REPORTONIMPACT EVALUATIONOFPILOT WEATHER BASED CROP INSURANCE STUDY (WBCIS)

Submitted to :Department of Agriculture and Cooperation
Ministry of Agriculture
Government of India
By :Agricultural Finance Corporation Ltd.
Head Office, Mumbai
JANUARY,2011

Evaluation of Pilot Weather Based Crop Insurance Scheme

CONTENTSEXECUTIVE SUMMARY ………………………………………………………………………………………………… 05

AGRICULTURAL RISK MANAGEMENT .............................................................................................................28
1.1
1.2
1.3
1.4
1.5
1.6

NEED FOR STABILIZING FARM INCOMES ........................................................................................................ 28
EVOLUTION OF AGRICULTURAL INSURANCE: TRENDS AND PROBLEMS ................................................................. 29
ALTERNATIVES TO CROP INSURANCE: INDEX-BASED WEATHER INSURANCE .......................................................... 34
AGRICULTURE INSURANCE SCHEMES IN INDIA ................................................................................................ 36
GLOBAL EXPERIENCES WITH CROP INSURANCE............................................................................................... 49
RELEVANT CASES OF PUBLIC-PRIVATE PARTNERSHIP IN CROP INSURANCE ........................................................... 56

NATIONAL AGRICULTURAL INSURANCE SCHEME (NAIS) /RASTRIYA KRISHI BIMA YOJANA (RKBY) ................59
2.1
OBJECTIVES ............................................................................................................................................. 59
2.2
BENEFITS EXPECTED FROM SCHEME ............................................................................................................. 59
2.3
SALIENT FEATURES OF THE SCHEME ............................................................................................................. 60
2.4
OPERATIONAL MODALITIES ........................................................................................................................ 65
2.5
GENERAL ............................................................................................................................................... 69
2.6
WORKING EXAMPLE OF SUM INSURED AND PREMIUM .................................................................................... 70
2.7
PERFORMANCE OF NAIS........................................................................................................................... 71
2.7.1 Coverage under NAIS ...................................................................................................................... 71
WEATHER INSURANCE IN INDIA.....................................................................................................................75
3.1

PILOT WEATHER BASED CROP INSURANCE SCHEME (WBCIS)........................................................................... 75

EVALUATION STUDY ON WBCIS .....................................................................................................................89
4.1
4.2
4.3
4.4
4.5
4.6
4.7
4.8

TERMS OF REFERENCE ............................................................................................................................... 89
UNIVERSE OF THE STUDY ......................................................................................................................... 90
SAMPLE SIZE FOR QUESTIONNAIRE SURVEY ............................................................................................ 90
PROFILE OF FARMER SAMPLE ............................................................................................................. 90
SAMPLING METHODOLOGY ............................................................................................................... 91
SELECTION OF OTHER STAKEHOLDERS ....................................................................................................... 92
STUDY TOOLS .......................................................................................................................................... 92
FIELD SURVEY, DATA COLLATION AND ANALYSIS ............................................................................................ 93

KEY FINDINGS FROM PRIMARY AND SECONDARY RESEARCH ........................................................................94
5.1
SUMMARY OF FINDINGS FROM PRIMARY RESEARCH WITH FARMERS ................................................................. 94
5.2
SUMMARY OF FINDINGS FROM INTERACTIONS WITH OTHER KEY STAKEHOLDERS .................................................. 96
5.3
KEY INPUTS FROM INTERACTIONS WITH INSURERS......................................................................................... 101
5.3.1 Key Inputs from Interaction with AIC ............................................................................................ 101
5.3.2 Key Inputs from Interaction with ICICI Lombard ........................................................................... 103
5.3.3 Key Inputs from Interaction with IFFCO-Tokio .............................................................................. 104
5.4
FINDINGS FROM SECONDARY DATA PROVIDED BY INSURERS ........................................................................... 105
5.4.1 Farmers Insured ............................................................................................................................ 105
5.4.2 Farmers Benefited ......................................................................................................................... 106
5.4.3 Area Insured (in Hectares)............................................................................................................. 107
5.4.4 Total Premiums (in Lakh INR)........................................................................................................ 107
5.4.5 Total Claims (in Lakh INR) ............................................................................................................. 108
5.4.6 Growth of Weather Insurance Portfolio of AIC and ICICI Lombard ............................................... 109

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5.4.7

Other Observations ....................................................................................................................... 110

EMERGING SCENARIO AND KEY CONCLUSIONS............................................................................................112
6.1
SCOPE OF WEATHER INSURANCE ............................................................................................................... 113
6.1.1 Perils Covered................................................................................................................................ 113
Indications from Farmer Beneficiaries of WBCIS........................................................................................ 113
6.1.2 Basis Risk....................................................................................................................................... 114
6.1.3 Design of Weather Insurance Products......................................................................................... 115
6.2
TRANSPARENCY IN WBCIS....................................................................................................................... 117
Indications from Farmer Beneficiaries on Reliability of Weather Stations................................................. 118
Indications from Other Key Stakeholders on Reliability of Weather Stations ............................................ 118
Analysis of Satisfaction Level for IMD & Private Weather Stations ........................................................... 118
Indications from Farmer Beneficiaries on Effectiveness of WBCIS against Manipulation.......................... 119
Indications from Other Key Stakeholders on Effectiveness of WBCIS against Manipulation ..................... 120
Indications from Farmer Beneficiaries on Explanation of WBCIS Policy..................................................... 120
Indications from Farmer Beneficiaries on Resolution of Queries regarding WBCIS ................................... 120
6.3
SETTLEMENT OF CLAIMS .......................................................................................................................... 121
Indications from Farmer Beneficiaries on Time Delay in Intimation or Receipt of Claims ..................... 121
Analysis of Delays in Claim Settlement ...................................................................................................... 122
6.4
BENEFITS OF WBCIS .............................................................................................................................. 123
Key Indicators of Weather Insurance Programme of BASIX....................................................................... 124
Growth of Weather Insurance Portfolio of AIC and ICICI Lombard (in Area Insured) ............................ 124
Indications from Farmer Beneficiaries on Effectiveness of WBCIS as a Protection Tool against Crop Losses& Climate Change Effects............................................... 125
Indications from Farmer Beneficiaries on Usefulness of WBCIS as an Alternative to NAIS ................... 126
Indications from Other Key Stakeholders on Usefulness of WBCIS as an Alternative to NAIS ............... 126
6.4.6 Claim Benefits of WBCIS................................................................................................................ 126
6.5
WEATHER STATION INFRASTRUCTURE FOR WBCIS ....................................................................................... 128
6.6
RELATIONSHIP BETWEEN NAIS AND WBCIS................................................................................................ 129
6.7
ANALYSIS AND CROSS-COMPARISON OF WBCIS PRODUCTS OFFERED BY INSURERS ............................................ 130
KEY SUGGESTIONS AND AGENDA FOR ACTION ............................................................................................132
7.1
SUGGESTIONS FROM KEY STAKEHOLDERS .................................................................................................... 132
7.1.1 Suggestions from Farmer Beneficiaries of WBCIS ......................................................................... 132
Main Suggestions for Improving Crop Insurance in India (Integrating Inputs from Farmers and OtherKey Stakeholders)..................................................................... 134
STRUCTURAL RECONFIGURATION OF INDIAN CROP INSURANCE MODEL .......................................................................... 134
UNIVERSAL COVERAGE OF FARMERS AND DEVELOPMENT OF MACRO-LEVEL INDICES (TALUKA-LEVEL WEATHER INDICES) FOR
CATASTROPHIC INSURANCE ................................................................................................................................... 136
RELATIONSHIP BETWEEN NAIS AND WBCIS AND NEED FOR BLENDED/COMPOSITE PRODUCTS .......................................... 138
COVERAGE OF PERILS UNDER WBCIS...................................................................................................................... 138
HYDRO-METEOROLOGICAL AND CROP LOSS DATABASE DEVELOPMENT ......................................................................... 139
IMPLEMENTATION OF INTEGRATED DATA SYSTEM IN INDIA .......................................................................................... 140
BENCHMARKING OF WEATHER INSURANCE PRODUCTS ............................................................................................... 142
IMPROVING FINANCIAL LITERACY AND TECHNICAL UNDERSTANDING OF FARMERS THROUGH CONTEXT-SPECIFIC, PROVEN
MEASURES ......................................................................................................................................................... 147
IMPROVEMENT IN SERVICE DELIVERY ASPECTS OF CROP INSURANCE.............................................................................. 147
PHYSICAL INDIVIDUAL/AREA ASSESSMENT OF NON-INDEXABLE/LOCALIZED (HAIL/FROST/WIND) LOSSES (UNDER BOTH WBCISAND NAIS) ........................................................................................................................................................ 148
MAKING CROP INSURANCE MANDATORY FOR CONTRACT FARMING INITIATIVES .............................................................. 149

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PROMOTING COLLECTIVE-BASED MODELS OF CROP INSURANCE ................................................................................... 149
LEGAL AND REGULATORY IMPROVEMENTS................................................................................................................ 151
OTHER SUGGESTIONS ........................................................................................................................................... 152
ANNEXURE 1: PRIMARY RESEARCH DATA OUTPUTS ....................................................................................154
ANNEXURE 2: MAIN POINTS FROM INTERACTIONS WITH EXPERTS AND INTERMEDIARIES..........................183
ANNEXURE 3: CASE STUDIES ........................................................................................................................191
ANNEXURE 4: LIST OF NGOS / MICRO FINANCE INSTITUTIONS (MFIS) / INSURANCE BROKERS / SAUS/SUBJECT MATTER EXPERTS COVERED DURING PRIMARY RESEARCH ..........................................................200
ANNEXURE 5: REPORT ON ADDITIONAL ASPECTS INDICATED BY ADDL. SECRETARY (CREDIT & COOP.),MINISTRY OF AGRICULTURE, GOI DURING THE MEETING HELD ON 23.6. 2010 ............................................202
I. COMPARATIVE ANALYSIS OF WEATHER INSURANCE PRODUCTS OF AIC AND ICICI LOMBARD ........................................ 202
Study of Rainfall Insurance Contracts implemented in Rajasthan during Kharif 2009.............................. 202
II. COMPARATIVE ANALYSIS OF IMD AND PRIVATE WEATHER STATIONS BASED ON PAYOUTS ............................................ 215
III. ESTABLISHMENT OF TECHNICAL SUPPORT UNIT FOR CROP INSURANCE IN THE MINISTRY OF AGRICULTURE AND ITS PROPOSEDSTRUCTURE ........................................................................................................................................................ 218

