strategic sampling with remote sensing

Strategic Sampling with Remote Sensing Case Study from India

November 2014

World Bank Technical Assistance Engagement with GOI AIC of India

Applied Geosolutions LLC

Table of contents 2

1. Background

2. Study methodology

3. Study results

4. Impact and replicability

India has a vibrant agriculture insurance market …

1. Background

Ex-ante financing arrangement

• Use remote sensing technology

• Use of smartphones

Actuarial design and ratemaking covering longer historical time periods

Private competes with public insurer

Ex-post financing arrangement

Crop cutting experiments (CCEs)

Simple calculation of premiums not reflecting actual risk exposure

No private sector involvement

Financing arrangement

Premiums

Claims settlement

process

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Involvement of private sector

Lower fiscal exposure More predictable budget Faster claims settlement

Lower basis Risk

Faster claims settlement

Lower adverse selection Better risk-signaling and

incentives to adapt to climate change

Enhanced efficiency

1999: Initial Public program (NAIS*)

2010: New PPP (mNAIS and WBCIS*)

Benefits 34 million farmers covered

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*NAIS: National Agriculture Insurance Scheme - mNAIS: Modified NAIS *WBCIS: Weather Based Crop Insurance Scheme

… where the World Bank had a long-standing engagement

1. Background

PILOT

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0

200

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Crop season with no payout Crop season with payout

Yiel

d (k

g pe

r hec

tare

)

Reference average yield

Coverage level at 80% of average yield

Yield shortfallto be compensatedby insurance payout

Area yield index insurance is a type of insurance which pays farmers with respect to

the reference average yield in the area

1. Background

• Low administration costs

• No moral hazard • No adverse selection • Captures a wide range of risks that can negatively

impact crop yields

Compared to individual farm insurance, AYII is easier to manage and is not affected by farmer behavior

1. Background

Costs

Feasibility

Quality

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However Area Yield Index Insurance is subject to basis risk, which occurs when

farmers incur production losses but do not receive payouts

Basis risk can be minimized by

1. Defining homogeneous producing zones (the Insured Units) with high levels of correlation between farmers of the same Insured Unit

2. Generate an accountable, reliable and statistically accurate system of measuring actual average area-yields in the defined Insurance Unit Use Crop Cutting Experiments (CCEs)

Basis risk arises mainly due to: • Localized perils (e.g. hail, or flooding by a nearby river),

that do not impact on the area-level average yield and therefore are not covered by AYII

• Non homogeneous crop production and yields within the same area

Area-yield in IU

Farmer’s yield

1. Background

Population Area (sq metres) Block 186,090 177

Gram Panchayat (GP)

11,326

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1. Background

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In 2010, the GOI lowered the level of the Insured Units in order to reduce basis risk

which raised implementation issues

• This shift raised significant implementation issues:

Costs: 10 million CCEs/ year required Quality of CCEs: CCEs require highly trained staff

Example of average size and population of Block and GP in State of Bihar

Question from GOI to the World Bank: Can remote-sensing technology be used to improve the quality and efficiency of the agriculture insurance program?

Table of contents 9

1. Background


3. Study results


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Our analysis tested and compared three estimation strategies

CCEs

CCEs

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Yield estimation based purely on Crop Cutting Experiments (CCEs)

Yield estimation based purely on remote sensing (RS) technology

Yield estimation based on a combination of CCEs and RS technology: “Strategic Sampling” Use of RS “Behind the Scenes”

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2. Study methodology 11

Yield data was collected through 510 crop cutting experiments (CCEs)

• Rice yield was estimated from 510 crop cutting experiments (CCEs) conducted within two districts in the State of Bihar during October-November 2012*

• Within each Gram Panchayat (GP), 10 CCEs were conducted:

* Yield was measured by the consulting group Skymet

Key District and GP Level Statistics were calculated • The average district ground measured yield (kg/m2) is 0.49 kg/m2 and varies between 0.32 and 0.77 kg/m2 • The standard deviation of yield in the entire data set is 0.151 kg/m2 (n=510) • The variance within a GP is lower than the variance across GPs

The standard deviation of the GP means (n =51) is 0.10 kg/m2. The standard deviation of the yield standard deviation within a GP is 0.03 kg/m2

• Yield is spatially auto-correlated sill of 0.025, a nugget of 0.01, and a range of 9 km

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Yield statistics obtained from data collection were used to produce 200 simulations of yield and remote sensing maps

GP Level Statistics calculated from Bihar yield data

Simulated RS data Simulated yield data

Simulated 200 “Perfect information maps” reflecting true yields at GP level

Simulated 200 “NDVI maps” with varying level of RS quality

• Assumed a standard linear relationship between the simulated yield and the simulated NDVI observations and added a varying level of noise to this relationship in order change the R2 from 0.1 to 0.7 at intervals of 0.1.


