Using Remote Sensing Technologies to ImproveSampling

Opportunities in the new Pradhan Mantri Fasal Bima Yojana (PMFBY)Agriculture Insurance Program

Workshop at IFPRI, New Delhi, 21st December 2016Mangesh Niranjan Patankar

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Agenda

• Where are we? CCEs in the current context of PMFBY

• How can we improve?

• Optimization, experiences from pilot

• Way forward

Where are we?CCEs in the current context of PMFBY

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CCEs in the context ofPMFBY

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Standing Crop (Sowing toHarvesting) - Area YieldIndex Based Coverage

Prevented Sowing

Mid Season Calamity

Post Harvest Coverage

Localized Calamity

Claim given in case of ‘failed’ sowing of majorcrops due to inclement weather condition. Covertriggers if more than 75% area is affected.

Provision for ‘on account’ settlement of claim incase of a wide spread calamity affecting the crops.Such payment would occur only if 'estimated' lossis more than 50%, with actual claim amountcapped at 25% of 'potential claim'

‘Indemnity style’ claim assessment. Farmer needsto file for the losses happening due to localizedperils like hailstorm. Coverage reflects expensesoccurred till the event

Forms core ‘index style’ component of the product.‘Crop Cutting Experiments’ to be carried out at the‘notified area’ to determine the ‘representativesample yield’ based on which yield index for thegiven territory is decided. Such yield is thenmatched with the ‘threshold yield’, which is70%/80%/90% of the average historical yield(excluding maximum 2 calamity years). Yieldshortfall is paid as claim at a pre-determined rate.

‘Indemnity style’ claim assessment. Covers postharvest losses suffered from crops which areallowed to dry in cut and spread condition on thefarms, within maximum 2 weeks of harvesting.Perils included – cyclone, cyclonic rains andunseasonal rainfall.

Existing Mechanism

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Random selection offield (well before theharvesting begins)

Locating and markingexperimental plot in

the specific field

Harvesting the CCEplot

Threshing andharvesting of crop

Weighing, Drying andWeighing dry weights

(depending on thecrop)

Publishing plot data,extrapolating yield

index based onaverage yields fornumber of plots

Granularity

Cos

ts, R

esou

rces

Not muchemphasison agro-climaticseggregation

Existing Mechanism

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Party Function

Local Revenue DepartmentOfficer (Patwari)

Plot Selection, Conducting CCEs,Reporting in a specific format (Form – 2)

Agriculture Department User of the data for insurance and otherpurposes

Monitoring Agency Deployedby Insurer

Coordination with Patwari, Monitoringactual CCEs based on targets

Insurer, Reinsurer Ultimate user of the data for calculationof claims/disbursement of claims

State Level CoordinationCommittee on Crop Insurance

First level of contact for any clarificationson notification, claim etc.

Department of Agriculture andCooperation and Farmers’Welfare (DAC & FW)

Ultimate dispute resolution authority.Decision binding on state, insurer, bankand farmers

PMFBY: Where we are… Emphasis on Use of Technology

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• Use of drones for reliability, accuracy, speed ofconducting CCEs

• Online transmission of data to avoid delays inclaims assessments and payouts

• Use of mobile for real time transmission of CCEdata with GPS, date & time stamping

• Use of these technologies to settle claims basedon satellite images/derived products once thecorrelations are established

• Cost of technology including purchase ofhardware devices like smart phones will beequally borne by state & central Govt., at 50:50basis.

• Technical Advisory Committee to assist stategovernment and insurers on technologyintervention

• NITI Aayog deliberating on the CCE optimizationmechanism

Central Crop Insurance Portal

http://agri-insurance.gov.in/Login.aspx

How can we improve?

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• Minimize the number of experiments. E.g. even for a circle level assessment, state of Maharashtraneeds about 55,000 crop cuttings.

• Minimize time lags in publishing the crop yield statistics, generated for the cropping period

• Predict sowing failure and associated economical loss

• Determine accurate crop yield statistics at a granular level (micro level)

• Minimize on-site loss survey expenses and delays in case of localized calamities like hailstorm

• Detect early warnings from the field and implement an action plan accordingly for irrigation, agri-credit and other agri-inputs

• Minimize insurance claim disputes by the use of objectively verifiable techniques

• Equip the stakeholders with necessary technological know-how (especially the governmentmachinery, insurers and agricultural research agencies)

Key Requirements

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Geographical Scope Limited to the State of Maharashtra 14 districts for production of the yield statistics All the districts for production of the weather statistics (overall

40,000 villages)

Temporal ScopeKharif 2012 to Rabi 2015

Project PartnersSwiss Re, Agriculture Insurance Company of India, Niruthi Climateand Ecosystems, CRIDA, Government of Maharashtra

Special AssistanceRecognizing the long term utility of the concept, TOPS projectreceived an assistance from Government of Maharashtra, under itsPPP-IAD programme. Rest of the support was received from Swiss Reand AICI.

