SONU AGRAWALMANAGING DIRECTOR

INDIA HEADQUARTERS

G 31 Quest OfficesDLF Golf Course RoadGurgaon 122 002 India

GLOBAL RESEARCH CENTER

SIDBI Innovation CenterIndian Institute of TechnologyKanpur 208 016 India

NORTH AMERICA

350 Fifth Avenue59th Floor, New York CityNY 10118 USA

Making Crop Insurance Work for Farmers

Discussion Agenda

2

Complaints against Crop Insurance Doesn’t pay adequately when there are losses Claims ratio are low, - not adequate returns on subsidy Doesn’t cover farm level losses Doesn’t cover small farmers

PRODUCTS DISTRIBUTION SETTLEMENT MODERN FARMING

Sub-phase01 Mar to 30 AprPayout per hect.

Daily rainfall (mm) more thanTrigger Pay out (Rs.) Variable Pay out

Per mm (Rs.)

Nature of Cover multiple event

15 0 76.220 380.9 76.225 761.8 60.144 1904.4 Exit

1904.4

Premium 2433 (23%)

Max Possible Loss Ratio due to Unseasonal Rainfall

78%

Mirzapur WBCIS - Unseasonal Rainfall Cover

Bhadohi MNAIS:

•Estimated Loss Ratio due to Unseasonal Rainfall is around 500%

•Bhadohi is adjacent district of Mirzapur

PRODUCTS DISTRIBUTION SETTLEMENT MODERN FARMING

Disease Congenial Weather (DCW)

Cover Phase, From 15-Jul-15

To 15-Sep-15

Index C

Maximum of consecutive DCW days where DCW is a day when Daily Min Temp, Daily Max Temp and Daily Average RH is within the benchmarksBenchmarks

Daily Max Temp (>,<) 29.40 35.60

Daily Min Temp (>,<) 19.50 27.30

Average RH (>,<) 63.00 87.00Max Payout (Rs.) 30,000

Disease Congenial Weather (DCW)

Date From 1-Jul

Date to 30-Sep

Index Condition Definition

Maximum of consecutive days of Rainfall > 2.5 and HumidityEve > 40

Strike (>) Payoff (Rs.)

2 5,0004 12,500 7 25,000 9 40,000

12 60,000

Max Payout (Rs.) 60,000

Two approved termsheets for Pomegranate within same districts for locations that are just 15 kms apart

Average Payoffs not Product Design is the selection criteria Even Sum Insured under each peril is not the same – Would farmer trust us with his risks?

PRODUCTS DISTRIBUTION SETTLEMENT MODERN FARMING

What do these Examples Suggest

• Flaws are in the design not the Scheme• If objective is to cover actual losses, design basis risk can be

minimized significantly• How do we do that?

• Bringing Irrigation, Soil and Sowing Period in the Weather Equation

• Proven research done by WRL on improving efficacy of product by introducing Irrigation and Soil variables.

• Designing product based on farmers’ risk perception rather than University suggested Thresholds and Strike

• Shunning the payoffs and adopting

PRODUCTS DISTRIBUTION SETTLEMENT MODERN FARMING

Weather Insurance

Data Quality – Case Study We were asked to review data of one

location in Moradabad which

reported received rainfall far greater

than what our station was showing

Data was reviewed using TRMM

rainfall charts that showed possible

rainfall within the range of 8 mm to

35 mm.

No recognized disturbance in synoptic

Charts was found by Meteorologists

Meteorologists thus could establish

correctness of data provided

Data Quality - Dispute Resolution

Stations need to be checked with

IMD referenced prototypes

Iso-lines and homogenous regions

need to be identified

Need to recognize that two stations

at the same location can differ by

as much as 10% ( or even higher in

case of rains) – more stations in the

same hom. region needs to be

checked

Data from Distant stations need to

be adjusted for distance

ABOUT US CLIENTS PLATFORM HOW IT WORKS IMPACT EDGE

Coffee - KarnatakaYield Weather Insurance

Damage Assessment - Hail / Flood / Landslide

10

100 Meters

250 Meters

Object based hierarchical image analysis to classify imagery of plots

Measured concurrently on the ground using standard rangeland monitoring procedure

Objects are further classified into vegetative groups and to species level by Rule Based Classification.

Well defined thresholds and Near Neighbor Classification Algorithm is feasible.

Use of spectral camera to enhance results and assessment. UAVs use for mid-term surveys is more feasible. However, for yield assessment, there are challenges:

Regulations: Not allowed by DGCA if allowed would be flying max at 150 mtr height

Can cover max 300 ha in an hour long flight – District like Jodhpur would require 27 UAVs

Advocating the use of helicopter in assessing losses due to localized calamities

Contd..

11

Inundation Statusand Yield EstimationMonitor Yields through Satellite images from LISS4, LANDSA and SAR

LANDSAT images of 30m x 30m resolution. For more detailed analysis, LISS4 images of 5m x 5m resolution.