APPENDIX 1: SAMPLE RAINFALL INSURANCE CONTRACTS FOR RAJASTHAN DURING KHARIF 2009 ................................223
APPENDIX 2: HISTORICAL PAYOUTS FOR CONTRACTS IN APPENDIX 1 ............................................................................231

ANNEXURE 6: REFERENCES ..........................................................................................................................235

LIST OF CHARTSCHART 1: PRODUCT LIFE CYCLE CURVE ............................................................................................... 112
CHART 2: RESPONSE ON COVERAGE OF PERILS UNDER WBCIS ............................................................ 113
CHART 3: RESPONSE ON LOCATION OF W EATHER STATION ................................................................... 114
CHART 4: RESPONSE ON DESIGN OF WBCIS POLICY ............................................................................ 115
CHART 5: RESPONSE ON RELIABILITY OF W EATHER STATIONS ............................................................... 118
CHART 6: RESPONSE ON EFFECTIVENESS OF WBCIS AGAINST MANIPULATIONS ..................................... 119
CHART 7: RESPONSE ON EXPLANATION OF WBCIS POLICY AT TIME OF PURCHASE ................................ 120
CHART 8: RESPONSE ON RESOLUTION OF QUERIES REGARDING WBCIS................................................ 121
CHART 9: RESPONSE ON TIME DELAY IN INTIMATION OR RECEIPT OF WBCIS CLAIMS ............................. 122
CHART 10: KEY INDICATORS OF W EATHER INSURANCE PORTFOLIO FOR BASIX ....................................... 124
CHART 11: AREA INSURED BY TOP 2 INSURERS UNDER W EATHER INSURANCE (IN ACRES)........................ 125
CHART 12: RESPONSE ON USEFULNESS OF WBCIS AGAINST CROP LOSSES ........................................... 125

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CHART 13: RESPONSE ON USEFULNESS OF WBCIS AS AN ALTERNATIVE TO NAIS................................... 126
CHART 14: PERCENTAGE OF FARMERS BENEFITTED THROUGH CLAIMS UNDER WBCIS ............................ 127
CHART 15: CLAIMS RATIO UNDER WBCIS .............................................................................................. 127

LIST OF TABLESTABLE 1: PSSCI SCHEME COVERAGE....................................................................................................................... 40
TABLE 2: SALVAGE VALUE UNDER SEED INSURANCE SCHEME ...................................................................................... 43
TABLE 3: PREMIUM RATES UNDER SEED INSURANCE SCHEME ..................................................................................... 44
TABLE 4: FIIS - COVERAGE & EXPERIENCE ............................................................................................................... 45
TABLE 5: CROP-WISE AND STATE-WISE DISTRICTS SELECTED FOR FARM INCOME INSURANCE SCHEME .......................... 48
TABLE 6: FINANCIAL PERFORMANCE OF CROP INSURANCE PROGRAMMES IN SOME SELECT COUNTRIES ......................... 54
TABLE 7: PREMIUM RATES UNDER NAIS................................................................................................................... 61
TABLE 8: CUT –OFF DATES FIXED FOR NAIS............................................................................................................ 63
TABLE 9: NUMBER OF CROP CUTTING EXPERIMENTS TO BE CONDUCTED UNDER NAIS ................................................. 64
TABLE 10: MONTHLY CUT –OFF DATES FOR DECLARATIONS – SEASON WISE UNDER NAIS............................................. 68
TABLE 11: SUM INSURED LIMITS AND PREMIUM RATES FOR PADDY / RICE ..................................................................... 70
TABLE 12: SUM INSURED AND PREMIUM ..................................................................................................................... 70
TABLE 13: SUM INSURED, PREMIUM & SUBSIDY UNDER NAIS ...................................................................................... 71
TABLE 14: STATE WISE COVERAGE OF NATIONAL AGRICULTURAL INSURANCE SCHEME ................................................... 72
TABLE 15: GOVERNMENT’S DIRECT FINANCIAL SUPPORT TO NAIS ............................................................................... 74
TABLE 16: PREMIUM SLABS FOR WBCIS................................................................................................................... 79
TABLE 17: FARMERS INSURED UNDER WBCIS.......................................................................................................... 106
TABLE 18: FARMERS BENEFITED UNDER WBCIS ...................................................................................................... 106
TABLE 19: AREA INSURED UNDER WBCIS (IN HECTARE) ........................................................................................... 107
TABLE 20: TOTAL PREMIUM INCOME UNDER WBCIS (IN LAKH INR) ............................................................................ 108
TABLE 21: TOTAL CLAIMS MADE UNDER WBCIS(IN LAKH INR) .................................................................. 108

TABLE 22: W EATHER INSURANCE COVERAGE OF AIC & ICICI LOMBARD (IN ACRES) .................................................... 110
TABLE 23: DISTRIBUTION OF RESPONDENTS ‘NOT SATISFIED’ WITH TIME TAKEN FOR CLAIM SETTLEMENT ...................... 122

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EXECUTIVE SUMMARYAGRICULTURAL RISK MANAGEMENT
1.

Agriculture is an intrinsically risky economic activity. Given the exposure of crop
yields to a multitude of perils, risk management systems are imperative for stabilizing
crop incomes through reduction of seasonal and inter-annual variability.

2.

The vulnerability of resource-poor farmers and landless agricultural labourers isaggravated by the preponderance of uninsured risks in conditions where
opportunities for full-insurance are absent. Agricultural shocks are further amplified inrural areas where the markets for land, labour and credit are inter-locked.

3.

Crop insurance is based on the fundamental principle of insurance business, that is,the ‘laws of large numbers’. The risk is distributed across space and time. Crop
insurance brings in security and stability in farm income. Crop insurance protects
farmers’ investment in crop production and thus improves their risk bearing capacity.
Crop insurance facilitates adoption of improved technologies and, encourages higher
investment resulting in higher agricultural production.

4.

In India, J S Chakravarti designed, as early as in 1920, a scheme of agricultural
insurance based on rainfall for India. According to Chakravarti (1920, referred to in
Mishra 1995), agricultural insurance in India should be a package consisting of thefollowing, in increasing order of priority as per conditions prevailing during the times:(i) Insurance of buildings, granaries and agricultural implements (ii) Cattle insurance(iii) Insurance of crops.

5.

There are huge coverage gaps in terms of farmers benefitted and crops being
covered under the state-sponsored and heavily subsidized National Agricultural
Insurance Scheme (NAIS), a multi-peril, area based crop insurance scheme that is
mandatory for loanee farmers. The alternative index-based weather insurance
products (micro insurance products) that were developed to overcome the defects inthe traditional crop insurance schemes could address the problems of moral hazard,
high administrative costs, long delays in settlement, low verifiability etc.

6.

Crop Insurance is based either on the ‘area approach’ or ‘individual approach’. The
area approach is based on ‘defined areas’ which could be a block / mandal/ hobli/
firka or any other smaller contiguous area. The actual average yield per hectare of
the notified crop for the defined area is determined on the basis of Crop Cutting
Experiments (CCEs).

7.

Public crop insurance programmes have been around for several decades in US,
Japan, Brazil, Sri Lanka, Mauritius, and Mexico. There are important similarities inthe evolution of crop insurance in the United States, Brazil, and Japan. The three
have gone through considerable adjustments, learning from their own experiences.
After heavy losses in the beginning years, programme administrators haveintroduced new rules, including higher premium rates. Subsidies have been essential,
and they are provided by the government on the grounds of broad social objectives.

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8.

In summary, the financial experience with publicly-provided, multiple-peril crop
insurance has been disastrous. These programmes were heavily subsidized andgovernments not only paid part of the premium, but also most of the delivery andservice costs, and they covered aggregate losses even when the losses exceed
targeted levels over long periods of time. Until now, most agricultural insurance
programmes in the world have not been able to fully cover their own indemnity
payments (I) and administrative costs (A) with the collected premiums (P).

NATIONAL AGRICULTURAL INSURANCE SCHEME (NAIS) /RASTRIYA KRISHI BIMA YOJANA (RKBY)
9.

The crop insurance scheme currently being implemented in India is the National
Agricultural Insurance Scheme (NAIS) which started from Rabi 1999-2000 season.
AICIL or the Agriculture Insurance Company of India Ltd. has taken over the
implementation of National Agricultural Insurance Scheme which until the financial
year of 2002-03 was implemented by the General Insurance Corporation of India.

10. Salient Features of the Scheme: The crops in the following broad groups in respect ofwhich (a) the past yield data based on Crop Cutting Experiments (CCEs) is available
for adequate number of years and (b) requisite number of CCEs are conducted for
estimating the yield during the proposed season. The Scheme extends to all States
and Union Territories. The States / UTs which are opting for the Scheme would be
required to take up all the crops identified for coverage in a given year. The States /Union Territories once opting for the Scheme will have to continue for a minimum
period of three years. All farmers including sharecroppers and tenant farmers
growing the notified crops in the notified areas are eligible for coverage.
Comprehensive risk insurance will be provided to cover yield losses due to nonpreventable risks.
11. The Scheme would operate on the basis of ‘Area Approach’ i.e., Defined Areas foreach notified crop for widespread calamities and on an individual basis for localized
calamities such as hailstorm, landslide, cyclone and flood. The Defined Area (i.e.,unit area of insurance) may be a Gram Panchayat, Mandal, Hobli, Circle, Firka,
Block, Taluka etc. to be decided by the State / UT Govt. However, each participating
State / UT Government is required to reach the level of Gram Panchayat as the unit
in a maximum period of three years.
12. The crop yield insurance scheme has been largely unsuccessful with low coverageand high claims to premium ratio. There are problems with both the design andimplementation of the schemes. Crop Insurance to be a meaningful policy risk
management tool, would have to reach out to a majority of farmers. At present only
about 15 percent of farmers and 17 percent of cropped area.

