Editorial BoardSmt. Pournima Gupte, Member (Actuary), IRDAIShri P.J. Joseph, Member(Non-Life), IRDAIShri Nilesh Sathe, Member (Life), IRDAIDr. T. Narasimha Rao, Managing Director IIRMShri Sushobhan Sarker, Director, National Insurance AcademyShri P. Venugopal, Secretary General, Insurance Institute of IndiaShri V. Manickam, Secretary General, Life Insurance CouncilShri R. Chandrasekaran, Secretary General, General Insurance CouncilDr. Nupur Pavan Bang, Associate Director Indian School of Business

EditorK.G.P.L. Rama Devi

Published by Dr. Subhash C Khuntiaof behalf of Insurance Regulatory andDevelopment Authority of India

Printed at: NavaTelangana Printers Pvt. Ltd.,# 21/1, M.H. Bhavan, Near RTC Kalyanamandapam,Azamabad Industrial Area, Musheerabad, HyderabadPh: 91-40 27673787, 27665420

2010 Insurance Regulatory and DevelopmentAuthority of India

Please reproduce with due permission

The information and views published in thisjournal are those of the authors of the articles.

Disclaimer: Due to administrative reasons, IRDAI could not roll out the previous edition of the Quarterly Journal.

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The central theme of the current issue ofIRDAI quarterly Journal is Crop Insurance.

The articles on crop insurance have been sourcedfrom those having deep knowledge and expertisein the field. We believe that they will be founduseful by not only those who underwrite the cropinsurance business but also by the generalreaders.Weather variation and the associateduncertainty of crop yields has been a globalphenomenon. Agricultural activity and theincomes therefrom are often influenced byvagaries of nature like droughts, floods, stormsetc. These events being beyond the control of thefarmers, often result in heavy losses in cropproduction and the farm incomes. The magnitudeof loss is also increasing due to the growingcommercialization of agriculture. As on today,agriculture engages about half of the totalworkforce in the Indian economy and contributesabout 17 % to the Gross Domestic Product (GDP).In such a scenario, the need and the importanceof crop insurance cannot be over emphasized.Crop Insurance is a necessity for a majority offarmers but is faced by problems of design andfinance. Moral hazard and adverse selection aremore pronounced in Crop Insurance as comparedto other lines of insurance business.Several Crop Insurance Schemes have beendesigned and rolled out by the Government ofIndia from time to time starting from theComprehensive Crop Insurance Scheme (CCIS)of 1985 to the Pradhan Mantri Fasal Bima Yojana(PMFBY) of 2016. The approach towards theseschemes has been one of continuousimprovement based on the recommendations ofvarious committees appointed to study theshortcomings and the loopholes of these schemes.The efforts have resulted in a coverage of 30% ofthe gross cropped area during the year 2016-17.However, we still have along way to go to increasethe coverage of crop insurance and also thenumber of farmers insured. Improving theconfidence of the farmers in the various crop

insurance schemes and on the very concept ofcrop insurance is paramount to achieve this.This can be achieved only through a concertedeffort by all the stake holders of the cropinsurance sector. Quick settlement of claims isa very important element in encouraging thespread of crop insurance. Assessment of claimsat the right time and timely disbursement ofclaims will not only help the farmers and theirfamilies to overcome the challenges of economicdistress but encourages other uninsured farmersto opt for crop insurance. IRDAI, as theregulator, would constantly endeavor to providea supportive regulatory environment for thedevelopment of this sector. Boosting the CropInsurance would not only develop theagricultural sector but also the generalinsurance sector.I am pleased that the articles published in thisissue have covered various aspects of CropInsurance in India, discussing the problems andprospects associated with the sector. This wouldencourage further discussion on the issue andwill provide inputs and potential solutions to thevarious problems and issues being facedcurrently. The next issue of the journal wouldbe on the theme of “Reinsurance”

Publisher Page

Dr. Subhash C Khuntia

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Dr. Subhash C Khuntia

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InsideInside

Towards improving crop yieldestimation in the insuranceunits of Pradhan Mantri FasalBima Yojana - Dr. C.S. Murthy

Crop Insurance & TechnologyIntervention,(Odisha-

experience) - Dr.Rajesh Das

Agriculture/Crop Insurancein India: Key issues and wayforward - Azad Mishra

Technology InterventionsIn Crop Insurance

- Ashok K Yadav and Nima W Megeji

Agricultural / Crop Insurance In India -Problems And Prospects - Dr. S. Pazhanivelan

A Closer Look at AgricultureInsurance of India - Vivek Lalan,

Pradhan Mantri Fasal BimaYojana(PMFBY) – Issues inhibiting its bigsuccess and probable way forward- M K Poddar,

ISSUE FOCUS

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Given the importance of agriculture in India in the historical, economic andcultural context, the need for transferring the risks of farming throughinsurance,needs no emphasis. The current edition tries to bring out the varied facetsof the Crop Insurance,as a long term risk management tool, and also discussesissues and the challenges associated therewith.

“The biggest challenge in the crop insurance value chain is assessing cropyield in the insurance units for determining the indemnity payout. Therefore, theeffectiveness and sustenance of the insurance scheme largely depends on the yield-loss assessment’’, argues Mr.C.S. Murthy’s team in their article ‘Towards improvingcrop yield estimation in the insurance of PMFBY’. The article also stresses uponthe need for enhancement of transparency quotient in the Crop Cutting Experiment(CCE) processes, ensuring that crop yield estimates are done in an objective manner,minimizing the human induced biases, through use of satellite, mobile and GIStechnologies. It also proposes to finally replace CCE in the long run with an alternative mechanism.

Terming Indian agriculture as a “gamble in the monsoon”, Dr. Rajesh Das has analyzed the cropinsurance experience of the State of Odisha in his article ‘Crop Insurance & Technology intervention.Odisha is one of those states having considerable exposure to drought and floods. After understandingthe benefits of crop insurance and taking into consideration the importance of Crop CuttingExperiments(CCE) in settlement of claims, the State pioneered in the usage of technology by streamliningthe CCE process across the State through digitalization of data using the mobile applications. The articleillustrates how the coordinated efforts of district and state level officials along with other key stakeholdersin monitoring the implementation and progress were the key to the success of the scheme.

Delay in issuance of notification, lack of awareness about the benefits of insurance, enrolmentprocess, non-existence of land ownership title documents for the tenant/share croppers; lack of adequateman-power for conducting large number of crop cutting experiments have been identified as some of theissues in the spreading of crop insurance by Mr. Azad Mishra in his article ‘Agriculture/Crop Insurancein India: Key issues and way forward’. The recommendations include involvement of all stakeholdersfor spreading awareness, as well as utilization of technology such as Digital India Land RecordModernization Programme (DILMRP), utilization of remote sensing and drone based technology forsmart sampling for timely settlement of claims.

In his article ‘A closer look at Agriculture Insurance of India’, Mr. VivekLalan touches upon thevarious obstacles hindering the smooth functioning of the crop insurance schemes in India. He stressedon the need to conduct large scale insurance awareness campaigns at the grass root level, to expand itsoutreach by linking of Aadhar number enabling Direct Benefit Transfers and use of technology for fastersettlement of claims etc.

Utilization of sophisticated technology including Satellite Imagery and Remote sensing basedinformation for assessment of crop yields/ losses is discussed in the article ‘Technology interventions incrop insurance’ by Mr. Ashok K Yadav.

Mr. M K Poddar, in his article presents the various operational issues plaguing the Pradhan MantriFasal Bima Yojana (PMFBY), from achieving landslide success. Some of the issues identified are skeweddistribution of the risk, perceiving the payment of subsidy as financial burden bysome States, poor qualityof CCE data etc. He also suggests a few measures that could make the Scheme sustainable and arguesthat like in many developed and developing countries, a comprehensive legislation on AgriculturalInsurance should be put in place.

‘Remote Sensing Applications in Crop Insurance being a success story from Tamil Nadu usingTamil Nadu Agricultural University-Remote sensing-based Information and Insurance for Crops inEmerging economies (TNAU-RIICE) technology’ was presented by Dr.S. Pazhanivelan. The article alsoshows how remote sensing could be used to assess the impact of floods and droughts on crop conditionsalong with yield loss assessment.

The amount of insured losses from each major natural catastrophe – be it floods or localizedcalamities have been rising progressively. Reinsurance is an extension of the basic, fundamental conceptof pooling and is an integralpart of the entire insurance business cycle. The focus of the next issue will beon ‘Reinsurance’.

-K.G.P.L. Rama Devi

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BEWARE!! IRDAI does not sell Insurance

The public are hereby cautioned regarding the following:

Some of you must be receiving phone calls from persons claiming to be employees

of Insurance Regulatory and Development Authority of India (IRDAI) and trying

to sell insurance policies or offering some ‘benefits’.

Please note that IRDAI does not sell or promote any company’sinsurance product or offer any ‘benefit’.

IRDAI regulates the activities of insurance companies to protect the interests of

the general public and insurance policyholders.

Report to the nearest police station and file FIR if:

Any person approaches you claiming to be IRDAI employee for sale of insurance

products or offering any ‘benefit’,

Any unlicensed intermediaries or unregistered insurers try to sell insurance

products.

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Towards improving crop yield estimationin the insurance units of Pradhan Mantri

Fasal Bima Yojana

1. Introduction

India has a long history in thedesign, development andimplementation of variouscrop insurance schemes withsuccessive improvementsfrom time to time. The idea isto insulate the farmingcommunity against variouscultivation risks. Governmentof India introduced traditionalcrop insurance in the year1972 on a limited scale,followed by national levellarge scale introductionof the Comprehensive CropInsurance Scheme in 1985,National Agricultural CropInsurance Scheme in 1999,Pilot Weather Based CropInsurance Schemes in 2007and Pilot Modified NAIS in2010. However, implementa-tion of PMFBY from kharif2016, is a revolutionary steptowards improving agri-culture insurance system inthe country. PMFBY,primarily an area-yieldinsurance contract, hasmany positive features tocompensate for multiple risksduring the entire life cycle of

the crop season. Use oftechnologies viz. remotesensing, mobile and dataanalytics is being increasinglyattempted for effectiveimplementation of thescheme in the last two years.

National Remote SensingCentre (ISRO) has takenseveral initiatives in recentyears to demonstrate thetechnology capabilities tomeet the informationrequirements of cropinsurance. These initiativesinclude (a) pilot studies indifferent districts, (b)

development and implement-ation of Mobile technology forfield data collection forimproving crop yieldestimation and crop lossassessment, (c) training tothe field level personnel ofState Departments on mobilebased field data collection, (d)collaborative studies withAgricultural InsuranceCompany of India Limited(AICIL) to improve cropinsurance with remotesensing and GIS technologies,(e) awareness-cum-trainingto the industry on technologyutilisation, (f) development ofa Decision Support System forcrop insurance for Odishastate and (g) conductingspecial studies to support theStates.

The biggest challenge in thecrop insurance value chain isassessing crop yield in theinsurance units fordetermining the indemnitypayout. The effectiveness andsustenance of area-yieldinsurance scheme is thereforelargely dependent onthe objective yield-loss

Issue Focusw

India has a longhistory in the design,

development andimplementation of

various cropinsurance schemes

with successiveimprovements from

time to time. The ideais to insulate the

farming communityagainst various

cultivation risks.

Dr. C.S. Murthy is Head, AgriculturalSciences and Applications, Remote SensingApplications Area, National Remote SensingCentre (NRSC-ISRO), Hyderabad.

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assessment mechanism usingreliable, current and historicalcrop yield data, which poseda serious challenge

In India, crop yield estimationin the insurance units is doneby conducting Crop CuttingExperiments (CCE) in thefield-plots selected through asampling scheme. Subjectivityin the yield measurementshas become a major concernand it is widely agreed that thequality of crop yield dataneeds to be improveddrastically to enhance thestrength of the crop insurancecontracts for their sustenance.Technology interventionssuch as use of satellite datato improve crop yieldestimation is largely re-commended and henceattempts are being made toadopt the same since the startof PMFBY in kharif 2016.This paper examines variousloopholes in the currentmechanism of yield esti-mation through CCE andsuggests the strategiesfor enhancing technologyutilisation to address bothhuman induced andmethodological shortcomingsto improve the yield data.

Implications of inaccurate andbiased yield data on theinsurance mechanism are firstdiscussed followed bydifferent strategies forimproving the yieldassessment.

2. Implications of biasedyield data

Bias in the crop yield data ofdifferent insurance units is on

the lower side for most of thetime, as observed fromvarious reports, news itemsand views of differentstake holders. Such under-estimation of crop yieldsin the insurance units, hascascading effect on the entiresystem of insurance. Reducedyields attract higher payouts,reflecting higher risk andhigher cost of insurance(premium rate) in subsequentyears. Another impact of thebiased data is that it reducesthe threshold / guaranteedyield of the crop for aninsurance unit which is basedon the average of preceeding5-7 years yield in theinsurance unit.