Initial Deliverables (set in 2012)1. Weather statistics – Produce historical (1982 to 2011) as well

as current (2012 to 2015) weather data statistics at village level(for 40000 villages) for the whole state

2. Yield statistics – Prepare historical (2001 to 2011) as well ascurrent (2012 to 2015) yield statistics for selected regions atvillage level for specific crops (jowar, bajra, cotton, soybean andgram)

As a bi-product, it was observed that the state government alsobenefited from the project as the project demonstrated thatconsiderable resource optimization is possible using the smart CCEsampling methodologies.

TOPS Project in theState of Maharashtra

• TOPS (Terrestrial Observation and Prediction System) integrates extensive librariesfor data retrieval and preprocessing, algorithms for processing satellite data,interpolation of meteorological data, application programming interfaces to facilitateintegration of multiple models, and numerous databases to archive and manage themetadata associated with model inputs and model outputs.

• Satellite imagery is extensively used in the project to identify the crop and cropconditions.

• The project also heavily uses handheld mobiles and a mobile app named CropSnapfor capturing GPS tagged images, crop booking, crop stage assessment and cropyield estimation.

• The project uses cloud technology for the storage of data where the images areinterpreted through machine learning algorithms (artificial intelligence). The entireacquisition and processing process is automated taking into account all thecommunication issues in rural India.

• A dedicated portal was created to indicate the locations of CCEs, yield statistics andcontact information of the farmers. Software for dynamic sampling scheme was alsocreated.

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Further details - TOPS

• Create an intelligent samplingscheme using TOPS technology todetermine the minimum number ofCCEs to be conducted

• Cluster villages in each circle intolow, medium and high yieldcategories

• Conduct virtual CCEs with CropSnapin each village

• Conduct CCEs manually at chosenlocations based on intelligentsampling and virtual CCEs

• Assimilate crop yield data from theCCEs into the satellite-based yieldmaps to produce a final yield surfacefor each village

• Collate crop yields for each villageand submit the data to Maharashtragovernment

Methodology

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ManualCCE’s

CameraCCE’s

Panoramic

Close-up

Granular

CR

OP

SNA

P Phototo

Yield

Yield

MA

CH

INE

LEA

RN

ING

&A

NA

LYTI

CS

Potential Yield

Cal

ibra

te

High Resolution Final Yield (0.5 ac pixels)

Refinement

High Res Satellite DataHigh Res Weather Data

Crowdsourcing

Send Spreadsheets

HistoricalYield Models

Village-level yield monitoring using satellite, weatherand mobile data

CROPSNAP

Steps in crop yield estimation

A unique examplewhere severaltechnologies(Satellites/Sensorsand DataManagement) put touse for tackling acomplex challenge!

• Conducted nearly 6000 CCEs spread across 14 districts during Kharif and Rabiseasons of 2014 in selected crops. Further, in 2015, around 6000 CCEs wereconducted in 108 villages together in Kharif and Rabi. Data for gram, jowar,bajra and soybean was found to be within 8%, 13%, 15% and 22% of the yieldsassessed by independent methods.

• Intelligent sampling performed better during Rabi season when compared toKharif. Lowest savings were seen in Soybean and highest in Jowar.

Specific Recent Findings

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• Use of image based crop recognition can be operationalized once thenecessary models are in place – but it takes time!

• Accuracy can be an issue in the initial years, given that the initial data fed tothe models needs to be from the historical datasets available throughmanual conventional crop cuttings – which may not be the best source forsuch data

• Need an end to end system/portal, which takes care of

– smarter sampling,

– rapid yield assessment using manual as well as image based inputs and

– digital reporting of the yield

• Smooth adaptation by government is important to ensure that the effortsare not limited to pilots

Way Forward…

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Example of potential expansion of village-level yield data to individual fields within the village. This process involves processing high-resolution satellite data at sub-meter resolution to identify and tag individual fields

Future outlook: Farm-level crop yield estimation

Thank you…

Contact Details:Mangesh Niranjan Patankar

Client Service Manager Agriculture, Assistant Vice PresidentProperty & Specialty Underwriting

Swiss Re Services India Private Ltd., Mumbai, IndiaDirect: +91 22 6661 2153 Mobile: +91 77108 91100 E-mail: Mangesh_Patankar@swissre.com

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