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5 Feb 2011 Sanjay Kaul NCSML Agri Insurance
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5 Feb 2011 Sanjay Kaul NCSML Agri Insurance






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5 Feb 2011 Sanjay Kaul NCSML Agri Insurance 5 Feb 2011 Sanjay Kaul NCSML Agri Insurance Presentation Transcript

  • National Collateral Management Services Limited Weather Data Infrastructure: Challenges and W Forward ay Accelerating Agri Insurance in India 5th February, 2011 1
  • Our Services End-to-end services across the value chain 2
  • Weather and Indian Agriculture High dependence on Weather • 60% of land holdings in India rain-fed • 90% of crop losses attributable to weather • Excessive wind speed • High relative humidity • Deficit or excess rainfall • High or low temperatures • Many areas prone to natural calamities like floods and drought • Diminishing ground water resources • Weather risk is the most significant volatile risk 3
  • India & Rainfall Seasonal Distribution of Rainfall No Season Duration Rainfall 1 Pre Monsoon March-May 10.4% 2 South West Monsoon June-September 73.4% 3 North East Monsoon October-December 13.3% 4 Winter Rain January-February 2.9% Cropped area Range various Classification No Rainfall under ranges of rainfall in IndiaArea Cropped 1 < 750 mm Low Rainfall 33% 2 750-1125 mm Medium Rainfall 35% 3 1125-2000 mm High Rainfall 24% 4 > 2000 mm Very High Rainfall 8% 4 * Source (IMD & MoA, GOI)
  • Weather based Crop Insurance Scheme Weather Index based insurance product Premium subsidy shared by the Government Weather indices could be Maximum/ Minimum Temperature, Relative Humidity, Excess/ Deficit Rainfall and/ combination of above or Replaces human subjective assessment with objective weather parameters 5
  • National Agricultural Insurance Scheme (NAIS) vs Weather basedCrop Insurance Scheme (W BCISSI No NAIS WBCIS Practically all risk insurance Covers only parametric weather related1 cover risks like temperature, humidity, rainfall etc. Technical challenges in designing weather indices and also correlating weather indices Easy to design if 10 years of2 with ensuing yield losses. Needs up to 25 historical yield data is available years’ historical weather data Basis risk related to rainfall can be very high3 High basis risk but moderate for other weather parameters Highly prone to Less prone to tampering/administrative4 tampering/administrative influence influence High loss assessment cost5 Low assessment cost (Crop cutting experiments) Lengthy/delayed claim6 Faster claim settlement settlement7 Reinsurance not easy to get Reinsurance is available 6
  • Weather Insurance - Key Challenges Lack of quality historical weather data other than rainfall Delay in getting weather data from government institutions High data cost of private data providers Immediate need to improve the weather station density Questions over the data supplied by the private players Accreditation of W eather Stations Lack of insurance education and awareness 7
  • Weather Station Infrastructure 3000 Automatic W eather Stations have been installed across the countryGovernment Data Providers  India Meteorological Department  Revenue Dept, Water Resource Dept etc.  Agriculture University  Research Institutes/StationsPrivate Data Providers  NCMSL  WRMS  Express Weather  Agro Com 8
  • NCMSL Journey Creation of network of weather stations across the country at relevant crop growing areas to monitor weather parameters at hourly interval First AW installed in May 2005 at Khanapur, S Maharashtra for ICICI Lombard India’s largest & first private organization to establish own network of 1000+ Automatic W eather Stations in India. 9
  • Progress Since May 2005 10
  • Weather Parameters Tracked Rainfall (amount and intensity) Temperature (min. and max.) Relative humidity W speed and direction ind Atmospheric pressure Heating Degree Day (HDD) Cooling Degree Day (CDD) Dew point
  • Weather Data Collection Near real time climate data collection from remote locations QC WeatherMan Database Dissemination
  • Step 1:Importing the raw weather data to WeatherMan
  • Step 2: Software application to check the data quality
  • Step 3: Validation of imported weather data as per given conditions
  • Step 4: Report generation as per the clientrequirement
  • Operationalization Under the security of local host Trained Service Engineer – timely monitoring Automation of the process Data quality check based on predefined parameters Storage and retrieval of data in desired format for dissemination
  • Weather Data SYNOP Data Climate DataData that are collected in real- Data that are quality controlled bytime at various stations around the respective agency where thethe globe and provided through data is collectedthe GTSMinimum Quality Checks Thorough Quality ChecksNormally provided four times a Provided within few hours today monthsUsed for Weather Forecast, Most appropriate for the WeatherAviation industry Insurance/Derivative Industry 18
  • Challenges Installation & Commissioning of Weather Stations on short notice Retrieval of data on daily basis from remote locations of India Tackling the possibility of data tampering incidences 19
  • W Forward ay Offline CCTV/ ebcam with recording facility W Dedicated Weather W Portal eb Public Private Partnership (PPP) Accreditation of Weather Stations Apex Enforcement Authority Standardization in data collection, archival and distribution 20
  • Thank You 21