Therefore, the probability ofexperiencing less than thethreshold yield (which isalready on lower side due topast series of biased data)gets minimised gradually overa period of time. As a result,the insured farmerswould be either uninde-minified or partiallyindemnified despite facingcrop losses. Consequently, thecrop insurance contract willbecome a financial riskenhancing instrumentrather than risk reducinginstrument, as the farmersmay endup paying thepremiums without getting thecompensation for crop loss inreturn. Thus, biased yieldestimation in the insuranceunits leads to disastrous andcascading effect on the cropinsurance mechanism in theshort run as well as in the longrun.

3. Strategies forimproving crop yieldestimation

Three broad strategies forimproving the crop yieldestimation in the insuranceunits include; (1) measures toenhance transparency andobjectivity in the CCE process,(2) implement smartsampling on the basis of yieldproxies to improve thesampling design in terms ofreduced sample size andlogical distribution of thesample plots and (3) replacethe CCE with alternatemechanism. The main focus ofthis paper is on the measuresfor immediate implementa-tion and these are mostlyrelated to the first and secondstrategies mentioned above.The third strategy is theoutlook for medium to longterms and not muchemphasised here.

Human induced prejudices/choices and methodological

In India, crop yieldestimation in theinsurance units is doneby conducting CropCutting Experiments(CCE) in the field-plotsselected through asampling scheme.Subjectivity in the yieldmeasurements hasbecome a major concernand it is widely agreedthat the quality of cropyield data needs to beimproved drastically toenhance the strength ofthe crop insurancecontracts for theirsustenance. w

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limitations together impactthe quality of yield data in thecurrent system of CCE. Byinfusing technologies such asremote sensing, mobile, GISand data analytics the effectof these limiting factors can beminimised.

3.1 Selection of CCE plots

Generally speaking, four CCEplots in each insurance unit fora given crop and season areconsidered for yieldmeasurment and averageyield estimation. These fourplots are identified in therandomly selected fields. Theunderlying assumption is thatthe insurance unit ishomogeneous with respect tocrop performance and hencethe average yield of any fourplots represents theinsurance unit’s average. Thevalidity of this assumption isthe key to the success of thisrandomisation process.Unless and otherwise, it isestablsished with real data, itremains as a theoreticalassumption which may notmatch with ground situation.

In an insurance unit, the crop

area may be distributedunder irrigated conditions,rainfed conditions, semi-dryconditions, fertile areas, lessfertile areas etc. Further, inthe event of risk occurrence,part of the insurance unit onlymay be affected. Thus,spatial variability of cropperformance and spatialvariability in the occurrence ofdifferent risks – floods,drought, pest, diseases etc,within the insurance unitwould seriously distract thehomogeneity assumption.Therefore, random selectionof four CCE plots wouldtend to result in skewedrepresentation of fieldconditions leading to biasedestimate of the average yield.

Currently, random number offields for locating CCE plotsare being identified in thebeginning of the crop season.It means, the crop risks thatmay occur during the courseof crop season are not dulyrecognised and to this extentthe sampling is non-representative of the groundlevel situation. Therefore,selection of CCE plots shouldbe guided by yield affecting/indicating factors to ensureoptimal spread of these plots.

In order to overcome theabove stated sampling issuesand towards improving thedistribution of CCE plots, thescope for using moderateresolution satellite data hasbeen investigated in detail forwheat crop in Ujjain district.Sentinel data of 10m spatialreolution and 5-6 days repeathas been procured foranalysis. There are 21number of satellite imagescovering complete phenology

of wheat crop.

Crop mapping was done usingmultitemporal data anddecision rules approach. Onthe basis of sowing time, threetypes of wheat namely – earlysown, normal sown and latesown could be delineatedusing satellite data. Earlywheat and late wheatproduces less yield comparedto normal class as observedfrom the field data andinteractions with farmers.Early wheat completesflowering before the close ofwinter, where as late wheatcrop commences flowering inthe high temperature period.These could be the reasons foryield reduction in these twoclasses. The number ofirrigations ranges from 2-6based on water availability.

Insurance unit level wheatyield variability and itsassociation with satelliteindices are also analysed. Forthis purpose CCE wereconducted in some of thevillages based on the samplingscheme using satellite data. Itis observed that wheat yieldvariability within theinsurance units is higher andhence estimating averageyield using four CCE plots ineach village (insurance unit)may not produce therepresentative yield for theunit.

Satellite derived wheat NDVIprofiles, wheat crop map andNDVI based crop conditionzones are shown in Figs. 1-3.Index derived from temporalNDVI i.e., Season’s Max.NDVI has shown highcorrelation with wheat yieldas shown in Table1.

w

Human inducedprejudices/ choices andm e t h o d o l o g i c a llimitations togetherimpact the quality ofyield data in the currentsystem of CCE. Byinfusing technologiessuch as remote sensing,mobile, GIS and dataanalytics the effect ofthese limiting factorscan be minimised.

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Fig.2 Satellite derived wheat crop mapvillage, Ujjain district, Rabi 2017-18

Table 1: Correlation coefficient between Wheat yield and Season Maximum NDVI at Insurance units of Ujjain district (2017-18)

S. No. Halka Correlation between

(Insurance Unit) wheat yield and NDVI1 Ajawada 0.782 Bichrod Istamurar 0.713 Mungawada 0.834 Ninora 0.815 Rudaheda 0.826 JawasiyaSolanki 0.887 Jhalara 0.79

Fig.3. Wheat crop condition variability withinthe village, Ujjain district, Rabi 2017-18

Fig.1 NDVI profiles of CCE plots using 21 Sentinel-2 temporal Scenes

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3.2 Notification of fieldsfor CCE plots

The survey numbers of thefields selected for CCE arecommunicated to the fieldfunctionaries in the first oneor two months of the cropseason. This informationeventually reaches thefarmers of the village andcreates opportunities formoral hazard activities in theCCE fields. There are someincidents in recent yearswhere there was deliberatemismanagement of crop in thefields notified for CCE.This is typically a governancerelated issue and can beaddressed through manage-ment interventions.

3.3 Locating CCE fieldson the ground

The CCE plots are identifiedwith the help of surveynumbers of the correspondingfields. The location of thesefields is not represented in anydigital map base or bycoordinates. As a result, thereis scope for replacing theactual CCE field with nearbyor a convenient field in thesame village. Identification ofrandom numbers and fieldplots should go completely indigital mode with mapoutputs using the digitalcadastral layer of the village.Latitude and longitude detailsof the selected fields alongwith survey numbers are tobe advised to the fieldpersonnel. Mobile App maybe modified in such a way

that, only when the fieldperson reaches close to theselected survey number,within the predefined bufferzone of about 10m radius, thedata fields of the app areactivated enabling the dataentry. Thus, by using mapbase and by modifying themobile app, the identificationof CCE plots on the groundbecomes foolproof.

3.4 Recording withMobile App

Mobile Applications are beingused extensively by many ofthe states for recording CCEyield data, from 2017 kharifseason.Thus the intention toestablish transperancy in theprocess of CCE data collectionis made clear.

The element of concern in thisprocess is the location errorsof CCE plots measuredthrough GPS system of theMobile. Location errors of theCCE plots are ranging from 10mt. to 1000 mt. When the

CCE data is linked to map baseand satellite data for GISanalysis, it is evident thatmany of the CCE plots arewrongly located in non-agriculture areas, neigh-bouring villages etc. In somecases, there are more than 10CCE located in a GramPanchayat, as a result ofwrongly recorded latitudeand longitude. This is typicallya problem of data collection.The field level person usingthe Mobile app, has to wait fora few minutes, for getting thebest lattitude/longitude byusing the signals of morenumber of GPS satellites.Therefore, till the locationerror becomes less than 10-15m, the App should notenable the data fields forinputing the information. Ifthe mobile app based CCEdata is within the acceptablelocation error limits, suchdata is useful for furtheranalysis such as linking withsatellite data, weather data forthe purpose of analytics andvalue addition.

3.5 Post CCE verificationof yield data

Insurance unit average yieldscomputed from the CCE dataare quite often disputed bystake holders. In many casesthese data sets are suspectedto be biased thereby causingabnormal delay in the decisionmaking on claims settlement.Technologies play animportant role in theverification of yield data.Satellite derived crop

w

The CCE plots areidentified with the helpof survey numbers of thecorresponding fields.The location of thesefields is not representedin any digital map baseor by coordinates. As aresult, there is scope forreplacing the actual CCEfield with nearby or aconvenient field in thesame village.

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condition indices are availablein 10-60 metres spatialresolution once in five days.These indices are useful todetect the crop conditionanomalies in insurance unitsto corroborate with estimatedyield. By comparing the yielddata and crop condition dataof the current year withprevious normal years, onecan get an ideawhether the crop yieldreduction in thecurrent year, ifreported, is justifiedor not. For example,the insurance unit(Gram Panchayat)level average yields ofpaddy and season’smaximum NDVI ofAWiFS sensor for allthe units are plotted inFig. 4. Season’s maxNDVI of paddy is associatedwith paddy yield showingpositive correlation. Thismaximum NDVI correspondsto heading/flowering phase ofpaddy crop. This associationbetween NDVI and yield ofpaddy may be exploited tocorrect the wrongly reportedCCE data, when there are noabnormal weather conditionsor pest resurgence in the postheading phase of crop.

Similarly, weather data sets ofdifferent years can becompared. Using multipleparameters – satellitederived NDVI, NDWI/LSWI,rainfall, rainy days, dry spellsetc, decision rules can bedeveloped to infer whetherthe reported yield reduction

is justified or not. There isscope for developing semi-automated procedures forquick verification of yielddata. Localised risks and therisks that happen just beforeharvest may go un-noticed insuch verification process,which needs to besupplemented with groundtruth information.

3.6 Digital dataavailability

Adoption of remote sensing,mobile apps and GPS andimplementation of thetechniques of spatial analysisfor improving crop insuranceneeds GIS data base whichconsists of satellite images,mobile app collected CCE dataand field data, weather data

sets, shape files ofadministrative units –villages, Gram Panchayats,insurance units etc. It hasbeen observed that in manycases, all the insurance units(villages) could not beidentified in the availableshape files. A significantnumber of Gram Panchayats(insurance units) in Odisha,

remain unidentified in theshape file. Similarly, about25% of villages could notbe located in the shapefiles of some of the districtsin Maharashtra state.

Therefore, the mostimportant and immediaterequirement for techno-logy application in cropinsurance is availability ofuniform and standardshape files of villages/

blocks/districts. Withoutidentifying all the insuranceunits in map base,undertaking any scientificanalysis in respect of yieldverification, smart samplingetc.is not possible

3.7 Trained man powerfor conducting CCE

The number of CCEs requiredto support PMFBY is veryhuge, accounting to about 35-40 lakhs per year. ConductingCCE in such a large numberneeds logistic support, trainedpersonnel and budgetsupport. To generate qualityyield data, CCEs need to beconducted in a systematicway and hence it requirestrained manpower. The

Fig.4 Paddy yield versusAWiFS NDVI among the

insurance units (GPs),kharif 2016-17, Odisha

state (Data source:Department of

Agriculture and FarmersEmpowerment,

Government of Odisha)

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persons having someknowledge on field datacollection in agriculture aresuitable for this purpose. Lackof trained man power is oneof the most criticalimpediments faced by manystates and insurancecompanies. Coordinationbetween the agenciesinvolved such as departmentof agriculture, revenue andstatistics is an importantrequirement for successfulcompletion of the CCE. StateAgriculture Universities withtheir network of researchstations may be roped in tothe CCE task, to overcome theman power shortage on onehand and to avail theirexpertise for supervision andquality improvement on theother.

3.8 Correction factor forthe biased yield data

Development of a correctionfactor for biased yield data isa real challenge and needs tobe addressed. Some of thestudies have reportedregression approach –between yield and NDVI,between yield and rainfall etcfor correcting the yield data.These empirical methods maynot produce consistent resultsfrom place to place and timeto time. Uncertainty is highand may not be good foroperational use. By forcingthe data through regressiontechniques, another form ofsubjectivity would beintroduced in the yield data.

Another approach reported isgiving weightages to CCEyield, rainfall and NDVI.Arriving at optimal weightsfor different crops andlocations is a challenge. Tosum up, correcting the biasedyield data with empirical orsemi empirical or rule basedprocedures is still anunaddressed problem.Machine learning algorithmsmay be promising fordeveloping such correctionfactors. This is an importantR&D element in cropinsurance and there is a lot ofscope for initiating pilotstudies.

3.9 Smart sampling forreducing the number ofCCE

Smart sampling or intelligentsampling aims at two benefits(a) reducing the number ofCCE plots and (b) improvingthe distribution of CCE plots,without compromising theerror limits of final estimates.

Sampling scheme is applied ataggregated level say districtor taluk level, and theestimates are generated atdisaggregated level. Smartsampling will be efficient if astrong yield proxy isdeveloped preferably at alater part of the crop growingseason capturing the mostsystemic and idiosyncraticrisks that the crop has facedand using the same insampling design. Yield proxyis useful to arrive athomogeneous zones and tolocate the fields for CCE.Considering the limitations ofthe satellite based indices andweather datasets and otherdata, it is desirable to developa blended index as yieldproxy.