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WEATHER INSURANCE IN INDIA
13. Weather risk is the most significant agricultural production risks although risks ofother inputs such as soil, seeds, fertilizers, and management practices contribute toyield volatility. Under these circumstances, a financial innovation in the form ofweather insurance was introduced in the year 2003 as the ‘index-based rainfall
insurance’. Since then the weather insurance products have promised to overcome
the limitations of the traditional crop insurance scheme and ‘weather proof’ theincome streams of millions of agricultural households.
14. From Kharif 2007 season, a Weather Based Crop Insurance Scheme (WBCIS) has
been piloted across India to explore the effectiveness of Weather Based Crop
Insurance as an alternative to the National Agricultural Insurance Scheme (NAIS).
15. The Scheme shall operate on the principle of “Area Approach” in selected notified
Reference Unit Areas. Area Approach signifies that a “Reference Unit Area” shall beconsidered as a Unit-Area of Insurance for the purpose of acceptance of risk andassessment of compensation as well. Therefore, all insured-cultivators of a Notified
Crop in the notified Reference Unit Area shall be deemed to be on par so far as theirterms of insurance coverage and assessment of compensation are concerned.
16. All the cultivators (including sharecroppers and tenant cultivators) growing any
Notified Crop in any Reference Unit Area shall be eligible for coverage. Financial
Institution, for the purpose of the Scheme, includes all District Central Cooperative
Banks and also the PACS affiliated to them, all Commercial Banks and all Regional
Rural Banks, as defined in National Agriculture Insurance Scheme (NAIS).
EVALUATION STUDY ON WBCIS
17. The Government of India, Ministry of Agriculture, and Department of Agriculture &Cooperation have felt the need to evaluate the performance of the WBCIS scheme
and its impact on the farmers in order to facilitate policy decision in the matter withregard to its continuance or otherwise as a regular on-going plan scheme.
18. The study was conducted in 2 Districts each of 4 States [2 States each from Kharif
and Rabi] where the Pilot Weather Based Crop Insurance Scheme (WBCIS) was
implemented. For the purpose of meaningful evaluation, comparison & analysis,
another 2 States [1 State each from Kharif and Rabi] were selected where the Pilot
WBCIS was not implemented, to provide some counterfactuals (though limited given
the non-randomized nature of generating these counterfactuals) by serving as aControl Group. In order to capture the situation of states which have WBCIS partiallyor have witnessed other initiatives in Weather Based Crop Insurance, 2 States wereadded to the universe of the study.
19. A sample of 1000 farmers who availed Weather Insurance in the implementing states
constitutes the beneficiary group for the field survey. Further, 200 non-beneficiary
farmers (i.e. those who did not avail of the Weather Insurance Scheme in the both

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implementing and non-implementing states) were chosen, to serve as a Control
Group for comparative analysis and purposive evaluation. In addition, 75 farmers[from 2 selected states where the Weather Based Crop Insurance Scheme (WBCIS)
was not implemented but there have been some pilots related to weather insurance]
were selected for detailed interaction to ascertain their views on the scheme and its
prospects, if implemented.
20. The sampling procedure ensured the respondent farmers were a representative
group comprising of beneficiary and non-beneficiary farmers drawn from various
socio-economic categories [SC/ST/OBC/Women/General, Small Farmer/Marginal
Farmer/Large Farmer etc.].
21. Other key stakeholders like Banks, Insurance Intermediaries and Technical Agencies
were included for obtaining relevant inputs on WBCIS and its comparison with NAIS.
These stakeholders included MFIs/NGOs, insurance brokers, subject matter experts,
agricultural specialists from state agricultural universities and IARI.
22. The Study was designed to start with preliminary discussions with the officials of
Department of Agriculture in the Ministry of Agriculture, GoI and with the Agriculture
Ministries in the sampled states. Relevant information from the State Government
and implementing insurance companies was obtained on financial and physicalperformance of the Weather Insurance Scheme (crop-wise, season-wise and yearwise). Besides, the constraints and problems faced in the implementation process
were collected and collated through check-lists and interview guides.
23. The field survey was carried out by trained research associates and supervisors
under the close guidance of the Core Team. The primary and secondary datacollected from the field as well as different stakeholders was collated and analyzed
using statistical analytical tools and inferences drawn thereon.
EMERGING SCENARIO AND KEY CONCLUSIONS
24. The multi-pronged and detailed field research for evaluation of WBCIS has thrown up
a variety of perceptions, experiences, judgments, projections and perspectives thathave enriched the evaluation exercise. Both the supply and demand sides for thisproduct are evolving with the supply side currently being at a higher level ofunderstanding and sophistication compared to the demand side.
Scope of Weather Insurance in terms of Perils Covered
25. Increasing the number of perils in a WBCIS is not a constraint for insurers as they
have demonstrated in case of policies for horticultural crops which require risk
coverage for more complex weather events and other weather parameters in addition
to rainfall and temperature. As is the usual case elsewhere, the quality of coverage
under a peril is more important than the number of perils being covered. The issue ofAgricultural Finance Corporation

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insufficient coverage of perils has not come out as a significant concern during
interactions with other key stakeholders.
Scope of Weather Insurance in terms of Basis Risk
26. All stakeholders in weather insurance irrespective of whether they represent the
supply side, the demand side, or the non-transactional side, unanimously support theminimization of basis risk through a well-planned network of weather stations. The
critical need to minimize basis risk by proper coverage of weather stations isunderlined by the fact that more than three-fourth (77%) of respondents from thefarmer beneficiary sample are not satisfied with location of the weather station.
27. The location of weather station has the greatest bearing on the basis risk in aweather insurance contract once the key parameters of the contract have been set.
The location of weather insurance has the potential of turning weather insurance basically a loss compensation instrument, into a lottery. On the question of whatshould be the ideal radius for coverage of rainfall, the responses have been wideranging, from 25 km to 5 km. These numbers can be regarded as guesstimates,
moderated by the pulls of demand and constrained by the limitations of supply.
However there seems to be a growing unison among key stakeholders that coverage
under rainfall insurance should not be offered to a farm located at a radial distance ofmore than 10 km from a weather station. Starting from this heuristic value, there
should be efforts to systematically bring this down to 5 km within a couple of years
through better planning and guided enforcement. Since both Kharif and Rabi seasons
would essentially use the same infrastructure for weather data, the heuristic radial
distance for rainfall insurance would, in turn, become the guiding value for weather
insurance based on temperature indices.
28. Basis risk in weather insurance is not only inherent in the location of reference
weather station but it is also a function of the design of the WBCIS product. The
specialized nature of product development, the esoteric terminology used in a term
sheet, and the concoction of agro-meteorology, statistics and economics within theunderlying parameters have the undesirable effect of turning weather insurance into
an incomprehensible device.
Scope of Weather Insurance in terms of Design of Product29. The design of weather insurance product is similar to a black box which has weather
data as an input and a term-sheet as an output, continues to remain a mystery evenfor seasoned personnel dealing in sales and marketing of weather insurance. Due tothe typical structure of a weather insurance term sheet, farmer customers may notfind it easy to unravel the technicalities in the design of weather insurance. The taskof appraising and approving the designs of weather insurance products may hence
be entrusted to the regulatory agencies, designated expert committees, and entities
in the non-transactional space working to ensure a fair transaction between the

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supply side and the demand side of weather insurance. Owing to the practicallylimitless number of designs possible for weather insurance, the task of appraising adiverse portfolio of weather insurance designs and their contextual suitability is a
specialized task that unfortunately has not been able to attract the level of attention
and technical rigor which it truly deserves. The challenges in comprehensively
evaluating weather insurance products are compounded by the fact that weather
insurance products lie at a crossover of agriculture, statistics, meteorology, andfinancial risk management, each of which is a specialized field of knowledge withlimited expertise available. Therefore the task of identifying resource persons withgood understanding of more than one or two of the above fields is quite challenging
and requires substantial efforts to bring such rare expertise on board.
30. For a common man, the simplest evidence of a good weather insurance design is its
claim payout during seasons which are devastating on a widespread level. Over the
first five seasons of its WBCIS experience, AIC has been able to provide claim
benefit to nearly 62% (cumulative across all seasons) of the farmers insured by it.
The overall claims ratio of AIC for the six seasons of its WBCIS coverage is nearly
77% which indicates that out of every 100 rupees of premium received by it, it has
paid out an average of 77 rupees as claims to the insured farmers. The percentage of
farmers benefitted during the first four seasons of WBCIS coverage by ICICI
Lombard is 41% while its overall claim ratio during the five seasons of its WBCIS
participation has been 60%.
31. One of the common criticisms of weather insurance has been its limitation of
insignificantly compensating the insured farmers for even the worst of crop seasons(e.g. Kharif 2009). For the less prudent farmer customers, the time lag to sense thislimitation of weather insurance may be slightly higher, 3 to 5 years by a conservative
measure. Such a situation for even an apparently well-designed weather insurance
contract may be the result of the inadequacy in any or both of following parametersnamely, the maximum sum assured, and the maximum probable loss. The term ‘total
sum assured’ in weather insurance contract may be deemed anomalous in the sensethat even when a farmer has lost the entire crop during a particular stage, thecompensation accruing to that farmer under the policy may not be the maximum sum
assured under the weather insurance contract. It will be rather simply the addition of
the sum assured of the weather insurance covers operative during that stage. The
consequent indemnity may only be a fraction of the maximum sum assured under theweather insurance policy bought by the farmer. The maximum probable loss denotes
the highest cumulative payout (sum of payouts of all constituent covers) among all
the cumulative payouts simulated historically from a weather insurance contract. The
quantitative difference between maximum sum assured and maximum probable loss
for a weather insurance contract represents a theoretical gap between the maximum
payout committed by that contract and the actual payout that could be expected fromthat contract even in considerably adverse years.
32. Insurers have expressed commitment for design of weather insurance products which
fulfill the expectations of farmers and other key stakeholders. All the insurers are

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working with at least one specialized external agency for technical support on productdevelopment. AIC and ICICI Lombard both have undertaken efforts in the past to tap
the expertise of IARI - India’s leading research institution in agriculture. Despite theapparent synergies, the desired outcomes could not be achieved because of
mismatch in resource requirements (time, investment in research, manpower etc)
required to make a significant breakthrough. The limited size of weather insurance
portfolio, reasonable uncertainties regarding its scalability and high claim ratios seemto be the key deterrents for insurers to sustain investments in weather insurance
product research and development with a long-term view.
Overall Effectiveness of Weather Insurance vis-à-vis National Agricultural
Insurance Scheme (NAIS)
33. Most key stakeholders with a fair understanding of both WBCIS and NAIS haveacknowledged the fact that both have some unique advantages and uniquelimitations. Since neither of these two types of crop insurance can singularly address
the diverse production risk management of Indian farmers, it would be better to allow
both of them as independent, standalone types or in a complementary manner.
34. NAIS is a crop insurance which could not translate into conceptual appeal andstructured design into fair, widespread, and sustainable value for its designatedbeneficiaries. As the largest crop insurance programme in the world, it has its shareof unique advantages like comprehensive coverage, physical assessment and low
physical infrastructure requirements.
35. Weather insurance is another type of crop insurance which was borne out of the needfor objective, transparent, prompt and administratively-simple claim settlement.
36. The hopes of the stakeholders in the crop insurance space have now shifted toremote-sensing technologies which is witnessing rapid advancement. Till the time
remote-sensing technology becomes so reliable and cost-effective that it can beutilized for loss assessment of existing insurance units, the crop insurance sector in
India will go through a transitional phase wherein NAIS and WBCIS can play the roleof either complements or alternatives, but not substitutes. The application of remotesensing for crop yield/loss estimation has shown the promise of rectifying the big
malaise of NAIS, which is its loss assessment procedure: sub-optimal, unwieldy anderror/risk-prone. Therefore the growth curve of remote-sensing applications will
determine the future path of crop insurance in India.
Weather Insurance and Weather Station Density in the Context of Minimizing
Basis Risk
37. From field research and pilots, the location of the weather station for claim settlement
has come out as the most important factor for farmer-clients to believe weather
insurance as trustworthy. The location of weather station has the greatest bearing on
the basis risk in a weather insurance contract once the key parameters of theAgricultural Finance Corporation

