% =

Agricultural area by rainfall class

Percentage of cropped area

About 1/3 of the

country is constantly

threatened by drought

Meanwhile, 1/6 of other

parts of the country

are threatened by

floods

Source: Government of India

Govt. Network: 8,000

Private Network: 7,000

AWS / ARG Network in the Country (~ 15,000)

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25 mm 100 mm

Rainfall spatial variability – Basis risk

A B

Issues to improve basis risk?

Item Details Total

Cost of one AWS (USD) 5,000 319,29,80,000

Maintenance per year (USD) 500 159,64,90,000

No. of Villages in India 638,596

Total over 5 years (USD) 4,789,470,000

• One AWS at every village

• Need to replace the sensors after 5 years

• Where to install? How representative the location is?

• Data tampering and quality still be a big issue?

• No historical data available for new locations

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Key elements: Monitoring Modeling Forecasting Local to Global Based on changes in biogeochemical Cycles

Comprehensive Data Management System

(Source: Niruthi CESPL)

Weather data at every kilometer - Virtual Weather Stations

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NOAA + local sources MODIS/ Landsat/IRS Various microwave data

• Air Temp • Relative Humiity • Solar exposure • Rainfall • Wind speed

• Satellite data without cloud • Resolution 250 / 16 / 23.5 m • Repetivity: Daily / 26 / 21 Days

• Soil moisture (during cloud)

WaterWatch data archive

Daily download

Storage

Download for selected days

Storage Storage

Import

Quality check Calculate

Import

Quality check NDVI + Albedo

calculation Fill

Import

Quality check Interpolation

(MeteoLook)

• NDVI • Albedo, LAI, fPAR, APAR • Soil Moisture

• Air Temperature • Relative Humidity • Transmissivity • Interception • Wind

Meteo data AQUA / TERRA data

Weekly Meteo Data

AMSR-E / ASCAT data

Satellite Data

• Land/Water Mask • Roughness & Displacement Height

Flexible Thematic Data

Static Additional Data (WW development)

Various Sources

• SRTM Digital Elevation • Land Use Map • Soil Type Map

One-time download

Storage

Import

Quality check Process

Static Thematic data

ETlook

ETlook Output Data at 8 Days interval

• Daily weather data • Actual Evapotranspiration • Potential Evapotranspiration • Crop Water Deficit (PET-AET) • Rainfall Surplus (P-AET) • Biomass production • Yield calculation at Harvest

ETlook Operational Flowchart

Download for every day

• Area mask • Land cover • Min stomatal resistance • Water content (saturated & residual) • Elevation

Static Thematic Data • Latitude • Jarvis Temperature • VPD slope coefficient • Annual T amplitude • Water heat storage coefficient • Tenacity factor • Soil resistance parameters • Light use efficiency

Validation of 10 years historical data through Crop Simulation Model, viz., InfoCrop, DSSAT Source:

eLEAF

Total Biomass rice [ton ha-1] 2011 & 2012

Source: eLEAF

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Government support

• Use of technology and models to estimate village yields

• Reduced number of CCEs through intelligent sampling

o Improved data accuracy

o Reduced fraudulent claims

o Transparent and tamper proof data systems

o Fast claim settlement

• Involvement of all private insurance companies and

other service providers along with international NGOs

• Involvement of community – Crowd Sourcing

• Use of technology to reduce basis risk

• Use of satellite data to gather information at macro level

i.e., up to village or a group of villages

• Use of communities to gather information at micro level

i.e., farm level

• Geo-tagging and geo-fencing of insured fields

Community Based Crop Insurance Model

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GPS and GPRS enabled hand-held devises GPS and GPRS enabled hand-held devises

Insurance Portal

Pilot Project on Technology Based Yield Estimation Techniques at Village Level for Crop

Insurance under NAIS in Maharashtra State” in PPP-IAD

Government of India has introduced a “National Crop Insurance Program” (NCIP) by merging Modified National Agriculture Insurance Scheme (NAIS), Weather Based Crop Insurance Scheme (WBCIS and Coconut Palm Insurance Scheme (CPIS) throughout the country from rabi 2013-14. Under restructured NCIP, unit area of insurance at Circle level has been reduced to the village / village panchayat level. Under MNIAS, ten Crop Cutting Experiments (CCEs) were mandatory; however, under NCIP, four CCEs for main crop and eight CCEs for secondary crops are mandatory at village level. Thus, reduction in unit area to village level leads to significant increase in the current level of CCEs, which would be highly resource intensive (monetary & labour) task. Therefore, State Governments has not introduced NCIP including Maharashtra state, so far. Hon. Chief Minister of Maharashtra State has directed to take up pilot projects on village level crop insurance during 2015-16 cropping season (i. e., kharif and rabi) using the advance technologies. Using the remote sensing and crop modeling techniques, village level productivity can be estimated. In advance methods, CCEs can be reduced by combining all the three techniques along with data on different weather parameters received from satellite, village level productivity for past seven years and current years productivity can be estimated. For this purpose, a proposal from eeMAUSAM is submitted to the Department of Agriculture, Government of Maharashtra to conduct a pilot program in three Circles (viz., Shendurwada, Manjri and Gangapur) covering 63 villages in Gangapur tehsil, Aurangabad district under “Public Private Participation – Intensive Agriculture Development” (PPP-IAD). Crops selected for kharif pilot are Bajra or Pearl Millet (Pennisetum glaucum), Macca or Maize (Zea mays) and Jowar or Sorghum (Sorghum bicolor).

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Insurance Portal cont..

Insurance Portal cont..

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Insurance Portal cont..

Conclusion

• Improved products & processes with public private participation

• Lower premium due to assured market, competitive bidding, price

discovery and portfolio risk management

• Better penetration through alternative market channels along with

existing stakeholders

• Technology support for better efficiency, qualified data, improved

transparency and quick claim settlements.

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Thank you