Yield estimation in theinsurance units is based onsmart sampling data involvesempirical procedures andhence is prone to errors.Therefore, quantification oferror and assigning errorlimits to the final estimateneeds due diligence. Anotherimportant point of attentionwhile adopting smartsampling is that it is morelikely that in some of theinsurance units there will notbe any CCE plots and henceno yield measurement.Therefore, the average yielddata of insurance units thatresult from smart samplingtechniques is ‘estimate’ andnot ‘measured’.

w

Yield estimation inthe insurance units

based on smartsampling data

involves empiricalprocedures andhence prone to

errors. Therefore,quantifi-cation of

error and assigningerror limits to the

final estimate needdue diligence.

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In the event of implementingthe smart sampling techniqesin the near future i.e., next 1-2 years, the compatibilitybetween the smart samplingderived yield estimates forthe implementing year andthe corresponding yieldderived from the CCE basedmeasured yields of previousyears will pose a problemwhich may be overcome tosome extent by empiricallytransforming the CCE basedyields of historic years byusing the concurrent datasetsof one year.

3.10 Dispensing with CCEsystem

Considering the complexitiesassociated with the currentmechanism of CCE asmentioned in the abovesections, the most preferredchoice is to replace the systemwith alternative mechanismthat is less prone to errors.Development of analternative scientific methodof yield estimate in theinsurance units is the biggestresearch challenge. Generally,crop yield estimation methodsare of three categories –empirical, semi-empirical andsimulation models. Remotesensing derived NDVI whichrepresents crop vigour hasbeen correlated with yield toinvestigate the possibility ofdeveloping crop yield indexfor crop insurance.Considering the limitations ofNDVI, some studies haverecommended the use of bio-

physical variables derivedfrom satellite data to developindex based crop insuranceschemes. Local weatherconditions, crop managementpractices, soil, variety/hybrid, water relatedparameters, etc. areimportant yield determinantsbut their effect is notcompletely manifest in anysingle index. Therefore, semiempirical techniques arebeing developed involvingspectral indices, weather dataand local crop growingconditions. Adopting cropsimulation models call forvery intensive fielddata on different variables,calibrations etc limiting itsscalability.

Technology based innovationsneed to be blended with localcontexts, i.e., local cropgrowing conditions such ascultivation practices, soil,weather elements etc. thatfrequently influence the

agricultural production.Quantifying the frequent andlocalized phenomena thataffect the crop production is amain challenge in the area-yield crop insurance. Machinelearning algorithms may bepromising to develop yieldestimation techniques. Thus,alternative methods for cropyield estimation are still indevelopment phase andhence replacement of CCEwith other mechanism is yetto be realised.

4. Conclusion

Crop yield data is the mostcrucial data for the area-yieldinsurance contracts. Cropyield estimation in insuranceunits continues to be thesubject of greater concernwith ever increasing disputeson the quality of yield data.

Although, technology infusionto improve yield measure-ment has been started, byway of using satellite imagesand mobiles, since the launchof PMFBY in kharif 2016,there are still several factorsplaguing the quality of yielddata. Loopholes or shortcomings in the currentsystem of crop yieldestimation in the insuranceunits and the means toimprove the system throughtechnology interventions in amore strategic way arehighlighted in this paper.These interventions wouldcertainly fix the methodologyrelated factors and also

w

Considering thecomplexities associated

with the currentmechanism of CCE as

mentioned in the abovesections, the most

preferred choice is toreplace the system withalternative mechanism

that is less prone toerrors

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minimise the human inducedbiases in order to make theyield measurements moreobjective. Biased yield dataleads to disastrous andcascading effect on the cropinsurance mechanism in theshort run as well as in the longrun. Technology interven-tions should be undertaken ina big way across the nation toovercome this menace of yielddata quality and to sustainthe crop insurance systemwith wider acceptability..

References

Anonymous, 2014, Report ofthe Committee to review theimplementation of cropinsurance schemes in India,Department of Agricultureand Cooperation, Govern-ment of India, available atwww.agricoop.nic.in.

Ibarra, H., and Skees J.,2007, Innovation in risktransfer for natural hazardsimpacting agriculture,Environmental Hazards 7,62–69

Leblois, A., and Quirion, P.,2013, Agricultural insurancesbased on meteorologicalindices: Realizations, methodsand research challenges.Meteor. Appl., 20: 1–9,doi:10.1002/met.303.

Mishra, P.K. 1996,Agricultural Risk, Insuranceand Income.Arabury,Vermont: Ashgate PublishingCompany.

Smith, V., and M. Watts,2009, Index basedagricultural insurance indeveloping countries:

Feasibility, scalability andsustainability. Rep. to theGates Foundation, 40 pp.[Available online at

http://agecon.ucdavis.edu/research/seminars/files/vsmithindex- insurance.pdf.

Turvey, C.G., and MclaurinM.K., 2012, Applicability ofthe Normalized DifferenceVegetation Index (NDVI) inIndex-Based Crop InsuranceDesign. Weather, Climate andSociety 4, 271-284, DOI:10.1175/WCAS-D-11-00059.

Views expressed in thispaper are author’s

personal only and not ofthe affiliatingorganisations

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Crop Insurance & TechnologyIntervention,(Odisha-experience)

When I entered the College ofAgriculture for a Bachelorcourse in Agriculture in earlyeighties, the Agronomyteacher welcomed us with thesentence “Indian Agricultureis a gamble in the Monsoon”.Even after 35 years, I still feelthat in spite of all our scientificdevelopments in the field ofagriculture, we still aregrappling with variousuncertainties and thesentence has not lost itsrelevance. In those days, thebest way to make agriculturesecure was to bring thecultivated area underirrigation, use more fertilizers,better varieties of seeds andprophylactic sprays tosafeguard against imminentpest attack. Odisha as a statethat had not harnessed muchbenefit out of the first “GreenRevolution” continued withthese strategies.

The climate change has madethe arrival of monsoon,distribution of rain anddeparture uncertain.Although irrigation potentialhas already been created for

50% of cultivated area andGovernment is seriouslyattempting to bring more areaunder irrigation, properavailability of water forirrigation is still a question. Infact water availability inirrigated commands is solelydependent on distributionand quantum of rainfall duringrainy season. Without havinga proper ground waterrecharge plan, the unjudicioususe of ground water mayfurther complicate the matterin future. The indiscriminate

use of fertilizers & pesticidescoupled with climate changemade the emergence of newpests affecting agriculturalproduction.

The long exposure to coastline (about 480 Km) makesOdisha more prone to cycloneand floods. An analysis ofoccurrence of drought andflood in past 50 years revealthat in 42 years, theagricultural production in theState has been affected byeither drought or flood andeven both in the same year(Table-I).In this back drop,the need for providing aprotective cover to farmersthrough “Crop Insurance” hasbecome a pressing necessitythan ever before.

The history of Crop Insurancein Odisha dates back to 1999when the NationalAgricultural InsuranceScheme (NAIS) wasintroduced as a flagshipprogramme. The Statesuccessfully implemented thescheme till Rabi 2015-16. Inthe interim period schemeslike MNAIS, WBCIS, NCIP

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The long exposure tocoast line (about 480Km) makes Odisha moreprone to cyclone andfloods. An analysis ofoccurrence of droughtand flood in past 50 yearsreveal that in 42 years,the agricultural produ-ction in the State hasbeen affected by eitherdrought or flood andeven both in the sameyear

Dr.Rajesh Das

Nodal Officer (PMFBY) Directorate of

Agriculture Government of Odisha

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etc, were implemented onpilot basis.

In 2011 a major interventionin the NAIS scheme wasmade by lowering down theInsurance unit of Paddy toGram Panchayat Level fromBlock level. It is pertinent tomention here that paddy isthe major crop of the stateand accounts for about 95% ofthe insurance. Thus the Statewas able to extend the benefitof the programme to a largechunk of farmers.

It is revealed from the NAISimplementation data that inKharif season about 16-18lakh farmers were coveredunder the programme andsimilarly during Rabi seasonabout 60,000—80,000farmers were covered. Theaverage areas covered forKharif & Rabi season were 13lakh ha and 0.75 lakh harespectively .The Rabicoverage under insurance has

always been minimal. (Table-II).

The turn around to theInsurance programme camein the year 2015-16 (SchemeNAIS) when the estimatedclaim level for Kharif ’15reached about 2000 crore.The state never had that kindof claim payment history.This was an eye opener for allat the administrative leveland a serious relook was givento Crop Cutting Experiment(CCE) process, the main wayof claim assessment.

It was then decidedthat the CCE process shall bedigitized and all pre-selectedCCE points will be geo-tagged.The experiences of capturingCCE data using Mobile Appunder “FASAL” project ofMahalnobis National CropForecast Centre (MNCFC)came in really handy. TheDistrict level officials wereidentified as “MasterTrainers” to train thePrimary Workers regardingcapturing of CCE datathrough mobile App usingSmart Phone. All the primaryworkers were provided witha complete set of CCE kitcomprising of weighingbalance, measuring tape, ironpegs, rope, cloth bag,tarpaulin, cap etc. It ispertinent to mention thathere the conduct of cropcutting is treated as anexperiment and for anexperiment to yield desiredresults, it has to be performed

with properly calibrated tools,precautions and protocols.

While all these arrangementswere being made, the“Pradhan Mantri Fasal BimaYojana (PMFBY)” waslaunched. The mainstay of thescheme is “Use ofTechnology” and this boostedthe State’s initiative tostream line the CCE process.As a first step in this regard,all Primary Workers wereprovided with an incentive ofRs. 2500/- for downloadingthe “CCE Agri-App” andregistering in portal with acondition that they will becapturing and uploading CCE-data for three years.Additional Incentive of Rs.100/- per CCE was providedfor capturing & uploading theCCE data. Training Campswere organized for “PrimaryWorkers” as well as “Districtlevel Approvers”. In fact theconcept of “District Level

The turn around to theInsurance programmecame in the year 2015-16(Scheme NAIS) when theestimated claim level forKharif ’15 reached about2000 crore. The statenever had that kind ofclaim payment history.This was an eye openerfor all at theadministrative level anda serious relook wasgiven to Crop CuttingExperiment (CCE)process, the main way ofclaim assessment.

wIt was then decidedthat the CCE processshall be digitized andall pre-selected CCEpoints will be geo-

tagged. Theexperiences of

capturing CCE datausing Mobile App

under “FASAL” projectof MahalnobisNational Crop

Forecast Centre(MNCFC) came in

really handy.w

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Specific “Mobile App”are being developed forloss assessment in caseof Localized Calamity &

Post Harvest Losses.Efforts are also beingmade to notify more

crops under theprogramme. A major

learning from theprogramme

implementation is thattechnological

intervention is the onlyway for taking the

scheme further.

Approvers” as a check &balance measure in CCE dataapproval process wasintroduced at the behest ofOdisha.

For effective programmeexecution “What’s App”groups were created in eachdistrict through which CCEschedules were shared.Guidelines for multi levelphysical CCE verification wasformulated and districtadministration was instructedto scrupulously monitor theprocess and progress. AnOfficer in the rank of Addl.Dist. Magistrate was declaredas “Nodal Officer” tocoordinate the CCE process.Periodic video-conferencesbetween State and Districtofficials were held to keep atab on the progress of conductof CCE and its approval. A“State level What’s Appgroup” involving all key stake

holders and also DistrictMagistrates was also createdfor sharing of ideas andmonitoring the programme.Two new collaborativeprojects “Crop InsuranceDecision Support System(Technical Partner-NRSC,Hyderabad)” and ScienceBased Crop Insurance(Technical partner-International Rice ResearchInstitute,Manila,Phillipines)”were launched foraugmenting PMFBYimplementation.

Outcomes- This changedthe entire scenario ofprogramme implementation.Odisha became the pioneerstate in the country withregards to use of technologyin Crop Insurance. Thishelped in quicker claimsettlement (by end of June ’17i.e one of the earliest in thecountry) bringing intransparency to the CCE-process-the core area ofcontroversy and instillingconfidence among theempanelled insurancecompanies. As a result, whilethe actuarial premium rateswere going high in otherstates, Odisha got muchbetter rates for Kharif ’17 &Rabi 2017-18. The experienceof four seasons are presentedbelow in Table-3.

The journey, did notend there. The CCE results ofKharif ’16 and Kharif ’17 wereanalyzed by MNCFC and thefindings are being used to plugin the gaps in the system. It

has also been decided to go forsmart sampling techniquesbased on crop phonologicalparameter for selection ofideal plots for conduct of CCEand use of satellite imageries(coupled with groundtrothing) to assess sown areaunder various crops in anInsurance Unit. The state isalso contemplating toimplement a novel concept“Picture Based Insurance” ona pilot basis starting fromKharif ’18.

Besides the techno-logical innovations andinterventions, the StateGovernment has formulateda scheme for systematicpublicity campaign especiallyto bring in more non-loaneefarmers into the ambit of thecrop insurance and capacitybuilding of the State officialsin loss assessment in case ofvarious risk scenarios.Specific “Mobile App” arebeing developed for lossassessment in case ofLocalized Calamity & PostHarvest Losses. Efforts arealso being made to notifymore crops under theprogramme. A major learningfrom the programmeimplementation is thattechnological intervention isthe only way for taking thescheme further.