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contract have been set. The location of weather insurance has the potential of turning
weather insurance - basically a loss compensation instrument, into a lottery.
38. On the question of what should be the ideal radius for coverage of rainfall, theresponses have been wide-ranging, from 25 km to 5 km. These numbers can be
regarded as guesstimates, moderated by the pulls of demand and constrained by thelimitations of supply. However there seems to be a growing unison among key
stakeholders that coverage under rainfall insurance should not be offered to a farm
located at a radial distance of more than 10 km from a weather station. Starting fromthis heuristic value, there should be efforts to systematically bring this down to 5 km
within a couple of years through better planning and guided enforcement.
39. The density of AWS and IMD observatories holds the key to better pricing of risk
products with passage of time and enabling the introduction of weather insurance
based on other parameters. Warehouse of daily rainfall data for weather insurance isalso very important for disaster management as well as weather advisory service.
40. The requirements for a high-density weather station network are not uniform acrossthe country. These are influenced considerably by the exposure of crop yields toweather-borne risks, presence of microclimates, spatial distribution of landholdings
and demand for crop insurance.
41. The growth momentum in demand for weather insurance, triggered by WBCIS, has
actuated the quest for achieving international standards in weather data services.
IMD, the fountainhead of technical expertise on meteorology, has also responded
keenly to the demand for new weather stations which work on state-of-the-art
technology. During the period 2008-2010, IMD planned to set up more than 500 new
automatic weather stations (including automatic rain gauges). ISRO, the apex
institution in India for space research, has collaborated with IMD for installation ofanother 1000 AWS across India.
42. Timeliness of claim settlement is an inherent strength of WBCIS as weather data is
the only external input required for computation of claims under WBCIS. The concern
among farmer beneficiaries regarding the timeliness of claim intimation/settlement
under WBCIS may be regarded as moderate. This concern is likely to be addressed
automatically with the implementation of a proper weather data management system
in India as most of the delays arise from non-availability of timely weather data.
Reliability & Accuracy of Weather Data (particularly from Private, Third Party
Data Providers)
43. The low level of concern among farmer beneficiaries regarding the reliability of
weather stations or manipulation of claim inputs/results lends credence to thetransparency and reliability of WBCIS.
44. WBCIS has been successful in galvanizing insurers to look beyond the existing
network of weather stations and work out pragmatic ways to meet the weather
insurance demand, from wherever it had been emanating. The receptivity and theAgricultural Finance Corporation

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problem-solving orientation of insurers has been successful in spawning a whole new
business class of third-party, private weather data providers.
45. At the same time, the responsiveness, timeliness and representativeness of bothweather data and the service providers have improved substantially enabling
insurers, in many cases, to periodically track claim status and to compute interim
claims much before the date of completion of risk coverage under WBCIS. The
quality of the weather data for claim settlement is no more an issue of concern for the
insurers and the insured, even when third-party, private weather data providers comeinto the picture.
46. Services of Private, Third-Party Weather Data Providers are critical for authentic andtimely settlement of claims in many areas and in situations where no other alternative
is available. Due to the high flexibility of location, proven reliability and timeliness ofdata supply by third-party weather stations, they are gaining increasing ground for
Weather Insurance pilots. A few farmers, nevertheless, do entertain the suspicion
that the data provided by private / third-party data providers may not be accurate.
Scope for Improving Weather Data System, Data Collection and Data
Availability
47. The current growth of weather station network in India is largely haphazard anddevoid of a coordinated approach and integrated planning. Since most of the ongoinggrowth is driven by expansion in outreach and penetration of WBCIS, it would be in
the interest of concerned agencies to take up this issue and work out ways toaddress it through the involvement and keen participation of key stakeholders.
48. Weather infrastructure should be enhanced on high priority as any investment
towards generation of weather data should be looked upon as an investment in public
good with substantial payoffs in terms of ability to design more robust products inmedium-long run. In the short term, it will substantially benefit the credibility of
WBCIS due to more representative weather stations.
49. Crop loss data are the basis for the development of vulnerability functions to estimate
overall risk leading to indemnification. Therefore, crop loss data have to be gathered
in a more systematic manner, that losses be recorded by peril and at the highest
level of resolution possible. Having high resolution loss data would first improve therobustness of the vulnerability functions as the correlation between weather hazard
and crop loss would be more spatially representative. Crop loss information (atindividual or at an aggregate level) may be fed in by the afflicted farmer customers
through the toll-free phone service which can be stored in the centralized loss
database. The validation of such losses may be undertaken both through physicalverifications and juxtaposition with the corresponding weather or yield data.
50. Government should promote the development of a centralized data centre for WBCIS
and other requirements in agricultural extension, research and development.

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Weather data from such centralized data centre should be priced reasonably to givethrust to product development and research in weather insurance.
Weather Insurance as a Substitute for NAIS
51. On the important question of whether doing away with NAIS makes sense, theunanimous response has been in the negative. Comprehensive coverage of losses is
the key advantage of NAIS which in case of WBCIS is its faster claim settlement.
52. Right from its successful pilot in 2003, weather insurance was looked upon as a likely
successor to NAIS. With the passage of time, the innate limitations of weather
insurance have surfaced and have raised serious questions about its ability toreplace area yield insurance. Even the naysayers of NAIS have started to realize thatweather insurance can be complementary to area-yield insurance, rather than acting
as its substitute.
Weather Insurance and NAIS as Alternatives
53. On the question of offering NAIS and WBCIS as alternatives for the farmer-client tochoose, there was no clear view. Though some experts who trust the discrimination
ability of farmers, were in favour of giving farmers the option to choose their crop
insurance type, there were an equal number of practitioners who felt that such an
option will inevitably engender confusion, dissatisfaction and mistrust among farmers.
Since there are bound to be mismatch in payouts for the same location under the two
dissimilar crop insurance schemes, farmers may unintentionally or deliberately exploit
this mismatch to demand parity with the more beneficial outcome. It would be chaotic
for farmers and implementing agencies to deal with both co-existing. The major
problems can be:(i)Wide variations in premiums by way of the concessional subsidy have alreadycreated problems in farmers adopting insurance. While premium subsidy for
NAIS is being gradually reduced, WBCIS is being supported by a considerable
proportion of subsidy(ii) Another important difference between the two schemes making the choiceimpractical is the difference in the payout frequencies and magnitudes of WBCIS
and NAIS
54. In case of unequal payouts from NAIS and WBCIS, either the farmer-clients will press
for equal payouts on the pretext of insufficient awareness and understanding or willturn away from crop insurance with dissatisfaction citing discrepancies/contradictions.
The political economy of crucial support from farmers may cause a point ofcontention or rift between the State and the Centre if farmer dissatisfaction gains
sufficient magnitude. Even when farmers will find one type of crop insurance better
over the other, it will solely be on the narrow-minded basis of benefits received bythem during their period of experience, rather than on the inherent strengths of thegiven insurance type. Therefore for a technically intricate concept like crop insurance,

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the aim of achieving a minimum threshold of awareness and understanding among
farmers should be held paramount before empowering farmers with the option ofchoosing between different types of a social good like crop insurance.
Integrating Weather Insurance with NAIS
55. After examining the potential pitfalls in offering farmers the option of choosing
between NAIS and WBCIS, the possibility of integrating WBCIS with NAIS appears tomake better sense with the inputs from various stakeholder groups. All key
stakeholders, other than farmers, have acknowledged the unique advantages of
WBCIS and NAIS and have supported the continuation of both types till a more
optimal type of crop insurance is found. The viewpoints of the various key
stakeholders, particularly those with a direct or indirect stake in the outcomes fromcrop insurance, have been in favour of the crop insurance scheme which has
demonstrated better payout ability (more/bigger/widespread payouts) during theirexperience. Despite this natural preference, most of these stakeholders have also
been realistic enough to admit that any one type of crop insurance, either WBCIS or
NAIS, is incapable of meeting the expectations of the farmers and the larger
community of stakeholders. Therefore, the possibility of integrating WBCIS with NAIS
is more plausible in the current scenario.
56. One of the more workable alternatives could be to break-up the total sum assured
under crop insurance into two equal components: the first component (50%) will besettled on the basis of weather-based index whereas the second component (50%)
will be settled on the basis of area yields. The component of crop insurance requiringarea yields can take estimates of area yield from both CCEs and Remote-Sensing(RS) technology. The dependence on the estimates from RS technology can be
gradually increased with improvement in its resolution and accuracy.
Analysis of Weather Insurance Products and Benchmarking Standards
57. The involvement of designated agencies of the State Government in theadministration of WBCIS has led to the standardization of WBCIS products,
especially after one or two seasons of WBCIS experience in a given state. Thoughstandardization of WBCIS products being offered by various insurers in a particularstate is desirable for minimizing information asymmetries and simplifying productcommunication, it creates disincentives for insurers to undertake improvements in
product development for reducing basis risk. Basis risk in weather insurance is not
only inherent in the location of reference weather station but it is also a function of thedesign of the WBCIS product.
58. The specialized nature of product development, the esoteric terminology used in aterm sheet, and the concoction of agro-meteorology, statistics and economics within
the underlying parameters have the undesirable effect of turning weather insurance
into an incomprehensible device.