Simulated district with 25 GPs and 10 CCEs per GP Simulated district with 25 GPs and 2500 RS pixels per GP

200 maps 200 maps with 7 levels of RS

quality

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For each simulated NDVI map, 3 Estimation strategies were tested

Standard CCEs

NDVI only

RS data is not used: The standard sampling scenario assumes that each IU is randomly sampled

*Good: IU Yield estimate from satellite> 100% average yield - Bad: IU Yield estimate from satellite between 70% and 100% average yield - Ugly: IU Yield estimate from satellite below 70% of average yield

Only RS data is used: Average estimated yield per IU relies only on the remote sensing data.


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Simulated NDVI map

8 CCE sampling densities tested

Good, bad, ugly RS data is used to target CCEs : IUs split into three categories based on yield estimated from satellite data: - Good* weight factor = 2 - Bad* weight factor = 4 - Ugly*weight factor = 8

3 8 CCE sampling densities tested

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Difference between claim payments based on true average yields against claim

payments under each estimation strategy was calculated

GP Level Statistics calculated from Bihar yield data

Simulated RS data Simulated yield data

Simulated 200 “Perfect information maps” reflecting true yields at GP level

Simulated 200 “NDVI maps” with varying level of RS quality


200 maps 200 maps with 7 levels of RS quality

3 estimation strategies tested

Claim Payment Rate for “Perfect information”

Claim Payment Rate for each estimation strategy

Measured the difference between claim payments under ‘perfect information’ against the claim payments under each estimation strategy

RMSE = Root mean squared error

CALCULATE Threshold yield is assumed to be 80% of average yield, or 0.39 kg/m2

8 CCE sampling densities tested

Table of contents 15

1. Background


3. Study results


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At 70% yield prediction accuracy, use of remote sensing technology to target CCEs

could quarter cost or halve basis risk

3. Study results – Result #1

Pure remote sensing index

Sampling Strategy : Good, Bad, Ugly

3. Study results - Result #2

Strategic sampling substantially outperformed pure remote sensing estimations, even with higher quality remote sensing

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Study findings show clear advantages of “strategic sampling” through RS technology

• By using satellite data to target CCEs, costs of implementation of CCEs can be reduced by a factor of 4 or payout accuracies increased by a factor of 2 These improvements depend on the quality of

remote sensing data

CCEs >

CCEs +

CCEs >

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1. Using RS technology to target CCEs helps reduce cost and increase accuracy of CCEs

• Basis risk is always lower with strategic sampling than with pure RS : Regardless of RS data quality Regardless of number of CCEs conducted

2. Using RS technology to target CCEs provides more accurate results than using only RS to substitute CCEs

3. Study results

Table of contents 19

1. Background


3. Study results


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Following the WB study, Strategic Sampling was endorsed by GOI

“Efforts may be made to rationalize the number of CCEs to be conducted. This will reduce cost and lead to improved quality and timeliness*»

*Report of the Committee to review the implementation of crop insurance schemes in India, May 2014


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While this study provides robust results on the advantages of strategic sampling,

several questions are still open

• This study provides robust results on the advantages of strategic sampling: Costs: Costs of implementation of CCEs can be reduced by a factor of 4 Quality: Payout accuracies increased by a factor of 2 Time: the acquisition of RS observations and the processing of all

relevant data can happen in near real time • This is a statistical analysis, not a cost-benefit analysis

Investments in quality RS might be more expensive than CCEs

• Strategic sampling might work better in countries with large agro-ecologically homogeneous areas

• Results might be different in other countries, however process can be replicated


APPENDIXES

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Area yield index insurance offers a advantages but is subject to basis risk, which

occurs when farmers incur production losses but do not receive payouts

1. Background

strategic sampling with remote sensing

Technology