This way it is expectedthat coordinated effort anduse of technology shall makethe State an example forother states to emulate.

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Sl.No. Year Normal Actual Kharif Rice RemarksRainfall rainfall Productionmms mms (In lakh MTs.)

1 2 3 4 5 6

1. 1961 1502.5 1262.8 36.99

2. 1962 1502.5 1169.9 36.32

3. 1963 1502.5 1467.0 42.47

4. 1964 1502.5 1414.1 43.59

5. 1965 1502.5 997.1 31.89 Severe drought

6. 1966 1502.5 1134.9 35.37 Drought

7. 1967 1502.5 1326.7 34.43 Cyclone & Flood

8. 1968 1502.5 1296.1 38.48 Cyclone & Flood

9. 1969 1502.5 1802.1 38.39 Flood

10 1970 1502.5 1660.2 39.13 Flood

11. 1971 1502.5 1791.5 33.76 Flood, Severe Cyclone

12. 1972 1502.5 1177.1 37.35 Drought, flood

13. 1973 1502.5 1360.1 41.91 Flood

14. 1974 1502.5 951.2 29.67 Flood, severe drought

15. 1975 1502.5 1325.6 42.74 Flood

16. 1976 1502.5 1012.5 29.58 Severe drought

17. 1977 1502.5 1326.9 40.50 Flood

18. 1978 1502.5 1261.3 41.89 Tornados, hail storm

19. 1979 1502.5 950.7 27.34 Severe drought

20. 1980 1502.5 1321.7 40.31 Flood, drought

21. 1981 1502.5 1187.4 36.63 Flood, drought, Tornado

22. 1982 1502.5 1179.9 27.07 High flood, drought, cyclone

23. 1983 1502.5 1374.1 47.63

24. 1984 1502.5 1302.8 38.50 Drought

25. 1985 1502.5 1606.8 48.80 Flood

26. 1986 1502.5 1566.1 44.56

27. 1987 1502.5 1040.8 31.03 Severe drought

28. 1988 1502.5 1270.5 48.96

29. 1989 1502.5 1283.9 58.40

30. 1990 1502.5 1865.8 48.42 Flood

31. 1991 1502.5 1465.7 60.30

32. 1992 1502.5 1344.1 49.76 Flood, drought

33. 1993 1502.5 1421.6 61.02

34. 1994 1502.5 1700.2 58.31

35. 1995 1502.5 1588.0 56.48

36. 1996 1502.5 990.1 38.27 Severe drought

37. 1997 1502.5 1493.0 57.51

TABLE-1

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38. 1998 1502.5 1277.5 48.85 Severe drought

39. 1999 1502.5 1435.7 42.75 Severe Cyclone

40. 2000 1502.5 1035.1 41.72 Drought & Flood

41. 2001 1482.2 1616.2 65.71 Flood

42. 2002 1482.2 1007.8 28.26 Severe drought

43. 2003 1482.2 1663.5 61.99 Flood

44. 2004 1482.2 1273.6 58.84 Moisture stress

45. 2005 1451.2 1519.5 62.49 Moisture stress

46. 2006 1451.2 1682.8 61.96 Moisture stress/Flood

47. 2007 1451.2 1591.5 68.26 Flood

48. 2008 1451.2 1523.6 60.92 Flood , Moisture Stress

49. 2009 1451.2 1362.6 62.93 Flood/ Moisture stress/ Pest

attack.

50. 2010 1451.2 1293.0 60.51 Drought/ Un-seasonal rain

51. 2011 1451.2 1327.8 51.27 Drought & Flood

52. 2012 1451.2 1391.3 86.29 Drought in Balasore, Bhadrak,

Mayurbhanj&Nowapara

districts.

53. 2013 1451.2 1627.0 65.85 Flood& Cyclone in 18 dists

due to Phailin.

54. 2014 1451.2 1457.4 85.78 Flood & Cyclone in 8 dists due

to Hud-Hud

55 2015 1451.2 1144.3 88.37 Late Season Drought

Views expressed in this paperare author’s personal only and

not of the affiliatingorganisations

TABLE-2 TABLE-3

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Crop Insurance acts asfinancial security to farmersby mitigating the risksassociated with agriculture.Crop Insurance providescompensation to the insuredfarmers in the event of croplosses due to various factorssuch as deficit rainfall, excessrainfall, high temperature,low temperature etc. Cropinsurance compensationduring adverse climaticconditions not only covers thefarm losses but alsoencourages investment onfarming for next season.

Under crop insurance, suminsured for the policy isequivalent to scale of financedecided for notified crop innotified district. Farmers haveto pay a premium amountwhich is charged by insurancecompany to insure the crop inspecified location for definedrisks and policy periodsduring the season. But thefrequency and quantum ofcrop losses may be very highand wide spread for somecrops and geographies. Thismay result into high actuarialpremium rates. So farmers

find it difficult to afford cropinsurance. In order to makethe crop insurance affordableto farmers, most of thecountries have developedcrop insurance schemeswherein subsidies areprovided on the premiumamount to be collected fromthe farmers. India also has itsown crop insuranceprogramme which providessubsidy to farmers.

Crop Insurance in Indiaformally started way back in1972 and has taken different

forms and shapes in recentyears. Government of Indialaunched Pradhan MantriFasal Bima Yojana (PMFBY)during 2016-17 with a goal ofminimum premium andmaximum insurance forfarmer welfare. Premiumrates for all insured cropswere kept at lowest ascompared to all previouslyimplemented crop insuranceschemes. Crop insuranceunder PMFBY has gainedsignificant outreach wherebythe coverage of famers underthe scheme increased by 18%as compared to 2015-16 andpenetration on Gross CroppedArea (GCA) reached 30%during 2016-17. Sum Insuredper hectare was changed fromvalue of threshold yield toscale of finance under PMFBYwhich resulted in increase inoverall sum insured by morethan 70%. Also new cropinsurance scheme came upwith more comprehensivecoverage wherein add oncover such as preventedsowing, post harvest losses,mid season payments,localized risks are added withexisting standing crop cover.

Agriculture/Crop Insurance in India:Key issues and way forward

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Crop Insurance providescompensation to theinsured farmers in theevent of crop losses dueto various factors such asdeficit rainfall, excessrainfall, hightemperature, lowtemperature etc. Cropinsurance compensationduring adverse climaticconditions not onlycovers the farm lossesbut also encourageinvestment on farmingfor next season.

Azad Mishra

Vice President , HDFC ERGO GeneralInsurance Company Ltd. 1st

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Key issues to ponder overand way forward

1. Time window availablefor coverage

Issues

Time window available forcoverage of farmers undercrop insurance is inadequatedue to delay in issuance ofnotification in many states.During PMFBYimplementation in 2016-17,coverage time window in somestates was as short as 10-15days, which resulted intolower coverage of farmers.

Way Forward

In order to provide ampletime window for creatingawareness and ensuringmaximum enrolment underthe scheme, StateGovernment should issuenotification for PMFBY atleast 3 months before the cutoff date which will provideinsurance companies anddistrict administration ampletime to increase coverage byputting in well coordinatedefforts.

2. Awareness about thescheme

Issues

After the launch of PMFBY,large scale marketingactivities have been organisedby Central and StateGovernments which resultedin increased non loaneecoverage of 24% of totalcoverage as compared to 7%during 2015-16. however stillmany farmers are not yet

covered under the schemedue to lack of awareness aboutthe scheme features, benefits,process of enrolment andprocess of claim settlement.Even for the block leveladministration, schemeawareness is low due to lackof adequate trainingprogrammes.

Way Forward

Awareness of the insurancescheme and its operationalguidelines needs to spreaduniformly wherein StateGovernment, Districtadministration and insurancecompanies should makecollaborative efforts tocommunicate the schemefeatures and process ofenrolment via different medialike television advertisements, press release, pressadvertisements, radio

advertisements, brochures,posters, banners, leaflets etc.Also regular farmer’smeetings and workshopsneeds to be conducted toincrease the awareness aboutthe scheme. Timelines,process and mode ofenrolment needs to be clearlybriefed through variousmodes of communication. Thiswill bring in more confidenceamong the farmers forenrolment under scheme. ANationwide marketing planneeds to be launchedinvolving all stakeholderssomething in line with JanDhan Yojana. Targets can beallocated at block level forcoverage of non loaneefarmers and rewardprogramme may be initiatedin line with Rural Housingmission.

3. Documentation forcoverage of farmers

Issues

It has been observed thatland documents are yet to bedigitized in some states andeven if digitized, the recentchanges in the crop sown arenot updated. Further to thistenant farmers (especiallyoral lessee) and sharecroppers in many locationsare not able to get coveredunder the scheme due to lackof proper documentation.

Way Forward

Early adoption of modelleasing act in addition toseparate guidelines forcoverage of tenants(especially oral lessee) and

w

Timelines, processand mode of

enrolment needs to beclearly briefed

through variousmodes of

communication. Thiswill bring in more

confidence among thefarmers for enrolment

under scheme. ANationwide marketing

plan needs to belaunched involving all

stakeholderssomething in line with

Jan Dhan Yojana.

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landless farmers need to beframed and implemented.Digitization of land recordsneeds to be given priority andall the land records needs tobe generated in soft form in astate or central portal. Thisalso needs to be properlyupdated before the start ofseason (with owner detailsand crop sown). Digital IndiaLand Record ModernizationProgramme (DILMRP) wasinitiated to usher in newsystem of updated landrecords, automated mutation,integration of textual andspatial records. The progressof digitization of land recordsunder this programme needsto be monitored properly andall digital land records shouldbe linked to Aadhaar card(which in turn can be linkedto bank account). This willhelp in smooth quality checkof land documents and willreduce over insurance whichwill further reduce subsidyoutlay.

4. Lack of adequateinfrastructures toconduct Crop CuttingExperiments (CCEs)

Issues

After the launch of PMFBY,notified units have gone downto Gram Panchayat formajority of crops andlocations which resulted inlarger number of targeted

CCEs for estimating yield atnotified unit level. Due to lackof adequate manpower toconduct CCEs in a short timewindow (usually 20 to 40days), the quality of CCEs isaffected. In addition to that,manual capturing andconsolidation of CCEs yielddata further delays theprocess.

Way Forward

Considering lack ofinfrastructure to conductlarge number of CCEs allacross India , guidelinesshould be framed for usage ofremote sensing and dronebased technology for smartsampling which will reducethe expected number ofCCEs. CCEs should bemandatorily conducted on themobile app which will reducethe timeline for collating yielddata which consequently leadto reduced claim settlementtime with added benefits ofbringing transparency andimproving quality of CCEs.

5.Adherence to seasona-lity discipline

Issues

As per the PMFBYoperational guidelines,seasonality discipline hadbeen clearly mentioned withcut off date for submission offinal coverage details, subsidypayment, CCE yield data

submission and claimscomputation. Howeverseasonality discipline has notbeen followed properly inmany states wherein therehad been delay in receipt offinal coverage detail,premium subsidy paymentby States to insurancecompanies, submission of finalyield report which in turnresulted in the delayedpayment of claims to farmers.

Way Forward

In order to ensure claimsettlement to farmers as perthe defined time lines,seasonality discipline shouldbe properly followed by allstakeholders.

In order to achieve theambitious goal of reachingpenetration up to 50% underPM Flagship scheme, allstakeholders needs to beworking together within theframework of operationalguidelines with strictadherence to seasonalitydiscipline which will bring intransparency in the systemand claim settlement withdefined timelines will proveas confidence booster forfarmers even during distresssituations.

Views expressed in thispaper are author’s

personal only and not ofthe affiliatingorganisations

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India is a vast country withvaried agro climaticconditions comprising ofmore than 14 million farmerswith an average landholdingof 2 -3 acres growing anumber of crops in a seasonmostly for self-sustenanceand approximately 60% ofthe area doesn’t have assuredirrigation. The agriculturalproduction is therefore,greatly dependent on rains,particularly south westmonsoons that provide rainsfrom June to September.Even a slight deviation ofthese rains in time andquantity causes great lossesin yields of various crops inone or the other part ofcountry every season. Giventhis uncertainty of weather,crop insurance is veryimportant and relevant forthe country.

Crop insurance in India–an overview

In India, there have beenproposals for crop insurance

during pre-independence era.The concept of rainfallinsurance had been mooted byJ S Chakravarty as earlyas 1920. Soon afterindependence, a committeehad been constituted toexplore the possibility of cropinsurance. We have beenexperimenting with variousforms of crop insurance inIndia.Crop insurance for H4cotton based on Individualassessment was provided byfertilizer companies from

1972 to 1978. Thereafter, theemphasis shifted to yieldindex based on area approach.After some initial pilots, a full-fledged area yield index basedscheme was launched for theentire country in 1985 whichran successfully for fourteenyears. The experience gainedthrough these schemes gaveway to the formation of abroader yield index basedscheme launched in 1999 i.e.National AgriculturalInsurance scheme (NAIS)which covered all food cropsand annual commercial crops.In this scheme, an element ofindividual assessment waskept, though on a limitedscale, to gain experience andit was used very scarcely.While this scheme was beingimplemented, AIC also triedrevenue based insurance inthe form of Farm IncomeInsurance scheme (FIIS) in2003-04 with little success interms of coverage and claims.Weather aspect wasintroduced from 2003 and

TECHNOLOGY INTERVENTIONSIN CROP INSURANCE

Ashok K Yadav, Manager and Nima W Megeji,Deputy Manager

Agriculture Insurance Company of India Ltd.