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59. The fundamental designs being employed for capturing production risks arising fromadverse weather events can be regarded incongruous with the peril/event being
insured through them. A number of glaring contradictions in definition of weather
perils abound in the WBCIS products which require a much critical evaluation before
being offered under WBCIS.
60. By their very nature, weather insurance products are difficult to comprehend for atypical Indian farmer who is equipped with limited capacities and experience. The
multitude of weather insurance products offered by various weather insurance
providers necessitates the need for benchmarking the various products to enable thefarmer to make an informed choice. Through benchmarking it may be ascertained
whether the products offered by the different insurance companies carry at least
comparable benefits (Protection vis-à-vis Premium). The complex weather insurance
products may be disintegrated into the constituent covers for different perils.
Impact and Usefulness of Private Sector Participation
61. Despite the presence of only three active insurers, the competitive landscape under
WBCIS is not placid or less fiercely contested than it would, if there were higher
number of competing insurers. With due regards to the inherent strengths of eachother, both AIC and ICICI Lombard have indicated loopholes and weakness in theinstitutional design and process control of WBCIS as an enabling factor behind thequantum leap of their main competitor in terms of key business parameters under
WBCIS. Even disregarding the innuendoes of the leading insurers, it is evident thatthe relatively flexible stipulations related to underwriting and process evaluation
under WBCIS, need to be reviewed rigorously and tightened, if required.
62. While AIC has drawn rave reviews from its channel partners (both WBCIS and NonWBCIS portfolio) by virtue of its open and empowering approach, particularly during
weather insurance product development; ICICI Lombard has demonstrated excellent
responsiveness and transparency in sharing all vital data, related to its businessstatistics. This tops up the remarkable initiatives undertaken by ICICI Lombard for
reducing inefficiencies due to intermediation and streamlining distribution. Some of
the measures introduced by ICICI Lombard for improving customer-centric processes
like enrollment, distribution, communication etc. and ensuring better operational
control may be relevant for replication under the umbrella programme of WBCIS.
Despite business statistics for WBCIS not strongly supporting its cause, IFFCO Tokiodisplayed admirable clarity on how it intends to scale up its WBCIS coverage and the
crucial groundwork necessary for it before its quest for scaling up its weather
insurance portfolio. The understanding and commitment of IFFCO Tokio regarding
weather insurance could be regarded to be in good harmony with the basic goals andorientation of WBCIS.

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Issues in Scaling-up Weather Based Crop Insurance Scheme
63. The key findings from the primary data collected from farmer beneficiaries of WBCIS
are summarized with the help of the following spider chart. This chart indicates thecorresponding percentage of respondents of a given category who are not satisfied
on various aspects (16 in our case) related to WBCIS. The categories of respondents
have been taken as overall sample, small and marginal farmers, non-loanee farmers,
and graduate farmers. These categories can be expected to represent a judicious
balance of preferred farmers, demanding farmers and informed farmers.

Based on the spider chart above, the aspects of WBCIS with the maximum ‘notsatisfied’ respondents are indicated below. The values in parentheses denote thepercentage of ‘not satisfied’ respondents averaged across the four categories:a)

Location of Weather Station (80.8)b)Mechanisms for Grievance Redress (56.5)c)

Convenience in Enrollment (56.5)

d)Resolution of Queries (53.3)

e)Responsiveness of Intermediary (45.3)

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Considering the above five aspects with the highest level of ‘not satisfied’
respondents, it can be easily seen that the first aspect deals with basis risk while theremaining four aspects deal with service delivery and convenience.
Referring again to above spider chart, the aspects of WBCIS with the minimum ‘notsatisfied’ respondents are indicated below. The values in parentheses denote thepercentage of ‘not satisfied’ respondents averaged across the four categories:a) Reliability of Weather Data (16.8)b) Protection Tool against Crop Losses and Climate Change (17.3)c) Effective against Political Risk and Manipulation (19.3)d) Weather as Basis for Crop Insurance (20.8)e) Usefulness as Alternative to NAIS (25.0)
Considering the above five aspects with the lowest level of ‘not satisfied’
respondents, it can be easily seen that the first and third aspects deal withtransparency and reliability of WBCIS, the second and fourth aspect deal with theprotection ability of weather insurance while the fifth aspects deals with its usefulness
as an alternative to NAIS.
For the sake of completeness, it would be helpful to know the aspects of WBCIS thatlie at the middle of the spectrum of ‘not satisfied’ respondents. Out of the 16 aspects
of WBCIS on which satisfaction of farmer beneficiaries are sought, the following six
define the mid-range responses. The values in parentheses denote the percentage of‘not satisfied’ respondents averaged across the four categories:a) Quantum of Sum Assured (44.3)
b) Explanation on WBCIS Policy (37.3)c) Time Delay in Claim Settlement (33.8)d) Period of Risk Coverage (29.8)e) Types of Risks Covered (26.8)f) Design of WBCIS Policy (25.3)
Efficacy & Effectiveness of WBCIS
64. Before the advent of WBCIS, weather insurance was a promising risk management
tool that had created enough buzz to remain a talking point in India for many years tocome. However the long-term customer appeal of weather insurance was dicey as
farmers could not see a sustainable value proposition in regularly buying this useful
but costly risk management instrument. It was during the budget of FY 2007-08 that

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Hon’ble Union Finance Minister laid the foundations for WBCIS by announcing anannual subsidy of INR 100 Crore. Private sector participation was incorporated into
the Pilot WBCIS from Rabi 2007-08 onwards.
65. The interest of farmers in weather insurance got reinvigorated as a result of theaffordable pricing of policies offered under WBCIS. Ever since then, weather
insurance has been going from strength to strength in India. WBCIS has transformed
the domain of agricultural risk management in India. The total coverage of weather
insurance which was languishing at less than 4 lakh acres before 2007-08 suddenly
received a boost from WBCIS enabling it to exceed 25 lakh acres in 2007-08. During
the 2009-10 marketing year, cumulative weather insurance coverage for AICIL and
ICICI Lombard has crossed 85 lakh acres, which is more than six (6) times the total
coverage of slightly less than 15 lakh acres during 2008-09.
Legal and Regulatory Environment for Index Insurance
66. Agriculture insurance is specialty insurance, and different from traditional generalinsurance in many respects. As an illustration, agriculture insurance, particularly crop
insurance programme is conceived as a ‘multiple-agency’ approach in which Rural
Financial Institutions (RFIs), State government, Central government etc. are actively
involved, with the government providing significant financial support. Moreover the
programme is compulsory for loanee farmers. The programme, thus is seen more asa social instrument of the government rather than a commercial instrument. A
programme of this nature and magnitude is unlikely to be effectively administered
unless backed by a statute or law. It may be worthwhile to note that the countries like
United States of America, Canada, Spain, Japan, Philippines etc. where crop
insurance is being used as an integral part of ‘agriculture risk management’ aseparate act / enactment is in force, and facilitating smooth implementation of the
programme.
67. Premium subsidy from government payable to ICICI Lombard under WBCIS is routed
through AIC. This circuitous route through a competing insurance company can be
obviated to streamline the funds flow and minimize transaction costs for concerned
parties. Timely receipt of premium subsidy is not only necessary for timely claim
settlement but also for meeting the regulatory requirements under Section 64 VB ofthe Insurance Act, 1938.
68. Government support in the form of premium subsidy is indispensable for WBCIS toattain widespread penetration in India. The caps on actuarial premiums and premiumsubsidy have to be hiked up for providing satisfactory coverage to cultivators in areas
prone to high and frequent risk exposure.
69. In order to further bring down the cost of crop insurance for farmers, WBCIS policies
should be exempted from service tax. Government has already waived service tax formany policies focused at rural and poor segments of the society.

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KEY SUGGESTIONS AND AGENDA FOR ACTION
70. The key suggestions for improvement of crop insurance as given by the farmer
beneficiaries of WBCIS are summarized with the help of the following spider chart.
During the questionnaire survey, the beneficiary farmers were asked to indicate theirthree most important suggestions for improvement of crop insurance. The ranking of
the suggestions should help in prioritizing the efforts for dovetailing crop insurancewith the expectations of the farmers.

The preceding spider chart indicates the corresponding percentage of farmer
beneficiaries who have rooted for a particularly suggestion and placed it among their
top 3 suggestions for improvement of crop insurance in India. The respondents havenot been segregated on the basis of any characteristic variable. The percentages on
the spider chart represent the percentage of respondents from the entire sample ofbeneficiary farmers of WBCIS from participating states. The aggregate percentage of
respondents who have rooted for each suggestion, irrespective of its position within
the top 3 ranks, can also be taken as a representative measure of its significancewithin the wide list of suggestions put forth by farmer beneficiaries of WBCIS.
From the spider chart above, the 3 suggestions that gained the highest share of
responses for the first rank are:a) Better Location of Weather Station (32%)b) Better Design of WBCIS Products (26%)c) Greater Awareness & Understanding of Crop Insurance Working (11%)

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Similarly the 3 suggestions that gained the highest share of responses for the second
rank are:a) Greater Awareness & Understanding of Crop Insurance Working (24%)b) Coverage of More Weather Parameters or More Perils (17%)c) Better Design of WBCIS Products (11%)
The 3 suggestions that received the highest share of responses for the third rank are:a) Greater Awareness & Understanding of Crop Insurance Working (20%)b) Premium Refund for Successive No Claims (16%)c) Coverage of Pest & Disease Risks in Weather-based Crop Insurance (15%)
The suggestions that received the highest aggregate (cumulative out of a maximumof 300%) percentage of responses among the top 3 ranks, irrespective of theirposition within the top 3 ranks, are as following:a) Greater Awareness & Understanding of Crop Insurance Working (55%)b) Better Location of Weather Station (46%)c) Better Design of WBCIS Products (45%)
The above suggestions define the agenda for further work towards bringing crop
insurance, particularly WBCIS, in alignment with the expectations of the farmers.
71. Gaps and weaknesses in the Indian agricultural extension system coupled with the
low level of educational attainment and awareness among farmers negate thebenefits of a competitive market in terms of the variety of weather insurance products
offered by different insurers. In such a setting, the aim should be to offer acompetitive market in terms of client (farmer) services. The existing level of client(farmer) services has been decried by almost all stakeholders during primaryresearch. This calls for a crop insurance model in which a single well-evaluated andfine-tuned standardized crop insurance policy is sold across an entire location withemphasis on differentiation based on client services and product communication. The
three leading insurers participating in WBCIS have also supported the idea of a crop
insurance model in which the main emphasis is on distribution and service delivery,
rather than the product per se. The Spanish model and the USA model are the twomodels of crop insurance that have been indicated to be the most relevant to theIndian context.
72. For the Indian context, the role of AGROSEGURO or RMA can be played by aconsortium of insurers participating in WBCIS. The representation of various insurers
in the apex-level administrative or technical agency can be based on a mix ofAgricultural Finance Corporation