Where visibility is affected due to clouds, Microwave SAR data is used

2D or 3D flood models can be prepared to estimate crop loss in Flood prone region (Ref: DST, NECTAR Project)

PRODUCTS DISTRIBUTION SETTLEMENT MODERN FARMING

Agro + Meteorology

Growth Monitoring

Phenology

Yield = f ( NDVI GDD Rain Index Ancillary data )

NDVI

PrecipitationWater Holding

PAR / GDD

Crop Yield Estimate

Empirical Method

YPA = f(xi)Xi = Meteorological IndicesVegetative indicesDrought Index

YPA = Yield per Unit Area

Multi Source InputsRemote Sensing Imagery / Weather DataHistorical Weather Data / Fertilizer and InputsSoil Detail / Irrigation Detail / Ground Truth

Robust Modeling Stasny - Goel Bayesian Method / Griffith AR Method Standard Ratio Estimation / Econometric Method Agro-Met Methodology / GIS MethodRemote Sensing Methodology

An integrated crop yield forecasting model adopting advanced remote sensing imagery, geographical information and appropriate statistical methodologies such as multivariate regression.

Yield Weather Insurance

Y = 0.78 - 0.0002*RF+ 0.00096*GDD - 0.0329*HDD + 3.006* Oct -1.21*Nov -8.61*Dec

R square : 0.67

Y = 18.76 - 0.0009*RF - 0.002*GDD + 0.025*HDD -11.63*Oct + 4.88*Nov - 4.24*Dec

R square : 0.79

Y = 2.601 - 0.0001*RF -0.00007*GDD - 0.002*HDD + 2.964*Sep - 0.023*Oct -

5.042*NovR square : 0.54

(Overall : Uttar pradesh, West Bengal and Bihar)

Model Results

Dividing crop period into stages

Vegetative : Count of consecutive unfolded leaves, until the reproductive parts are visibleReproductive : As soon as flowers / tuber / ear head are visible until all kernels / seed / tuber are physiologically mature

Damage based on parts of the crop

Crop Stand Damage : Count or % of crop stand area with no living axils / budsCrop Stem Damage : Count or % of crop stem snapped off with inability to yield or inactiveBranch Damage : Position and % of branches snapped off or damagedLeaf Damage : Count and % of leaves snapped off, shredded, de-colorized and inactiveEar / Pod / Head / Boll Damage : Count and % of yield part knocked off / chaffed / shriveled /broken or disease / pest infected

Fruit Damage Count and % of fruits / tree knocked off / malformed / disease / pest infected

Crop Yield estimation before Harvest

Locating representative sample area. Determining plant stand, row width & density ( plant / ear / fruit / pod ) sample population / 100 m2. Estimating yield based on observations.

In Season Crop Damage

PRODUCTS DISTRIBUTION SETTLEMENT MODERN FARMING

PRODUCTS DISTRIBUTION SETTLEMENT MODERN FARMING

Yield Insurance

Crop Cutting Experiments by Independent Third Parties with Government & Insurers Monitoring it on sample basis

Established Statistical & Parametric Models to estimate yield using function of NDVI & Weather Parameters as explained in product slides earlier Would minimize number of crop

PRODUCTS DISTRIBUTION SETTLEMENT MODERN FARMING

Pertinent Issues: Loanee Farmer

Our Research suggests up to 30% error in RUA / Station mapping by Banks

Loan is often offered for crop with highest Scale of Finance rather than crop actually sown

Bankers don’t keep a record of last year or season’s submission to make quick / correct MIS

VILLAGE FARMERS RUA BANK MISBALRASAR 1 CORRECTBARDADAS 79 CORRECTBHAMASI 86 CORRECTBINASAR 1 CORRECTBUNTIA 4 CORRECTDABALA 1 CORRECTDEPALSAR 1 INCORRECTDHADHAR 2 CORRECTKHANSOLI 109 INCORRECTRAMSARA 27 INCORRECTUNTWALIA 1 INCORRECT

Remote sensing can be used for crop identification of farmers’ crop An application has been developed for bankers to quickly prepare and save

farmer MIS for multiple future useApplication would also minimize human errors relating to MIS that have

caused non-payment of claims to many eligible farmers

PRODUCTS DISTRIBUTION SETTLEMENT MODERN FARMING

Secure browser based application allows easy access to bankers without compromising data security

Captures farmer information required for Insurance Database master of all the villages in and their Reference Unit Area / Weather

Station Banker doesn’t have to create new database every season

Automating Farmer MIS

PRODUCTS DISTRIBUTION SETTLEMENT MODERN FARMING

Pertinent Issues: Non-Loanee Farmer

Current Seasonality Discipline in some states exposes insurers to large adverse selection risks on non-loanee farmers (more in MNAIS).

Difficult to build team for non-loanee business if districts are allocated for one season or one year

Delay in notification in some states doesn’t allow us time to sell insurance to non-loanee farmers

Issues of ensuring KYC of non-loanee farmers

Discussion Agenda

19

Complaints against Crop Insurance Doesn’t pay adequately when there are losses Claims ratio are low, - not adequate returns on subsidy Doesn’t cover farm level losses Doesn’t cover small farmers

SONU AGRAWALMANAGING DIRECTOR

INDIA HEADQUARTERS

G 31 Quest OfficesDLF Golf Course RoadGurgaon 122 002 India

GLOBAL RESEARCH CENTER

SIDBI Innovation CenterIndian Institute of TechnologyKanpur 208 016 India

NORTH AMERICA

350 Fifth Avenue59th Floor, New York CityNY 10118 USA

Making Crop Insurance Work for Farmers