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Even a slight deviationof these rains in timeand quantity causes

great losses in yields ofvarious crops in one or

the other part ofcountry every season.Given this uncertainty

of weather, cropinsurance is very

important and relevantfor the country.

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pilot Weather Based CropInsurance Scheme (WBCIS)was introduced from Kharif2007.

This long experience ofimplementing crop insuranceschemes had raised theexpectations of farmers andnow they expect insurance toprovide compensation ontheir individual experiencerather than the ‘areaapproach’, in other words thefarmers want the ‘basis risk’to be minimized or eliminatedaltogether. Besides this, thelosses need to be assessed ina more transparent way andclaims are to be paid soonafter the harvesting is over.These make the insurers’ jobmore complex and requirehuge manpower.

The ultimate solution to allthese expectations andcomplexities lies in the usageof technology.

Research & Development

All along, while implementingcrop insurance, use oftechnology in various modes,albeit on experimental basis,had been tried and tested byAIC in collaboration andpartnership with variousnational and internationalinstitutes, World Bank, stateagricultural universities etc.So, when the present schemePradhan Mantri Fasal BimaYojana (PMFBY) wasconceptualized, the results of

all these technologies used incrop insurance were availablereadily which probably gavethe confidence to incorporateand advocate the usage oftechnology for variousactivities in PMFBY.

Remote Sensing Technology(RST) has been used for Cropacreage estimation, crophealth /stress assessment,and development of modelsfor yield estimation. RST hasalso been used for Cropmapping and assessment ofcrop condition based onNormalized DifferenceVegetation Index (NDVI)analysis. In addition to foodcrops, it has been successfullyused to map tea acreage, teayield estimation andprediction using vegetationindices and agro

meteorological model.

Studies have been carriedout to develop and test asampling methodology usingthe co-witnessed CCEs andremote sensing to estimateGram Panchayat (GP) levelcrop yields from block-levelcrop yields. TerrestrialObserv ation and PredictionSystem (TOPS) Technologywith empirical/mechanisticmodels has been used tomonitor and predict cropgrowth profiles, crop stressand yields.Mobile phones were used togeo tag the experimentalplots and record the real timerelay of the whole process.The experience so gainedhelped in improving andcalibrating the technologyand provided the muchneeded confidence that cropinsurance products can befurther improved with theincorporation of technology .

There are perpetualshortcomings like overinsurance or mis-match ofarea insured viz a viz areasown, yield data reported notbeing in sync with the overallcrop condition or weatherconditions that prevailedduring the season. AIC hasput technology to practicaluse to counter some of theseissues and has demonstratedthat it can be effectively usedin crop insurance.

There are perpetualshortcomings like overinsurance or mis-matchof area insured viz a vizarea sown, yield datareported not being insync with the overall cropcondition or weatherconditions thatprevailed during theseason. AIC has puttechnology to practicaluse to counter some ofthese issues and hasdemonstrated that it canbe effectively used incrop insurance.

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Experiences on use ofTechnology by AIC

1.Wheat Insurance:Haryana and Punjab

The first practical technologycentered insurance productwas in the form of NDVIbased insurance for wheatcrop introduced by AIC insome pockets of Punjab andHaryana. As a precursor todevelopment of this product,correlations between Agro-meteorological parametersand NDVI values for pastseasons were established toenable current season yieldestimation. The final yield isa reflection of the biomass/crop vigour. “NormalizedDifference Vegetative Index

(NDVI)” is a measure ofbiomass or crop vigour in theplant derived through RemoteSensing Technology. Itnormally ranges between 0and 1, but, can be scaledbetween 0 and 250. Thescaled values were adoptedfor the purpose of insurance.Temperatures above certaindegree, particularly duringthe month of March are likelyto reduce wheat yieldconsiderably. Therefore,temperature was used as asecond parameter to triggerclaim payout under thisinsurance.

The claims were payableagainst the likelihood ofdiminished Wheat output/yield resulting from a) lowercrop vigour (biomass) asmeasured using satelliteimagery in terms of NDVIwithin the specified taluka /block during the month ofFebruary (preferably duringthe 2nd / 3rd weekcorresponding to peak cropvigour) and / or b) hightemperature (in degreecentigrade) consecutively forspecified number of daysabove specified levels in the1st and / or 2nd fortnight ofMarch as measured atReference Weather Station(RWS). The uptake of theinsurance product was low asthe farmers were not sureabout the efficacy of thistechnology.

2.Area discrepancy:Rajasthan

There was a huge differencein the area insured and thearea sown of gram crop, as pergovernment records, duringRabi 2013 in Churu district ofRajasthan. Therefore, areasown under gram crop wasestimated through the imagesobtained from satellite andcompared with the arearecorded by the statedepartment. The claims wereultimately paid on the basis ofarea sown under gram cropassessed through satelliteimagery.

3. Remote Sensing-Based Information andInsurance for Crops inEmerging Economies(RIICE): Tamil Nadu

An international project,Remote Sensing-BasedInformation and Insurance forCrops in Emerging Economies(RIICE) in partnership withGIZ, IRRI, SARMAP, TNAU,Allianz Re and AIC as theinsurance partner in India wasimplemented to generate cropyields and crop monitoringusing satellite imagery andcrop modeling from 2012onwards. After four years oftesting in Cuddalore,Shivgangai, Thanjavur,Nagapattinam and Trichydistricts of Tamil Nadu, theState Government found itreliable and ultimately agreed

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Technology should beused on a larger scale

for the implementationof PMFBY. Although theScheme lays emphasis

on the use of technologyfor acreage estimation,crop monitoring, mid-

season loss assessment,assessment of losses dueto localized calamitieslike hails, inundation,

landslide and post-harvest losses, its use

has not picked upbecause there is no

protocol for the usage oftechnology.

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to use the data generatedfrom this technology forassessing the area sown underpaddy crop to arrive at the‘sowing failure / crop failure’under PMFBY during Rabi2016. It is being extended toother areas of the State andother States are alsoassessing the idea of using it.

4. Yield datadiscrepancy: Gujarat

In spite of Kharif 2016 seasonbeing good and no adversereports on crop production,the yield data submitted bythe State department forground nut crop in Gujaratshowed losses in some specificdistricts. This was contestedwith scientific results i.e.NDVI derived from satelliteimages and Unmanned AerialVehicle (UAV) imagesinitially. The matter was thenreferred to GoI and theTechnical AdvisoryCommittee.

Technology played asignificant role in establishingthat the crop was not as badas being presented by theyield data of the statedepartment and thus aformula was agreed to arriveat the loss assessment,thereby reducing the claims.

Way forward

Technology should be used ona larger scale for theimplementation of PMFBY.Although the Scheme laysemphasis on the use oftechnology for acreageestimation, crop monitoring,mid-season loss assessment,assessment of losses due tolocalized calamities like hails,inundation, landslide andpost-harvest losses, its usehas not picked up becausethere is no protocol for theusage of technology.MahalanobisNational CropForecasting Centre (MNCFC)is developing some protocols

for the use of technology forvarious purposes which willgo a long way in adoption oftechnology in crop insurance.

References:

1. Mishra, P. K. (1995),‘Is Rainfall Insurance aNew Idea: PioneeringScheme Revisited’,Economic and PoliticalWeekly, vol. XXX, no.25, pp. A84–88.

2. Rao, K. N. (Ed.). 2013.Agriculture Insurance(IC-71). InsuranceInstitute of India.

3. Remote Sensing-Based Informationand Insurance forCrops in EmergingE c o n o m i e s( R I I C E ) . h t t p : / /www.riice.org/about-riice/

Views expressed in thispaper are author’s

personal only and not ofthe affiliatingorganisations

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Issue Focus

Monitoring theproduction of field crops isimportant for ensuring foodsecurity in India. Accurateand consistent information onthe area under production isnecessary for national andstate planning butconventional statisticalmethods cannot always meetthe requirements. Thisinformation is vital to thepolicy decisions related toimports, exports and prices,which directly influence food

monitoring. SyntheticAperture Radar (SAR)imagery is a promising optionto overcome the issue ofcloud cover. Recent andplanned launches of SARsensors viz., RISAT (India),Cosmoskymed (Italy), TerraSAR-X (Germany) andSentinel 1A (ESA) coupledwith state-of-the artautomated processingprovide sustainable solutionsto these challenges.

With latest advances inremote sensing and crop yieldmodeling, it is now possible toprovide accurate informationon crop acreage, crop health,yields, crop damages and lossduring floods and drought.Early estimation of the end ofthe season yield can helpinsurers to envisage pay-outsand early claim settlementswithout waiting for the CCE

security. Fluctuations inproduction of field crops,influenced by extremeweather events viz.,variations in onset, progressand withdrawals of monsoons,floods caused by torrentialrainfall and unevendistribution, necessiates aproper crop monitoringmechanism on a spatial scale.Further Climate change posesthreat to agricultural cropsthrough extreme weatherevents. To ensure resilienceamong the resource poormarginal farmers, disasterrisk reduction in terms of cropinsurance is needed.

Remote sensing hasthe scope for cost effectiveprecise estimates of croparea. However, the technicalchallenges viz. cloud coverduring cropping season, widerange of environments, smallland holdings and diverse andmixed cropping systemslimits the use of remotesensing as a tool for cropw

AGRICULTURAL / CROP INSURANCE ININDIA - PROBLEMS AND PROSPECTS

Dr. S. Pazhanivelan

Remote Sensing Applications in Crop Insurance – A success story fromTamilnadu using TNAU-RIICE technology

Remote sensing has thescope for cost effective

precise estimates ofcrop area. But the

technical challenges viz.cloud cover during

cropping season, widerange of environments,

small land holdings anddiverse and mixed

cropping systems limitsthe use of remote

sensing as a tool forcrop monitoring.

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data. TNAU hasdemonstrated the efficacy ofSAR based rice cropmonitoring and informationsystem in Tamil Naduthrough the RIICEProgramme ‘Remotesensing-based Informationand Insurance for Crops inEmerging economies’ incollaboration withInternational Rice ResearchInstitute (IRRI), GIZ andSarmap, Switzerland.

TNAU RIICE aims atreducing the vulnerability ofsmallholder farmers engagedin rice production by cropinsurance. RIICE technologymakes use of satellite data to

generate information like ricearea statistics, mid-seasonrice yield forecasts and end-of season yield estimatesdown to the village level. Thishelps government decisionmakers, insurers, and relieforganizations in bettermanaging domestic riceproduction during normalgrowing conditions and duringthe compensations afternatural catastrophes strike.

Initiated in 2012 in thestate of Tamil Nadu, India,the project with Tamil NaduAgricultural University as itslead implementation partnerhas been activelycollaborating with the stateGovernment and theinsurance industry towardsestablishing a successfulmodel of technology leadingto sustainable delivery ofproducts and services. Thiscomes at the backdrop ofsustained engagement withthe Government and creatinga policy environment whichallows the project baseddeliverables to be used byboth public and privateinsurers in portfoliomonitoring and claimadministration in case ofimminent losses. Due to theoutreach efforts, theGovernment of Tamil Nadugave official approval forpiloting TNAU-RIICE

technology in the year 2016.The ensuing cropping seasoni.e., Rabi 2016-17 saw theworst drought in Tamil Naduin last 140 years. RIICEmeasured the rice area lost tobe about 1 million ha. of thesown area covering close to 1million farmers.

Pradhan Mantri FasalBima Yojana (PMFBY) is aflagship scheme of theGovernment of India toprovide insurance coverageand financial support tofarmers in the event of failureof any of the notified crops,unsown area and damage toharvest produce as a result ofnatural calamities, pests anddiseases to stabilize theincome of farmers, and toencourage them to adoptmodern agricultural practices.The scheme is a considerableimprovement over allprevious insurance schemes inIndia which aims to cover 50percent of the farminghouseholds within next 3years. The scheme envisagesthe use of technologies viz.,Remote sensing, Drones andmobile applications. PMFBYhas provision forcompensation under differentclauses viz., Prevented, Failedsowing and total crop failuredue to extreme weatherevents.

Pradhan MantriFasal Bima Yojana(PMFBY) is a flagshipscheme of theGovernment of India toprovide insurancecoverage and financialsupport to farmers in theevent of failure of any ofthe notified crops,unsown area and damageto harvest produce as aresult of naturalcalamities, pests anddiseases to stabilize theincome of farmers, and toencourage them to adoptmodern agriculturalpractices. w

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Prevented Sowing/Failed SowingRisk

On AccountPayment

Smart sampling ofCCEs

Acreage Estimation

End of the SeasonYield Estimates

Description as per PMFBY

Prevented sowing risk can bedefined as the risk of farmersnot being able to plan/sow thenotified crops in the insuredarea due to adverse seasonalconditions. A pay-out of up to25% of the sum insured isforeseen. The insurance policywill be voided thereafter.