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indicators that may include business size, experience in crop insurance, technicalexpertise, process excellence, investment (time, effort and funds) etc. This apex-levelagency can be given the responsibility of setting the detailed conditions for all
insurance products, in particular the product design and differentiated premium rates
which vary according to risk exposure. Subsidies from government can bechannelized through this apex-level agency to the participating insurers. Reinsurance
arrangements for the entire portfolio can be negotiated centrally to attract betterbusiness terms and to reduce transaction costs. At the field-level, crop insurance
policies may be distributed and serviced by insurers and their marketing agencies.
Insurers who intend to participate only in distribution and service delivery of WBCIS
may be allowed, entitling them only to marketing commissions or operating subsidies.
The insurers participating at the apex-level can contribute as co-insurers or shareholders in the entire crop insurance portfolio. The exact structural configuration of thecrop insurance model appropriate for India may be developed through detailed
consultations with insurers, subject matter experts and technical agencies.
73. Universal coverage of farmers under crop insurance should be pursued aggressively
alongside the goal of financial inclusion. Non-loanee farmers account for more than
50% of the total farmer base in India in the context of formal sources of credit. Such ahuge segment of farmers comprising the non-loanee farmers, who are already devoidof cheaper institutional credit, virtually pays double penalty as they are largely left outof a majority of governmental programs including the crop insurance programme. At
present, there are provisions to provide relief to such farmers in case of catastrophic
weather events or crop disasters, but the quantum of such relief is largely ad-hoc andlimited. In order to protect the non-borrowing farmers from extreme financial distressand provide basic economic security, the government can introduce ‘Catastrophe
Protection’ or ‘Non Insured Crop Loss Assistance’ for farmers, drawing inputs from asimilar program in the USA. Such protection can also become an effective conduit forchannelizing calamity and disaster relief funds from central and state governments.
By linking relief funds to Catastrophe Protection or Crop Disaster Assistance, thebenefit of such relief can be passed on to the targeted groups with greater
efficiencies and transparency.
74. By developing taluka level weather indices for catastrophic insurance, the movetowards more robust systems to mitigate climate change impacts is also ascertained.
Catastrophic risks being low probability and high severity events have in principle alower actuarially fair premium compared to more frequent and moderately severe
crop loss events. This topping-up of plain vanilla crop insurance products with low
premium catastrophic covers would ensure an excellent risk mitigation alternative tofarmers at a higher level of granularity (e.g. at taluka level). As the weather database
improves with time, and cheaper channels to deliver WBCIS or weather insurance
evolve, the sophistication of climatological modeling could be harnessed to develop
even village based covers, which could be envisioned as the ultimate challenge forthe frontiers of weather based crop insurance over the medium to long term horizon.

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Problems of microclimates and basis risk could also be tackled under these layeredrisk transfer provisions for a large section of farmers.
75. Till the time remote-sensing based crop loss estimation evolves to reasonable
perfection, an interim solution would be to blend the features of the area yield index
and weather index. Such blended products, may appeal more to farmers and otherstakeholders, as these would incorporate the respective strengths of both NAIS andWBCIS while limiting the undue dependence on any one of them. It would be
expedient to promote such blended products, with ideally an equal contribution to thetotal sum assured. Such blended products could be continued with almost an equal
emphasis on area yield index and weather index till an integration of remote-sensing
index becomes possible. The area yield index may then be systematically phased out
with improvement in reliability and granularity of remote-sensing index.
76. Coverage of further perils under WBCIS, with the predominant goal of meeting the
expectations of farmers and other key stakeholders, must be resisted to the best
extent possible. Multiple validation exercises in real-life conditions should be aprerequisite for inclusion of any new peril in a WBCIS policy. Experimental coverageof a peril should be discriminated unequivocally from the coverage of standard perils,
through proper representation in WBCIS policy. Rationalization of perils must be
undertaken ab initio to gauge the effectiveness of the coverage of a peril under agiven policy. Only perils which can be insured with reasonable representativenessand verifiability must be included under WBCIS.
77. It is recommended that in the future, crop loss data be gathered in a more systematicmanner, that losses be recorded by peril and at the highest level of resolution
possible. Crop loss information (at individual or at an aggregate level) may be fed inby the afflicted farmer customers through the toll-free phone service which can be
stored in the centralized loss database. The validation of such losses may beundertaken both through physical verifications and juxtaposition with thecorresponding weather or yield data. Development of a centralized loss database forcollection of agricultural loss experience of farmers and hazard impact should beinitiated. This information is a critical input in the product development process forweather insurance and has considerable bearing on how the product payouts
correlate with the actual loss experience. Maintenance and update of centralized loss
database can be subsequently managed by a suitable professional agency which
would be responsible for collecting and processing information from the farmers andother agencies.
78. Improving weather data system for insurers and research community working on
agricultural production risk management is the need of the hour. This calls for greater
responsiveness from public agencies like IMD to leverage their contribution towards
an integrated weather data system for India. In order to materialize the goal of an
integrated data system, concerted efforts are required to tap the synergy in weather
station installation by various public/private agencies. The current growth of weather
station network in India is largely haphazard and devoid of a coordinated approach

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and integrated planning. In order to attain the objective of an integrated data system
for India, Public- Private Partnerships (PPP) and integrated planning at the national
level should be promoted. These centralized efforts have to be followed bydecentralized implementation in identified locations across the country. Taking into
account the existing automatic weather stations, the immediate demand of new
weather stations for the approximately 300 high-priority districts (from the standpointof weather insurance) is around 4000 weather stations. This translates into an
investment requirement of nearly INR 40 Crore, presuming the cost of a reliable,
basic, automatic weather station to be INR 1 lakh.
79. By their very nature, weather insurance products are difficult to comprehend for atypical Indian farmer who is equipped with limited capacities and experience. The
multitude of weather insurance products offered by various weather insurance
providers necessitates the need for benchmarking the various products to enable thefarmer to make an informed choice. Through benchmarking it may be ascertained
whether the products offered by the different insurance companies carry at least
comparable benefits (Protection vis-à-vis Premium). The complex weather insurance
products may be disintegrated into the constituent covers for different perils. As crop
insurance built on a scientific platform is being increasingly used as important welfare
mechanism with substantial financial support from the government, it makes sense to
create a ‘Technical Advisory Body’ (TAB) by the government within the Ministry ofAgriculture and Cooperation (GoI) to review the progress of crop insurance schemeson continuous basis and to provide policy directions.
80. The fundamental mandate of the ‘Technical Advisory Body’ (TAB) would be toapprove roll-out of only those weather insurance products under WBCIS which canensure balance between the expectations of the demand side and the deliverability ofthe supply side. The TAB may also take the role of agriculture insurance
development agency with technical functions and can work closely with IRDA. The
suggestions of insurers and premier research institutions can be invited for identifying
such subject matters experts from India who can objectively assess weather
insurance products and provide inputs for improving them. Since weather-based crop
insurance is a relatively new financial instrument even globally, the possibility of
involving international experts (like actuaries, crop-weather simulation experts etc) insuch a body may also be considered.
81. To complement the process for improving weather insurance products, medium-term
research projects may be commissioned by the Government. As part of theseprojects, high quality weather data from IMD may be analyzed through interdisciplinary research exercises involving research institutions, agricultural
universities, industry think-tanks which can take up region and crop specificcorrelation and calibration exercises. The public good nature of these researchoutcomes could go a long way in making partial insurance products like index-based
weather insurance more popular and gradually affordable.

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Evaluation of Pilot Weather Based Crop Insurance Scheme

82. One aspect of WBCIS and weather insurance schemes in India that frequently cameout from our focus group discussions (FGDs), field surveys and interaction withintermediaries and experts is the need for improvement in service delivery. Both preand post policy sales period service holds the key to customer satisfaction as in any
other product. If the farmers believe that they are valued by the insurers as well as by
the intermediaries, their trust in the weather insurance being marketed also goes up.
83. Value added services like periodic dissemination of weather index data and claims
situation could go a long way in improving the popularity of the products marketed.
Use of SMS based weather data dissemination to progressive farmers or influential
farming groups in the social network of the farming community or complimentary
services like pre sowing weather forecasts could be provided at extremely low costs
while the corresponding returns from these minimal investments by the suppliers
could more than proportionately increase the returns over the long run. Input dealers
and field extension agents could be incentivized to hand hold the clients in the earlydays of being introduced to weather insurance policies which would create a
conducive environment for a fast and educated growth of an important risk
management strategy like weather based crop insurance. Discount schemes onpremium discounts for next season or lottery draws with modest gifts could be used
as a marketing strategy to influence farmers to purchase multiple policies of rainfall
insurance (based on their risk appetite and ability to pay) as most of the voluntary
purchased are confined to single unit purchases.
84. Considering the substantial financial outlay on providing crop insurance to farmers,
the expenditure on its dissemination and promotion through mass media should, atbest, be considered marginal vis-à-vis the annual revenue expenditure on operations
and financial support. The awareness-building campaign for crop insurance may bemodeled on the lines of the remarkably effective communication and promotional
campaign employed for Mahatma Gandhi Rural Employment Guarantee Scheme(alternatively NREGS). The network of institutions falling in the framework of Local
Self Governance should also be more effectively utilized for promotion of crop
insurance. In order to address the queries and grievances of potential and existing
farmer-clients in a personalized manner, the mass-level awareness building efforts
could be complemented by toll-free helplines. It may be useful to avail the existing
set-up of toll-free facility made available by Agricultural Universities and otherextension agencies. Interactive media sensitive to local conditions like street-plays or
insurance games should be employed in areas of more focused sales activity in order
to simplify the insurance mechanism and to make the potential adopters more
comfortable with complex insurance products.
85. With the outreach and penetration of self-help groups (SHGs) and other interestbased collectives spreading deep into the rural hinterlands of India, there areenormous opportunities to leverage these SHGs and other interest-based collectives
for increasing the patronage and reach of crop insurance in India. The utilization of
SHGs and other interest-based collectives as a vehicle for sales, distribution andpost-sales service delivery is going to be a win-win proposition as it can reduce theAgricultural Finance Corporation25

