Addressing Basis Risk Through Technologies

Transcription

1 Addressing Basis Risk Through Technologies Srinivasa Rao Gattineni eemausam Weather Risk Management Solutions History of Crop Insurance in India 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 1

2 History of Crop Insurance in India cont.. Area based schemes Pilot Crop Insurance Scheme (PCIS): The Comprehensive Crop Insurance Scheme (CCIS): The Experimental Crop Insurance Scheme (ECIS): The Pilot Scheme on Seed Crop Insurance (PSSCI): The Pilot Project on Farm Income Insurance Scheme (FIIS): History of Crop Insurance in India cont.. Weather / Area Based Schemes National Agriculture Insurance Scheme (NAIS): National Crop Insurance Program (NCIP): Modified National Agricultural Insurance Scheme (MNAIS): Weather Based Crop Insurance Scheme (WBCIS): Since 2004 Pradhan Mantri Fasal Bhima Yojana (PMFBY): Since

3 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 studentsafety for farmers along with their notified crops. 3

4 Basis Risk? 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) Rainfall variability 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 4

5 Farmers remain highly vulnerable to the weather in India Agricultural area by rainfall class Percentage of cropped area 100% = 159m ha 33 About 1/3 of the country is constantly threatened by drought 35 Meanwhile, 1/6 of other parts of the country are threatened by floods 24 8 Total Low rainfall (<750mm pa) Medium rainfall ( mm pa) High rainfall ( mm pa) Very high rainfall (>2000mm pa) Source: Government of India AWS / ARG Network in the Country (~ 15,000) Govt. Network: 8,000 Private Network: 7,000 5

6 Rainfall spatial variability Basis risk A B 25 mm 100 mm Issues to improve basis risk? 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 Item Details Total Cost of one AWS (USD) 5, ,29,80,000 Maintenance per year (USD) ,64,90,000 No. of Villages in India 638,596 Total over 5 years (USD) 4,789,470,000 6

7 Comprehensive Data Management System Key elements: Monitoring Modeling Forecasting Local to Global Based on changes in biogeochemical Cycles (Source: Niruthi CESPL) Weather data at every kilometer - Virtual Weather Stations 7

8 NOAA + local sources Various Sources MODIS/ Landsat/IRS Various microwave data Meteo data Static Thematic data AQUA / TERRA data AMSR-E / ASCAT data Air Temp Relative Humiity Solar exposure Rainfall Wind speed Daily download Storage Import Quality check Interpolation (MeteoLook) One-time download ETlook Operational Flowchart SRTM Digital Elevation Land Use Map Soil Type Map Storage Import Quality check Process Satellite data without cloud Resolution 250 / 16 / 23.5 m Repetivity: Daily / 26 / 21 Days Download for selected days Import Quality check NDVI + Albedo calculation Fill Storage Download for every day Storage Soil moisture (during cloud) Import Quality check Calculate Weekly Meteo Data Air Temperature Relative Humidity Transmissivity Interception Wind Static Thematic Data Area mask Land cover Min stomatal resistance Water content (saturated & residual) Elevation Flexible Thematic Data Land/Water Mask Roughness & Displacement Height Satellite Data NDVI Albedo, LAI, fpar, APAR Soil Moisture Latitude Jarvis Temperature VPD slope coefficient Annual T amplitude Water heat storage coefficient Tenacity factor Soil resistance parameters Light use efficiency Static Additional Data (WW development) WaterWatch data archive ETlook Output Data at 8 Days interval Source: eleaf ETlook Validation of 10 years historical data through Crop Simulation Model, viz., InfoCrop, DSSAT Daily weather data Actual Evapotranspiration Potential Evapotranspiration Crop Water Deficit (PET-AET) Rainfall Surplus (P-AET) Biomass production Yield calculation at Harvest Total Biomass rice [ton ha -1 ] 2011 & 2012 Source: eleaf 8

9 Government support Use of technology and models to estimate village yields Reduced number of CCEs through intelligent sampling o o o o Improved data accuracy Reduced fraudulent claims Transparent and tamper proof data systems Fast claim settlement Involvement of all private insurance companies and other service providers along with international NGOs Community Based Crop Insurance Model 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 9

10 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 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 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). 10

11 Insurance Portal cont.. Insurance Portal cont.. 11

12 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. 12

13 Thank you 13