PMFBY specifies “on accountpayment” of up to 25% of thesum insured in case thefollowing two conditions aremet: a) the expected finalseason yield is below 50% ofthreshold yield and b) Allperils are covered and thepayout is at revenue villagelevel.

PMFBY outlines the roleremote sensing technologycan play in the smart samplingof CCEs and can besuccessfully used to target theCCEs within the InsuranceUnit (IU)

It has been observed in someinstances that the area notifiedfor insurance exceeds theactual planted area in a giveninsurance unit. Fair cropinsurance should ensurecorrect insurance areas andthe PMFBY has provision toaddress this anomaly therebyavoiding area discrepancy.

Use of proxy indicators. Thisprovides the opportunity touse remote sensing based yieldindices to provide an alternatesource of yield data apart fromthe official CCEs.

Use of RIICE technology

RIICE satellite technology can be usedto verify the occurrence of the followingperils:Flood, drought, inundation as wellas their impact on village level. It willonly take 10 days post event (inexceptional cases 12 days) to verify theloss.

Prior to the mid-season RIICE canreport on loss areas as in the above case.In addition, as from the middle of theseason onwards, RIICE can also predictthe impact of a certain natural calamityon the expected final yield at the end ofthe season.

Before the end of the season RIICE canprepare a list of vunerable areasshowing symptoms of crop stress due toadverse seasonal conditions. This willlead to prioritisation of Insurance Units(IUs) across a homogenous regionwhere more number of CCEs arerequired.

In order to present an accurate overviewand to avoid over- or underinsurance, amap will be generated to show thelocation of rice in the monitored season,demarcating rice growing areas fromthe non-rice growing areas. This can bedone at village level, delivering the ricegrowing area in ha. on village level. Thisproduct can be delivered at mid-seasonat the earliest but during the upcomingseason it will be delivered two weeksafter the end of the season at the latest.

The remote sensing yield data isgenerated immediately after the end ofseason thereby providing sufficient timeto identify areas where expected finalyield will be lower. This will provide theareas where the official CCE data fromclaims point of view is critical. The otherway is to use the remote sensing basedyield data for actual claim settlement.

Application as perPMFBY Guidelines

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Methodology

The basic idea behindthe generation of rice acreageusing radar data is theanalysis of changes in theacquired data over time.Measurement of temporalchanges of SAR response dueto the rice plants phenologicalstatus lead to theidentification of the areassubject to transplanting. Therice acreage statistics arestored in map format showingthe rice extent and, in formof numerical tables,quantifying the dimension ofthe area at the smallestadministrative level -typically village unit-cultivated by rice. Theseproducts are linked todistrict, region, state andcountry, so that statistics on

any of these administrativeunits can be produced.

Rice yield prediction isperformed by combiningremote sensing, in situ,climatic data and an AgroMeteorological Model.Production (I), finally, issimply calculated bycombining yield estimation (t/ha) and the acreage (ha)derived from the radar data.

T N A U - R I I C Etechnology resulted in higheraccuracies of 89-93% for ricearea and 87-90% for rice yield

estimation and the salientfeatures of the technology are· High resolution Synthetic

Aperture Radar (SAR)imageries were used tomap and monitor Paddycrop area coverage.

· Application of MAPscape-Rice software withautomated processingchain.

· Integrating Crop Growthsimulation model ORYZAand RiceYES interface foryield estimation.

Satellite used : Sentinel 1A (ESA)

Spatial resolution : 20m

Temporal resolution : 12 days

Data acquisition : 19th Sep 2016 - 17th Jan 2017

No. of acquisitions : 11

SAR based Remote Sensing Products used in Crop Insurance

ProductRice area maps

Date of start ofseason map

Production lossestimates

End of SeasonModeled Yields

FrequencyOnce per season

monitored

Once per seasonmonitored

Once eventoccurred

End of season

DescriptionA detailed map of the rice growing area detectedfrom the analysis of Sentinel 1A data acquired every12 days through the monitored season.

The time series of images used to estimate, the startdate of the growing season for each pixel. This is acritical input to the crop model that estimates yield.It is also critical for estimating the area that hasbeen planted at a given date.

If the event occurs in a season that is beingmonitored, the imagery can be interpreted toestimate the area affected. The yield estimates areused to estimate the expected production loss fromthis damage per mapping unit.

Yield model incorporates weather and SAR data toproduce a yield value for each calibrated spatial unit

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Fig. Utility of TNAU-RIICE technology in PMFBY

Department of RemoteSensing and GIS, TamilnaduAgricultural Universityassessed the impact of recentdrought during 2016 on cropcondition using Sentinel 1Asatellite data acquiredbetween September 2016and January 2017 at 12 daysinterval. The annual rice areamap, seasonality maps andstatistics, crop signature andyield information were usedto meet the requirements ofdifferent features of PMFBYcrop insurance scheme.

1.Remote sensing forPrevented and Failedsowing

A detailed map of the ricegrowing area detected fromthe analysis of Sentinel 1Adata acquired during themonitored season was used togenerate rice area statisticsevery 12 days at village level.

Assessing Rice crop withSentinel 1A Satellite

Rice start of the Season mapand progression of planting

Normal area sown figures for the notified villages werecompared with the village wise area generated using SAR dataand the villages were identified for invoking prevented sowingwherever the area sown was less than 25 % with the reductioncaused by delayed onset of monsoon or water release fromcanal preventing the farmers from sowing or planting.

Failed sowing Total Crop Failure

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Backscattering signature for crop field were generated using the dB stack derivedfrom 11 date SAR images and the date of crop failure was assessed and the villages wereidentified for failed sowing (within thirty days after sowing) or total crop failure (beyond 30days).

District Villages Prevented / TotalChecked Failed Sowing Crop Failure

Pudukottai 193 27 160Ramnad 51 38 13Nagapattinam 155 34 112Tiruvarur 378 26 18Cuddalore 183 4 179Ariyalur 31 22 9Tiruchirapalli 502 210 -Erode 365 127 -Tiruvannamalai 1 - 1Kancheepuram 30 - 30Tiruvallur 16 - 16Virudhunagar 318 - 31Sivaganga 293 41 252

Total 2516 529 821

2. Assessing the impactof Flood and droughtusing Remote sensing

i. Flood maps from SARdata

The StateGovernmentof Tamil Nadu, India initiatedseveral policy level measuresin alleviating the losses in theaftermath of the 2015

devastating floods based on atimely assessment reportcontaining flood maps andstatistics provided byTNAU.The deadly depressioncrossing over the Tamil Naducoast in early November2015(as shown in the leftimage captured by ameteorological satellite),caused heavy rains and

subsequent flooding in manydistricts of Tamil Naduresulting in severe damage toagricultural land andproperty. In response to thecatastrophe, the RIICE’sflood assessment report wasdelivered as part of the reliefand flood rehabilitation effortsto the Government of TamilNadu .

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ii. Impact of Drought on crop condition

The impact of recent drought during 2016 on crop condition was monitored by retrievingtime series Leaf area Index using Sentinel 1A SAR satellite data and composite NDVI derivedfrom MODIS. The area under the classes of moderate and severe drought was assessed andshared with Insurance companies for possible loss and anticipated claim assessments.

LAI Map of Rice area NDVI Map of Rice area

3. Yield loss assessment

Rice yields and henceproduction at district, blockand village level are assessedby integrating remotesensing products viz., Ricearea, Start of the Season anddB Stack into the cropgrowth simulation modelORYZA. Yield loss if anywere estimated bycomparing satellite derivedrice yields with thresholdyields for the villages asnotified.

Samba rice (Paddy-II)growing villages inTamilnadu were monitoredfor crop loss assessment andthe remote sensingtechnology helped inidentifying or invoking

failure in 821 villages. In total8,80,179 farmers werebenefitted from the cropinsurance and the payoutswere to the tune of Rs.2,769.15 crores. The satellitetechnology has helped ingetting quicker payouts andalso to maximize thecompensation which was duefor the farmers ensuring thesocial protection.

Insurance payouts through TNAU-RIICE Technology

Crop Insurance No. of Farmers Claim Amountfeature benefitted (Rs. In Crores)

Prevented sowing 47,513 60.46Through RIICE TechnologyYield loss claims - RIICE 2,56,190 933.61Yield loss claims - DES 5,76,179 1,775.08Total 8,80,179 2,769.15

Views expressed in thispaper are author’s

personal only and not ofthe affiliatingorganisations

prevented/failed sowing in529 villages and total crop

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Given the constraints ofagrarian landscape of India,as a crop insurance solution,PMFBYis a good blend ofyield index insuranceworking on “Unit Area”(village panchayat / block /mandal/ patwari halka/revenue circle etc.) andtraditional named perili n s u r a n c e ( l a n d s l i d e ,hailstorm and inundation)aiming individual farm-based damage assessment.Farm based damageassessment is also prescribedfor post-harvest-on-fieldlosses due to unseasonal orcyclonic rains that damagecrops kept on the field fordrying. Therefore, PMFBYis a combination that takescare of systemic or covariaterisk associated withwidespread calamities aswell as idiosyncratic lossesarising from localisedcalamities viz hailstorm,landslide and inundation.Farmers are also indemni-fied in case they are not ableto sow, plant or transplantthe crop due to early-seasonadverse weather conditionsviz. delayed arrival ofmonsoon etc. Even in case of

the sown seed or seedlings,the seeds do not germinateor fail to survive due toadverse weather conditions,the farmers are eligible forcompensation. Whereas thefirst case is known asprevented sowing, thesecond one is called failedsowing/ planting.Further incase of mid-season adverseweather conditions, viz.prolonged dry spell aftergood initiation of crop, whichmay lead to yield losses, ad-hoc or on-account paymentsare prescribed so thatfarmers get some ad-hoccompensation in the interimto let him look for alternativeoperations.

No scheme previously hasoffered such a compre-hensive protection.However farmers probablyalso look for compensationagainst widespread diseaseand pest attacks that impactmany farms simultaneouslywhich sometimes cannot beanticipated or contained.PMFBY as on date do notcompensate such losses untiland unless they are reflectedin the yield estimates of theInsurance Unit Area.

Given that PMFBY beingthe most feasibleinsurance productwhich purportedly suitsthe majority stake-holders and that too atthe cheapest price forthe farmers, what isstopping it to be alandslide success? The

Issue FocusPradhan Mantri Fasal BimaYojana(PMFBY) – Issues inhibiting its bigsuccess and probable way forward

M K Poddar, GM,Agriculture Insurance Company of India Ltd

PMFBY is acombination that takes

care of systemic orcovariate risk

associated withwidespread calamitiesas well as idiosyncratic

losses arising fromlocalised calamities

viz hailstorm,landslide and

inundation.Farmersare also indemnified incase they are not able

to sow, plant ortransplant the cropdue to early-season

adverse weatherconditions viz. delayedarrival of monsoon etc.

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issues are many andmultifarious illustratedas under.

1. The critical challenge is indistribution, that is, mostlythe “bad risks” are gettinginsured. Areas or cropsprone to losses due to lack ofirrigation facility or the cropswhich are too susceptible toadverse weather conditionsare usually covered, leavingmajority of the “goodrisks” out of the insurancebasket. It goes withoutsaying that predominantly-bad-risk-insurance portfoliowill attract a high premiumrate which in turn will put astrain on StateGovernment’sbudget. Thisskewed distribution of risk isbasically due toadministrative slackness.

Crop Insurance in India hasalways remained a multi-agency program whereinroles of various agencies like,Banks/ PACS (PrimaryAgriculture CooperativeSocieties), StateGovernments and insuranceCompanies though well-defined are yet poorlyexecuted as there is noaccountability for notperforming the assignedduties. For example, forloanee farmers, the schemeis compulsory, but asubstantial part of theeligible loans is left un-insured on some pretext orother. Non-compliance ofcompulsory insurance,particularly from the goodrisk areas is making thescheme costlier for theGovernment, as for thefarmers the premium rate iscapped.

2. The second issue as far assustainability is concerned isthat, even if the good risksare brought in, andcompulsory provision iscomplied fully, the premiumsubsidy liability of the StateGovernments will go up inabsolute terms at least in theshort run. Therefore, StateGovernmentsmust allocatemore budget for PMFBYwhich most of the time isseen as an expenditurewasted. Only exception, inrecent times when Statescould see value in insuranceis during Rabi 2016-17,

when the States ofKarnataka and Tamil Nadusaw the non-loaneeparticipation soaringexceptionally high, followedby almost 300%loss ratio(the ratio of indemnity paid,and premium collected). Infact, from the insurers’ pointof view, this is a glaringexample of adverse selectionin a draught-like situation, atypical moral hazard that gotestablished during NAIS(National AgriculturalInsurance Scheme) regime.Insurance Companies raiseddoubts about (i) areas beinginsured without any cropsattempted by the farmersand (ii) extensive recordingof zero yields withoutconducting Crop CuttingExperiments (CCEs) by theState Government. This isan issue that plagued cropinsurance system in India fora long time and is still posinga problem in putting thePMFBY on a transparentand sustainable footing. Theonly solution isadvancing the cut-offdate for enrolment offarmers to a point oftime when the farmersare not aware about theimpending losses.