Evaluation of Pilot Weather Based Crop Insurance Scheme

typical insurance problems of moral hazard, high administrative and transaction
costs, lack of customer feedback and poor post sales service delivery. All such
advantages associated with merit focused pilot projects to assess the effectivenessof SHGs and other interest-based collectives in crop insurance promotion,
administration and service delivery. The selection of the SHGs and other interestbased collectives or the location for such pilot projects should take due considerationof the wide differences in the institutional strengths and operational efficiencies
across SHGs and other interest-based collectives.
86. It is a known fact that the economic exposure to uncontrollable risks is significantly
higher for a farmer participating in contract farming initiative than his peers on
account of the higher investments by the former for crop inputs, technology andquality control. In order to safeguard the interest of farmers participating in contract
farming and to promote trials/adoption by other farmers through demonstration effect,crop insurance needs to be given the status of a mandatory input under contract
farming initiatives
87. The continued imposition of service tax on weather insurance policies is startlingconsidering that other forms of insurance targeted at similar socio-economic
segments have been exempted from service tax. Service tax on weather insurance
policies sold to farmers must be waived reinforcing its utility as a social good.
88. The definition of insurable interest must be widened by AIC to include agriculturallabourers in its service net. The income of these workers depends considerably on
rainfall. Initially such coverage of agricultural labourers under WBCIS may bechannelized through reliable intermediaries and can be scaled up after successful
trials
89. Due to the structure of WBCIS, it becomes difficult for insurers to meet the stipulated
regulatory provision under Section 64 VB of the Insurance Act, 1938. Either the
structure of WBCIS or the concerned regulatory provision has to be modified suitably
to ensure proper compliance by insurers.
90. The ceilings on actuarial premiums and premium subsidy have to be hiked up forproviding satisfactory coverage to cultivators in areas prone to high and frequent risk
exposure. In order to incentivize the insurance intermediaries for serving their farmerclients better, their commission may be computed on a value much higher than thesubsidized premium being paid by a farmer insured under WBCIS. According tosome key intermediaries, the commissions for a WBCIS policy are relatively small todemand substantial efforts in marketing and service delivery.
91. The maximum probable loss denotes the highest cumulative payout (sum of payoutsof all constituent covers) among all the cumulative payouts simulated historically froma weather insurance contract. The quantitative difference between maximum sum
assured and maximum probable loss for a weather insurance contract represents atheoretical gap between the maximum payout committed by that contract and theactual payout that could be expected from that contract even in considerably adverse
years. In order to improve the representativeness and verifiability of WBCIS products,

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Evaluation of Pilot Weather Based Crop Insurance Scheme

the quantum of maximum sum assured should be made equal to the maximum
probable loss under that policy. In order to facilitate farmer-clients and other key
stakeholders to choose their WBCIS policy more effectively and enhancetransparency, insurers should provide historical payout distribution table/chart for the
given weather insurance contract for all years considered for structuring that contract.
92. NAIS presently provides for individual assessment of losses in case of localized risks,
viz. hailstorm, landslide and flooding, on an experimental basis. Farmers feel theexperiment is not adequate, and it should be implemented on a full scale, covering allareas. Earlier government reviews have supported the view that the localized
calamities should be assessed on an ‘individual’ basis in all the areas. The practice ofphysical individual/area assessment of losses from non-indexable/localized perils(Hail/Frost/Wind) must be extended to the entire coverage of NAIS and WBCIS.
93. ‘Welfare effects’ of WBCIS payouts should be studied to estimate the impact of thedifferences in income and asset positions of those who got payouts as against those
who did not, at times of droughts or village level catastrophes. The question whether
the payouts are substantial enough to shield household consumption from weather
shocks also needs thorough evaluation.

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Chapter 1
AGRICULTURAL RISK MANAGEMENT
Agriculture is an intrinsically risky economic activity. Variations in crop yields due to adverse
shocks like unmitigated moisture stress in the growing season, drought, natural calamities,
pest infestation, outbreak of diseases and input risks like non-availability of inputs at the righttime frequently affect the agricultural households1 in India. Price risks arising fromfluctuations in input and output prices also induce variability in the agricultural incomes. Yieldrisk becomes the most important agricultural risk given the fact that crop losses arising fromproduction shortfalls or complete crop output failure wipe out farm profits and trigger a
condition of distress where the cultivation costs are irrecoverable, triggering a high default
probability of the indebted farmers or inducing asset-depletion and poor investment potential
in future agricultural seasons. For a monsoon dependent agricultural economy like India
unmitigated yield risks become even more pronounced as it can be generalized that aroundsixty percent of the variations in crop yield are induced by fluctuations in critical weather
parameters like rainfall (Jodha 1972, 1978, 1981a, 1981b; Anderson and Hazell 1989,
Walker and Ryan 1990, Hardaker et al 1994). Given the exposure of crop yields to a
multitude of perils2, it becomes imperative to design risk management systems to stabilize
crop incomes by attenuating seasonal and inter-annual variability.
1.1Need for Stabilizing Farm Incomes
Given the multiple risks embedded in the livelihood of vulnerable communities, the stimuli
and corresponding response to stimuli is of immense significance. It may be noted that the
preponderant small and marginal farmers are naturally the most vulnerable groups as they
have a low asset base, are resource poor and predominantly operate under rainfed
conditions. With low investment potential and poor coping ability these households are thegreatest risk of falling into debt and poverty traps in the eventuality adverse weather shocks.
The vulnerability of resource poor farmers and landless agricultural labourers is aggravatedby the multitude of uninsured risks in conditions where the full-insurance opportunities areabsent (Moscardi and De Janvry 1977, Feder 1980, Rosenzweig 1988, Walker and Ryan
1990, Townsend 1994, Ravallion and Chaudhuri 1997, Kurosaki 1998). Farmers in India
have been observed to be risk-averse and that they seek to avoid risk through various
managerial and institutional mechanisms (Binswanger, 1980 and Hazell, 1982). Studies
have shown the limitations and adverse consequences of consumption and income1

As per the NSS 59th Round 2002-03, around 60 percent of the 148 million rural households in India being
cultivator households and 78 percent of the operational land holdings are marginal and small holdings (less than
2 hectares). The average land holding size for marginal farmers is as low as 0.4 hectares; about 13 percent of theholdings are of 2-4 hectares while 7.1 percent are of 4-10 hectares size (medium and semi-medium).

2Inter-temporal and spatial vagaries in the quantum and distribution of rainfall which assume significance ininducing yield uncertainty.

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Evaluation of Pilot Weather Based Crop Insurance Schemesmoothing by risk-averse poor households (Rosenzweig and Wolpin 1985, Deaton 1992,
Paxson 1992, Rosenzweig and Binswanger 1993, Morduch 1995). Traditional coping
mechanisms and adaptation strategies like drought proofing by mixed cropping, changing
varieties, crops and sowing time, matching crop phenology with weather and water
availability and diversifying income sources are not always efficient and effective against
aggregate climatic shocks and disasters. Indian farmers generally rely on informal
arrangements like diversification of crops and agricultural plots, off-farm activities, selfinsurance. Such traditional risk management systems are sub-optimal and informal risk
management strategies are inefficient as they fail to protect the households in the eventualityof covariate adverse shocks and catastrophic idiosyncratic shocks. Agricultural shocks arefurther amplified in rural areas where financial markets are incomplete and the imperfectland, labour and credit markets are inter-locked. These preconditions dictate the need forformal risk transfer mechanisms like insurance in Indian agriculture and crop insurance has
evolved as one of the most important means of indemnifying the losses in crop yields, andhence crop incomes.
1.2Evolution of Agricultural Insurance: Trends and Problems
Agriculture insurance, in its most popular avatar, crop insurance, has existed in many
countries as an institutional response to agricultural risk. Agricultural production issusceptible to vagaries of weather and large-scale damages due to attack of pests anddiseases making crop insurance a vital instrument in the stabilizing the crop incomes andhence secure the livelihoods of the agricultural community. Crop insurance is based on thefundamental principle of insurance business, that is, the ‘laws of large numbers’. The risk is
distributed across space and time. The losses suffered by farmers in a particular affectedarea are shared by farmers in unaffected areas or premium accumulations over good years
can be used for indemnification of losses in bad years. Thus, a good crop insurance
programme combines both self as well as mutual help principle. Crop insurance brings insecurity and stability in farm income. Crop insurance protects farmers’ investment in crop
production and thus improves their risk bearing capacity. Crop insurance facilitates adoptionof improved technologies and, encourages higher investment resulting in higher agricultural
production
In the early 20th century some private companies in the US introduced crop insurance
covering multiple risks, while protection against specific perils like hail insurance had been
introduced in Europe, the US and Canada, over a century ago. In India, J S Chakravarti3
3

The scheme is outlined and discussed in his book ‘Agricultural Insurance: A Practical Scheme Suited to Indian
Conditions' published in 1920’, printed at the Government Press, Bangalore. This piece of work, is one of theearliest monographs on the subject, but surprisingly does not appear to have been accounted for in the analytical
literature on agricultural insurance. The book was published in 1920; Chakravarti had been working on thesubject since a number of years prior to that. As he mentions in the preface to the book, the first seven chapterswere published in the Mysore Economic Journal during the years 1915 through 1917. He presented a paper on
Agricultural Insurance in 1917 at a conference of the Indian Science Congress at Bangalore. In 1920 he already 3
The scheme is outlined and discussed in his book ‘Agricultural Insurance: A Practical Scheme Suited to Indian