Huge financial burden onStates for running PMFBY isone of the critical limitingfactors for sustainability.Given an option, most of theStates would like to quitPMFBY and perhaps wouldlike to come back to NAIS

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Huge financial burdenon States for runningPMFBY is one of the

critical limiting factorsfor sustainability.

Given an option, mostof the States would like

to quit PMFBY andperhaps would like tocome back to NAIS for

the primary reasonbeing that under

PMFBY the money(premium subsidy) has

to be paid upfront tothe insurance

companies withoutknowing the return.

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for the primary reason beingthat under PMFBY themoney (premium subsidy)has to be paid upfront to theinsurance companieswithout knowing the return.States perceive thepayment of subsidy is aninstant loss and not as acost for transfer of riskto the insurer. This viewof seeing insurance premiumas instant loss rather thancost of risk transfer is allpervading and prevalentacross all lines of generalinsurance business. We arebasically an insuranceaverse society worried aboutshort term losses ratherthan long term riskmanagement solutions.

3. Thirdly, as mentionedabove, huge outgo asadvance premium subsidyseen as a costly affair forcertain cash strappedStates, raising the questionson the sustainability ofPMFBY from the politicalview point. Adding salt to theinjury, the data collectedfrom insurance industryshows that all the companiescombined made a gross profitof Rs.7000 croreapproximately out of firstyear of operation i.e., during2016-17, which is roughly32% of the national premiumvolume. Insuranceindustry’s view point isdiametrically oppositethough. Industry feels thathaving a 32% margin(excluding operating

expenses) in a good year like2016-17 is unsustainable asin a widespread droughtsituation like that of 2015-16where losses could go up toRs 50000 crore against aninsured liability of Rs200000 crore (2016-17 suminsured). Therefore, fromeither side there are issuesof sustainability loominglarge. To give comfort to theStates’ finances, premiumrates need to come down asquickly as possible and thiswill be possible, if only thelegitimate claims are paid.For area yield indexinsurance likePMFBY,season-end yieldlosses overwhelminglyconstitute the total claims.Therefore, yield estimationthrough CCEs assumes agreat importance.

4. The fourth issue is aboutyield estimation or lossestimation. Yield data of thepast years and for thecurrent insured season isperhaps the single mostimportant element aroundwhich the entiremathematics of Indian cropinsurance programrevolves. Irony is that CCEsthrough which the yield datais arrived at, is an ill-managed activity of theIndian Crop Insuranceprogram which needsrevamping. In fact, in thesixties when CCEmethodology and implementation was conceptualised,crop insurance was notthere. It was conceptualisedonly for generating basicagricultural statistics at adistrict or at sub-districtlevel to assist planning andpolicy making. The basicstatistical data compiledwere area, production andyield (APY) in respect of aparticular crop in a district.The survey through whichthis estimate is generated iscalled GCES (General CropEstimation Survey). Whencrop insurance started atcountry level in 1985, thesame GCES data was usedfor calculating guaranteedyield (threshold yield) andalso for estimating actualyield in the insured season.This means that the GCESdata collated for APYpurposes would also be usedfor insurance purposes forcalculating compensation for

Yield data of the pastyears and for the

current insured seasonis perhaps the single

most importantelement around whichthe entire mathematics

of Indian cropinsurance program

revolves. Irony is thatCCEs through which

the yield data isarrived at, is an ill-

managed activity of theIndian Crop Insuranceprogram which needs

revamping.

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yield losses. However,unfortunately within twoyears of implementation ofCCIS (Comprehensive CropInsurance Scheme 1985)some of the States startedaltering the CCE processwhich otherwise has soundstatistical basis. The statesstarted producing two seriesof yield estimates one forcrop insurance and other onefor APY statistics. WhenNAIS was introduced in1999 replacing CCIS, it wasclearly mandated in the

Scheme that only singleseries of estimate i.e., GCESestimate data would be usedfor both insurance and forAPY statistics to stopproducing a separate dataseries for crop insurance.However, NAIS was havinganother mandate of loweringthe size of insurance unit tovillage panchayat level formajor crops whichnecessitated an increasednumber of CCEs at districtlevel. The States could notdevelop their infrastructureto conduct the increasednumber of CCEs andgradually the qualitydeclined to an alarming level.

PMFBY, as such, requires asmany as 30 to 35 lakhs ofCCEs to be conducted duringKharif and Rabi seasonswhich appears to be aninsurmountable task for theStates to handle. It may notbe possible ever for theStates to conduct so manyCCEs in such a short timewindow ensuring quality. Tomeet the demand manystates are going foroutsourcing without anycapacity building resulting inpoor quality of data.Certainly conducting somany CCEs throughoutsourcing or otherwise isnot a sustainable propositionand this practice willeventually lead to large scaledisputes involving theinsurance companies andfarmers.

Only solution to theeverlasting CCEs issue is,perhaps, the use ofremote sensing. Nowadays satellite imagery andremote sensing technologyhave improved to an extentwhich can provide crop-areaestimation with 85% to 90%accuracy at village/ villagepanchayat level. As far asyield estimation isconcerned, accuracy variesfrom crop to crop, but itwould be safe to say that thelatest technology supportedby adequate number ofground truthing (field datacollection) and collection ofother related data likeweather data etc.has thepotential to produce a goodindicative yield. Over thelast couple of decadesremote sensing scientistsworking in the field ofagriculture have developedmany indices based onsatellite imagery viz.Normalised DifferenceVegetation Index (NDVI),NDWI (NormalisedDifference Wetness Index),Standard PrecipitationIndex (SPI), VegetationHealth Index (VHI), LeafArea index (LAI) and so on.All these indices attempt toproduce a yield forecast ormodelled yield at areasonably acceptable level.The European SpaceAgency’s (www.esa.int)Copernicus SatelliteProgram has come up with adozen earth observation

Only solution to theeverlasting CCEs issue is,perhaps, the use ofremote sensing. Nowadays satellite imageryand remote sensingtechnology haveimproved to an extentwhich can provide crop-area estimation with85% to 90% accuracy atvillage/ villagepanchayat level. As far asyield estimation isconcerned, accuracyvaries from crop to crop,but it would be safe to saythat the latesttechnology supported byadequate number ofground truthing (fielddata collection) andcollection of otherrelated data likeweather data etc.has thepotential to produce agood indicative yield.

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satellites named as SentinelSeries with primaryemphasis on studying theimpact of climate change andhow to mitigate the same toensure civil security. Thebest part of ESA’s programis that the sentinel data is ofvery high resolution, goodfrequency and swath andavailable free of cost for useby a registered user.Recently many privateresearch and start-upagencies have started usingthese high-resolution dataand started producing goodresults in terms of crophealth monitoring and yieldforecast.

It is also worth mentioningthat the Honourable PMheld a meeting on technologyintervention in PMFBY wayback in mid-2016 involvingDST, ISRO, NRSC andDAC&FW to bring inefficiency, objectivity andsustainability. Subsequent tothis NITI Aayog Agriculturevertical constituted a TaskForce on EnhancingTechnology Intervention inAgriculture Insurance. TheTask Force has sincesubmitted itsrecommendation to DAC&FW almost a year back.

For leveraging thetechnology intervention inPMFBY what is immediatelyneeded is devising anddefining protocols for usingremote sensing and data for

various claim triggers likeprevented sowing, mid-season adversity, damageassessment for localisedcalamities and for post-harvest losses. Until andunless the protocols aredefined and notified, therewill be a serious lack ofstandardisation.

The Probable way forwardis, therefore, to have acredible independentinstitutional mechanism tousher in usage of technologyin a structured manner.Ideally the agency should bemore of a Scientific Agencyof national eminence andinternational access such asNational Remote SensingC e n t r e ( h t t p s : / /w w w . n r s c . g o v . i n /

agriculture) which has adedicated AgriculturalDivision that does all kind ofnecessary remote sensingand capacity buildingactivities thatcan usher inthe technology interventionin PMFBY in a time boundmanner.

Problem with WeatherBased Insurance:Theother alternative to yieldindex insurance is WeatherBased Crop InsuranceScheme (WBCIS). WBCIScaught the imagination of theCentral and StateGovernments from 2007onwards and was an instantsuccess, and it becamealmost equal to NAIS in2012-13 in terms of areaunder insurance. Success ofWBCIS is based on thefundamentals of strongcrop-weather relationship.With growth of WBCIS,gradually, the fundamentalswere compromised, andstakeholders were found tobe more inquisitive infinding premium-claimrelationship so much so thatthe pay-out term- sheetswere developed assuringsure claims. This led to veryhigh premium rate forWBCIS. At present WBCIS isa poor cousin of PMFBY, onlyimplemented forhorticultural crops and insome districts chosen by theState governments.

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Insurance doesn’treduce the chances ofdrought or floodhappening, what it doesis, spreading theadverse impact overspace and time so thatthe affected farmers donot get a rude financialshock and theirlivelihood is reasonablysustained. Therefore,insurance is a necessityparticularly foragriculture sector,otherwise 125 countriesin the world would nothave establishedagriculture insurancesystem.

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PMFBY – Probable WayForward tosustainability

It is not difficult toappreciate the discomfort ofthe States which have to payPMFBY premium subsidythat takes a lion’s share ofState’s agriculture budgetonly to discover later in theyear that the money hasbeen made to some of theInsurance Companies. If ithappens year after year, onecan be sure of criticismpouring in from all quarters.The issue is that nobodywould like a commercialcompany making money outof farmers’ plight, given theagrarian distress in thecountry.

Over last 17 years, startingwith the introduction ofNAIS in Rabi1999-2000,Government, Centre and theStates combined spentapproximately an amount ofRs 75000 crore in imple-menting crop insurance. Aquestion arises whether theamount could have beenbetter utilized in the form ofdeveloping long-term capitalinvestment viz., augmentingirrigation facilities in 104perennially drought-pronedistricts. There is no doubtthat the cost of insurancewould have been much lowerby de-risking agriculturewith more cropped areascovered under permanentirrigation. Perhaps this is thereason why the CentralGovernment has already

initiated the PM KrishiSinchai Yojana, a 5-year-Rs.50000 cr project in 2015.

Insurance doesn’t reducethe chances of drought orflood happening, what it doesis, spreading the adverseimpact over space and timeso that the affected farmersdo not get a rude financialshock and their livelihood isreasonably sustained.Therefore, insurance is anecessity particularly foragriculture sector, otherwise125 countries in the worldwould not have establishedagriculture insurancesystem.It is also to be notedthat the agriculturalinsurance system is betterestablished and gainingstrength in most of the highand middle-incomecountries (where contri-bution of agriculture to theirrespective national GDP is insingle digit only) than inlower-middle and low-income countries. In manydeveloped and developingcountries, the system iscodified through properlegislation, so the variousagencies involved in theprocess do their jobsincerely. In India anAgricultural InsuranceAct is overdue. Time is ripethat India takes it seriouslyand goes for it as asubstantial part of Centraland State funds are involved.

Coming back to the issue ofStates’ discomfort is payingupfront premium subsidy –

a different model of financialadministration can bethought of where ininsurance company will notbe allowed to make any largeprofit. The empanelledInsurance Company will doeverything that it is requiredto do today and will continueto participate in thetendering process to win thedistricts and clusters.Additionally, they will aslosubmit the accounts at theend of the year to the Centreand States. Insurancecompanies will carry therisks with an overall cap of,say, 120% on its portfolioand a cap of, say, 80%. Whichmeans losses beyond120%falls on Central andState at a ratio of 40:60,whereas surplus arising outof pure losses below 80% isploughed back to the Centreand State in the same ratio.Centre and every State willcreate a separate cropinsurance fund account(similar to CCIS regime)which will be used only forcrop insurance purposes.Minimum limits of variousexpenses such asmanagement and publicityexpenses etc. to be borne byan insurance company canbe prescribed so thatinsurance companies arebound to incur the minimumservice related expenses tokeep the service quality at astandard level. InsuranceCompanies will be free tomake their own reinsurancearrangement to protect their

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own account.With thisarrangement the cost ofreinsurance will also comedown. As far as upfrontpremium subsidy isconcerned, the sharingpattern between Centre andState may be, 60:40, Centrepicking up a greater sharei.e., 60% of the premiumsubsidy leaving 40% to beborne by the State in placeof 50:50 at present. This willput less pressure on theState’s budget making themmore comfortable. On theclaims financing side beyond120% the sharing may be areverse one i.e., 40% Centreand 60% State – this willmake States more vigilantabout maintaining qualitycheck on the Crop CuttingExperiments. StateGovernments may choosefor reinsurance protection toprotect their own account.

Since insurance companies’losses are capped at 120%,certainly the actuarialpremium rate will comedown by at least 10% - 20%

initially and with greater riskmanagement by insurancecompanies and States, thepremium rate may fallfurther after some time.Insurance companies will beencouraged to use allscientific tools toauthenticate losses. It will bea win-win situation for all, forthe Scheme, for the Centre,for the States and for long-term insurers.

Conclusion:

PMFBY is a well-designedinsurance solution in theIndian context characterisedby large number of smallland holdings. It is quitecomprehensive in coveringthe major production risksinduced by adverse weatherconditions during the entirecrop life cycle. The schemeis very cheap for the farmersthough perceived as costlyby the State governments.

PMFBY, to be a majorsuccess needs adequatesupport services. Tofacilitate this, following the

best practices prevailingelsewhere particularly inhigh and middle-incomecountries, a comprehensivelegislation on AgricultureInsurance should be put inplace. Till that point of timeat least an IndependentAgency should be set up as apart of strong institutionalmechanism to objectivelyand transparently assesscrop losses in the insuranceunits. Remote sensingtechnology is a handy tool toobjectively assess crop areaplanted and monitor crophealth and to ultimatelyarrive at an indicative yieldor yield losses.

Further to strike a win-winsituation for all thestakeholders the existingrisk sharing betweenInsurance companies,Central and StateGovernments andReinsurers may be reviewedas suggested above.

Views expressed in thispaper are author’s

personal only and not ofthe affiliatingorganisations

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Vivek Lalan,Asst. VP, Agri Business,

Bajaj Allianz General Insurance

With weather being itsgreatest ally as well as itsgreatest adversary, agri-culture is one of the mostelemental form of activitieswhere a farmer toils theground, and reaps thereward – an occupation vitalfor the very sustenance ofhuman life on earth. In acountry like India, whereagriculture and allied sectorsaccount for around 14% of theGDP and employ about 50%of the workforce, is rankedtop in a list of populationsmost at risk from naturaldisasters, adequate solutionsneed to be implemented torender the economy lessexposed.

Crop insurance washence devised by Indianpolicy makers to make goodthe financial losses incurredby the agrarian community ofIndia. While the first evercrop insurance scheme gotimplemented in 1972, thecredit for pioneering the ideagoes to J. S. Chakarvarti, whoas early as in 1915, hadproposed rainfall based

A Closer Look at AgricultureInsurance of India

agricultural insuranceschemes.

The Schemes as ofToday

Fast forward to today,the Pradhan Mantri FasalBima Yojna, implementedfrom Kharif 2016 onwardsworks on the principle of –“One Season, One Crop, OnePremium Rate”.

Under this scheme thestates are divided intohomogeneous clusters based

on their risk profile, premiumpotential and agro-climaticzones and each cluster isallotted to a single insurancecompany based oncompetitive bidding. There isno capping on rates, hence,insurance companies will beable to charge actuarialpremium for the clusters, butthe farmer’s share is limitedto 2% of the total premium inKharif and 1.5% of the totalpremium in Rabi for foodgrain and oil seed crops and5% of the total premium forcommercial and horticulturecrops. The claims are afunction of Crop cuttingexperiments done by revenuedepartments across thecountry.

The scheme is spreadacross an area of 57 millionhectares covering 5.71 crorefarmers in its first year ofoperations itself, as against4.85 crore in 2015-16. Cropinsurance witnessed an 18%spike in penetration in thevery first year of the scheme’simplementation. Assuming asimilar rate of growth, thew

Crop insurance washence devised by Indianpolicy makers to makegood the financial lossesincurred by the agrariancommunity of India.While the first ever cropinsurance scheme gotimplemented in 1972, thecredit for pioneering theidea goes to J. S.Chakarvarti, who asearly as in 1915, hadproposed rainfall basedagricultural insuranceschemes.

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penetration of scheme shouldsoon reach 50% in nextcouple of years.

Another existingscheme that has beenrestructured and re-implemented is the WBCIS –the Weather Based CropInsurance Scheme. Thisscheme provides protection tothe insured cultivators in theevent of loss in crops yieldsresulting from the adverseweather incidences, like un-seasonal/excess rainfall, heat(temperature), frost, relativehumidity etc. Claims arisewhen there is a certainadverse deviation in ActualWeather ParameterIncidence in Reference UnitAreas (RUA) (as per theweather data measured atReference Weather Stations),e.g. its “Actual temperature”within the time periodspecified in the Benefit Tableis either less or morecompared to the specified “temperature Trigger”,leading to crop losses. In suchcase, subject to the terms andconditions of the Scheme, allinsured cultivators under aparticular crop shall bedeemed to have suffered thesame “adverse deviation” intemperature and becomeeligible for claims.

A Closer Look

On face, with numberssupporting it, the scheme looksuccessful, however, certainchallenges in its implementa-tion still remain. A deepanalysis of the same reveal

the problem areas thatcontinue to hinder a smoothfunctioning of the cropinsurance schemes of India. Afew have been broadlyanalyzed and discussedthrough this article:

1. Delay in Transferof Data: Since the actuallosses are determined basisCrop Cutting Experimentsconducted by StateGovernments, the claimpayments highly rely oncorrect and timely flow ofyield data. Often this datatakes a lot of time to gettransferred from the farms tothe insurer’s data base.Instead of manual processesof data keeping, the StateGovernments need to startusing technology to captureresults of crop cuttingexperiments. An applicationhas already been developedby Central Government forthis purpose, which ensurestimely submission of yielddata to Government andInsurance companies so as toenable a faster claimsettlement. Efforts also, needto be taken to enable DirectBank Transfers(DBT) so thatfarmers get the claimpayment directly in theiraccount. This can be doneonce AADHAR number islinked to all bank accountswhich will make DBT easierand errorless.

2. Low InsurancePenetration: For thepurpose of insurance, farmersare usually classified asLoanee and Non Loanee

farmers basis their creditusage through banks. Thescheme guidelines suggeststhat Loanee farmers are to becovered compulsorily throughtheir banks. However, thenumber of loanee farmerscovered under the scheme isabysmal as compared to theKisan Credit Cards (KCC)issued. Significant effortsneed to be taken to improvethe insurance outreach to theloanee farmers. One suchenabler would be linking ofAADHAR numbers with bankaccounts which will make iteasier to identify defaultingbranches.

The non loanee farmersalso receive the samepremium subsidy as theloanee farmers. However,they are not mandatorilycovered under the scheme. Anon loanee farmer can use hisBank, Agent or CommonService Centre as well forenrolment under the scheme.Interesting to note, IRDAIhas authorized all VillageLevel Entrepreneurs(Common Service Center),numbering up to anapproximate of 2.4 lakhs, tosell crop insurance. Neverbefore was such a hugechannel was opened upovernight to increasepenetration of insurance.Though, in the first year of thescheme’s implementation,around 1.37 crore farmerswere insured under the nonloanee category, still, the nonloanee coverage has a hugescope of improvement.

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3. Low Levels ofInsurance Awareness:The biggest selling point forany product is its timelyadoption by its targetedcustomers. This has been oneof the chink in the armour ofthe crop insurance schemes ofIndia. This is a main challengearea as a lot of farmers needto be still made cognizant ofthe benefits of having theircrops covered by an insurancepolicy. They need to beeducated on the coverageoffered and on what’s coveredand what’s not covered by thevarious schemes. Theinsurers and theGovernment. together canhence do wide range outreachprogrammes aiming towardssimplification of the policyclauses and conditions to thefarming grassroots. Thiswould also help to build apositive image about theseschemes which despite havinga claims ratio of around 70%are often doubted for theirreliability in protecting afarmer’s financial interest.

4. Delayed ClaimsSettlement: Claims are aninsurance scheme’s momentof truth and a delay in claimsdissemination can causesufficient discomfort. Toaddress the issue, the use oftechnology in claimsassessment is being thoughtoff from a long time. A lot ofwork is being done on remotesensing technology, whereinthe crop health can beassessed to a great extentusing NDVI (NormalizedDifference Vegetation Index)signatures. Stakeholders

should sit together to decidehow to use this data todecrease the number of CropCutting Experiments (CCEs).This will reduce the financialburden on states andinsurance companies,improve efficiency and willenable timely claimsettlement.

Conclusion

There needs to be aparadigm shift in how we lookat insurance in India, whereit is historically viewed as aninvestment rather than a riskmitigation tool. The lack ofawareness amongst thefarmers or other consumersin general, is rather a bigcaveat of the financialeducation system of thecountry which triggersawareness deficit on thevarious financial tools andlimits the opening of bankaccounts and linking themwith Unique IDs. It hencebecomes the collective

responsibility of insurancecompanies along withGovernments to restore faithof farmers in insurance as aconcept.

Large scale awarenessand education programs oninsurance need to beconducted at the grassrootslevels so that the benefitsseep down to even small scaleand tenant farmers. Theinfusion of technology, at alllevels of the schemeimplementation fromissuance to claims payout isanother pre-requisite to asmooth deployment. Finallya robust cooperation amongstthe various stake holders –from Union and StateGovernment, to banks to theinsurance companies isfurther required to ensurethat the third largest cropinsurance market after USAand China, builds andmaintains a successfulbusiness model. Such abusiness model shall build upthe farmer’s confidence ininsurers and will then notlimit itself to only cropinsurance. Rather, in the longterm, this shall then cascadeinto cross selling of variousother offerings from theinsurance companies, servingas a greater tool for farmersagainst any financialuncertainties they might face.

w

Large scale awarenessand education

programs on insuranceneed to be conducted atthe grassroots levels sothat the benefits seep

down to even smallscale and tenant

farmers. The infusion oftechnology, at all levels

of the schemeimplementation from

issuance to claimspayout is another pre-requisite to a smooth

deployment.

Views expressed in thispaper are author’s

personal only and not ofthe affiliatingorganisations

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1 . The article must be originalcontribution in the form ofessay, research paper or casestudy of the author.

2 . The article must be anexclusive contribution for theJournal and should not havebeen published elsewhere in thesame form.

3 . The article should ordinarilynot exceed 2000 words. Alonger article/research papermay also be considered if thesubject so warrants.

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7 . A brief write-up about theAuthor must also be sent.

8 . All the referred material in thearticle must be appropriatelycited. The authors are advisedto follow AmericanPsychological Association (APA)Style for referencing.

9 . All manuscripts shall be sent tothe Editor, InsuranceRegulatory and DevelopmentAuthority of India,Communication Wing, UIITowers, 9th Floor, Basheerbagh,Hyderabad 500029 along withelectronic mail to<journal@irda.gov.in> withthe subject line - Contributionto the Journal.

1 0 . Electronic version of thecontribution typed in MS Wordfile is essential for publication.

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1 2 . A honorarium of Rs. 2000/-would be given to each of thepublished articles.

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1 4 . The article shall beaccompanied by a‘ D e c l a r a t i o n - c u m -Undertaking’ from theauthor(s).

Declaration-cum-UndertakingTitle of the Article / Essay: ___________________________________

I/We (full name of author(s)) _________ hereby solemnly declare that the work presented in the article /essay/research paper ______________________________________________________________submitted by me/us for publication in the IRDAI Journal is:

1 . Not submitted to any other publications / or website at any point in time for publication

2 . An original and own work of the author (i.e. there is no plagiarism)

3 . No ideas, processes, results or words of other authors have been presented as author’s own work.

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Guidelines to the contributors of the Journal

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Policyholder Servicing Turn Around Times

Policy Service

Processing of Proposal and communication of decisionsincluding requirements/ issue of Policy/Cancellations 15 days

Issuing copy of proposal form 30 days

Response by the insurer on post policy issue service

related requests such as change in address/nomination/assignment of policy etc. 10 days

LIFE INSURANCE

Surrender value/Annuity/Pension processing 10 daysMaturity Claim/Survival Benefit/Death claim

without investigation 30 days

Raising claim requirements after lodging the claim 15 days

Death Claim Settlement / Repudiation with investigationrequirements 6 months

GENERAL INSURANCE

Appointment of Surveyor 3 daysSurvey Report Submission 30 days

Insurer seeking addendum report 15 days

Offer of settlement/rejection of claim after receiving first /

addendum survey report 30 days

GRIEVANCES

Acknowledging a Grievance 3 days

Resolving a Grievance 15 days

Maximum

Turn Around Time

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Some Important Insurance Related Websites

Insurance Related Links

1 Insurance Regulatory and Development www.irdai.gov.in

Authority of India (IRDAI)

2 IRDAI Consumer Education Website www.policyholder.gov.in

3 Insurance Information Bureau of India www.iib.gov.in

4 IRDAI Agency Licensing Portal www.irdaonline.org

5 Integrated Grievance Management System (IGMS) www.igms.irda.gov.in

6 Mobile Application to Compare ULIPs www.m.irda.gov.in

Insurance Education Institutions

1 Institute of Insurance and Risk Management (IIRM) www.iirmworld.org.in

2 Insurance Institute of India (III) www.insuranceinstituteofindia.com

3 Institute of Actuaries of India (IAI) www.actuariesindia.org

4 National Insurance Academy (NIA) www.niapune.com

International Links

1 International Association of Insurance Supervisors www.iaisweb.org

2 National Association of Insurance Commissioners www.naic.org

3 International Gateway for Financial Education www.financial-education.org

Other Links

1 Governing Body of Insurance Council (GBIC) www.gbic.co.in

2 General Insurance Council www.gicouncil.in

3 Life Insurance Council www.lifeinscouncil.org

4 Insurance Brokers Association of India (IBAI) www.ibai.org

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