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Evaluation of Pilot Weather Based Crop Insurance Scheme

designed, as early as in 1920, a scheme of agricultural insurance based on rainfall for India
which is a path breaking work in the space of agriculture insurance. His approach has an allIndia perspective, though data relating to the then Mysore state (in Karnataka now) are used
for the purpose of giving the scheme a concrete shape and for analyzing its operational andfinancial implications. Chakravarti’s insightful model was an innovative application of thefundamental principles of insurance to the subject of agricultural insurance.
According to Chakravarti (1920, referred to in Mishra 1995), agricultural insurance in India
should be a package consisting of the following, in increasing order of priority as perconditions prevailing during the times: (i) Insurance of buildings, granaries and agricultural
implements (ii) Cattle insurance (iii) Insurance of crops. He identified that the most importantelement of a system of agriculture insurance is the assumption of the risk of loss ordeficiency in respect of crop production, which forms the core of his scheme of agricultural
insurance because of its importance and complexity. By grouping insurance of houses,
implements, cattle, etc, can with other types of property insurance, it left the system ofagricultural insurance to grapple with crop insurance.
The detailed scheme of agricultural insurance laid down by Chakravarti was sensitive to theissues of basis of insurance i.e. whether the basis of insurance payout should be on thebasis of value of the crop or on its quantity, with his preference for value rather than quantity
as the basis given the inverse relationship between quantity and price of produce andeventualities in bad crop seasons. He took due consideration of indemnification level, role ofthe state within an ‘area approach’ analogous to Dandekar’s (1976) homogenous area
approach. He emphasized the problems of ‘human elements’ i.e. moral hazard in crop
insurance and suggested a scheme of drought insurance. According to him, the usual
remedy applied in other types of insurance to overcome the problem of moral hazard is‘partial insurance or under- insurance or deductible’. Chakravarti rightly argued that this
remedy may not be effective in case of crop insurance, because the crop to be insured is yet
to exist and the state of its existence thereof would depend on the actions of the insured
farmer which an insurer would not be able to monitor easily. This, according to him was likely
to reduce the benefit of insurance and hence its demand. In addition to information
asymmetry problems, he identified other constraints like illiteracy of most cultivators,
inadequate village statistics and general backwardness among the population.
The fundamental terms in the innovative rainfall insurance contract envisaged by
Chakravarty were harbinger of the structure of insurance contracts to be introduced eight
Conditions' published in 1920’, printed at the Government Press, Bangalore. This piece of work, is one of theearliest monographs on the subject, but surprisingly does not appear to have been accounted for in the analytical
literature on agricultural insurance. The book was published in 1920; Chakravarti had been working on thesubject since a number of years prior to that. As he mentions in the preface to the book, the first seven chapterswere published in the Mysore Economic Journal during the years 1915 through 1917. He presented a paper on
Agricultural Insurance in 1917 at a conference of the Indian Science Congress at Bangalore. In 1920 he alreadyhad eleven years' experience with the Mysore State Life insurance Scheme - first as secretary and then aspresident of the State Insurance Committee; see Mishra (1995)

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Evaluation of Pilot Weather Based Crop Insurance Scheme

decades later. As per his model, the aggregate rainfall from the beginning of the agricultural
year as measured at the rain-gauge at the taluka headquarters up to a certain date is lessthan a certain amount, then a certain sum of money will be paid in respect of the insured
field as compensation. The above contract has three critical elements: a specified date, aspecified degree of deficiency in rainfall and a prescribed amount of compensation. Box 1
highlights the salient features of the ‘Chakravarti Model of Agriculture Insurance’. It may be
noted that this scheme is very similar to that suggested in World Bank (1992). Itsarchitecture is based on empirical data and the details lay sound foundations for designinginsurance schemes in agriculture. The World Bank scheme aims to cover all rural
households by selling insurance in the form of ‘rainfall lotteries’ while Chakravarti’s schemefavoured coverage of only crop producers. The interesting framework suggested by
Chakravarti has definitely not received the visibility it deserves.

Salient Features of the Chakravarti Model
Some important features of the scheme are as follows:i)

The scheme is area-based and rain-gauge station specific.

ii) A year is divided into two seasons.
iii) Indemnity is payable if the total rainfall during a season is less than 65 per cent of the normal
rainfall. The percentage criterion can be varied depending on the agro-climatic features of thearea. Indemnity is determined with a view to stabilizing a farmer's net income. Assuming that a
farmer can get one-third of his net income even in a bad year, the scheme aims at an indemnity
equivalent to two-thirds of his income.

iv) Premium is calculated on the basis of the likelihood of a drought occurring and the size ofindemnity indicated above.

v)

The scheme is designed to be self- financing. Coincidentally, the amount of premium turns out
to be equal to the land tax for a unit of land. This makes the premium rate simple to determine,and variable with land quality and hence a farmer's net income.

vi) Though the scheme was designed primarily for annual contracts, quinquennial or decennial
contracts were recommended with a view to enhancing the economic benefits to farmers.

vii) Land owners and tenants are eligible for insurance coverage.
viii) The scheme also envisages insurance policies for co- operative societies and groups of farmers.

In the 1930s Japan and the US formalized public crop insurance schemes. Developing
countries of Asia, Africa and Latin America saw crop insurance schemes much later. In the
1960s and the 1970s there was a lot of optimism for the success of crop insurance schemes,
even for those in the public sector (Mishra 1995). Some studies in the 1980s and early
1990s [e g, Hazell et al 1986, Hazell 1992] argued that all-risk crop insurance programmes
performed dismally and highlighted the need for governments to be cautious in introducing
large crop insurance schemes. Some common findings are that crop insurance schemes arecostly, financially unsustainable and not in a position to produce welfare gains as envisaged.

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Evaluation of Pilot Weather Based Crop Insurance Scheme

Some studies [e.g. Ahsan 1985; Dandekar 1985; Ray 1985; Miranda 1991; Roberts et al
1991; Williams et al] 1993; Mishra 1994 provided evidence on why crop insurance would
work and made sense for protecting agricultural incomes of millions of farmers in thedeveloping world. Agricultural insurance schemes have been argued to be a form of ‘crop
credit insurance’ as most of the crop insurance schemes have been credit-linked. This
reduces the default risk to the lending institutions and probability of repayment remains
higher at times of crop failure as the compensation received from crop insurance enables thefarmers to repay their debts and stay avoid indebtedness to high cost informal credit sources
like moneylenders (Hazell et al 1986; Pomareda 1986).
There are some idiosyncrasies in the case of agricultural insurance making the above
mentioned problems standard attendant problems. The cost of information is much higher,
because the required data related to farms and even plots of land spread over a vastgeographical area. Collection of data is costly as well as time consuming. There is wideagro-climatic variation which results in the problem of adverse selection. A crop insurance
scheme can be based either on the 'individual approach' or the 'area approach'. In case of
the former, assessment of indemnity is made separately for each insured farmer based on
the crop yield of his or her farm and the premium may be determined separately for
individual farmers or for a group of farmers. In case of the area approach both the premium
rate and the indemnity are determined not separately for each farmer but for a group of
farmers. Most crop insurance schemes in the world are based on the individual approach.
Area- yield crop insurance schemes based on the area approach have attained popularity
recently.
Though much of the debate around the efficiency of agriculture insurance hovers around thesustainability of state-sponsored agricultural insurance schemes, private agricultural
insurance has also not been successful due to market failure and state failures owing to thefollowing reasons. First, private insurers have not been able to cope with systemic, nondiversifiable risks in assessing crop yields stemming from say, natural disasters affecting alarge number of farms over a widespread region. Even with the possibility of reinsurance, it
is hard to calculate fair premium in order to develop sufficient reserves for low probability but
high loss events. Second, problems of information asymmetry like adverse selection andmoral hazard problems raise the cost and risks of introducing crop insurance products moreso than other types of general insurance (Wenner and Arias 2003).
Adverse selection in insurance markets refers to the situation where insurers find it
impossible or very expensive to distinguish between high-risk and low-risk insurance
applicants and thus, price or yield insurance contracts at the average premium for all
individuals, which is inappropriate and non-sustainable. This results in undercharging highrisk customers and overcharging low-risk customers for identical contracts. Over time, thelow-risk - clients drop out of the market and the insurance company is left with a very highrisk pool of clients with higher expected indemnities that negatively affects insurer’s
profitability. Moral hazard refers to the situation where the granting of an insurance contract
can lead to a reduction in the application of good husbandry practices or the complete

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Evaluation of Pilot Weather Based Crop Insurance Scheme

altering of production practices on the part of the client, resulting in higher loss claims(Mishra 1995).
Information asymmetry problems affect all insurance markets but are more pronounced inagricultural risk markets because of costly verification of farmer behaviour and difficulties in
obtaining information. The administrative costs of physical monitoring of farmer effort andsegregating bad risk and good risk farmers can be prohibitive in rural India geographicalspread and heterogeneous production conditions.
On the other hand, setting theindemnification of loss at lower levels to discourage ‘hidden action’ and ‘hidden information’problems, can render the market very thin whereby advantages gained by pooling risk types,
the very essence of insurance intermediation, is lost. Private insurers do not have the
incentive to supply agriculture insurance in developing countries precisely for the
aforementioned reasons, and if it is available, it is not affordable to the high risk farmers as
the premiums are high given high probability of claims even if actuarial pricing of insurance
products is undertaken.
Historically, private crop insurance in developed countries has been limited to single peril
products, like hail insurance, for which it is possible to set actuarially sound premiums anddamage verification or loss assessment, is easier. Lack of appropriate time-series data ofyield as well as risk exposures creates disadvantages for the insurer. The adverse selection
problem is aggravated by high premium rates discourage participation of a large section of
the farming community and mostly high risk farmers participate. Moreover, in crop insurance,
the insured farmers do not have any control over the insurable event, but depending on
terms of the contract, they individuals can affect the indemnity values. The insured farmers
might refrain from taking evasive action to reduce farm risks to prevent the loss than anuninsured counterpart would possibly take, when expected indemnity payments exceed thevalue of efforts. This problem of incomplete information discourages participation of private
insurers in agricultural insurance. Another problem that contributes to undersupply ofagriculture insurance by the private sector is the non-independence of aggregate shocks likenatural disasters might create disincentives for insurers to come forward with insurance
schemes in the absence of affordable reinsurance arrangements.
The huge coverage gaps caused by the inability of private insurers to offer affordable
insurance products, especially in the multiple peril and catastrophic loss insurance market
segments has been attempted to be filled up by Government agricultural insurance
programmes, which goes beyond the default responsibility of welfare states to have systems
in place that ensures protection of income of agricultural households. This is also a valid
justification for creating an environment conducive to development of both market based risk
management instruments and state sponsored public agricultural insurance schemes. The
global experience of state-sponsored (Government -backed) agricultural insurance
programmes has not been satisfactory in general if financial viability and coverage of farmers
are the criteria. Other agricultural risk-related interventions by governments like pricesupports, input subsidies (fertilizer, irrigation, electricity) and credit market interventions canalso be very expensive and ineffective in terms of their net welfare-effects, and crop

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Evaluation of Pilot Weather Based Crop Insurance Scheme

insurance is not an exception. The heavily subsidized public crop insurance programmes
have been characterized by high claims ratio and actuarial losses. The unifying theme in theadministration of public crop insurance schemes is that financial performance of most of the
public crop insurance schemes has been disastrous in both developed and developing
countries and the multi-peril crop insurance like the National Agricultural Insurance Scheme(NAIS) in vogue in India today are very expensive and has to be heavily subsidized.
The models of crop insurance existing in some developed countries, such as US, Spain4,
France and Italy are characterized by a certain commonality by way of the central
Government providing the following:(i)

subsidies on premiums to farmers;(ii)

operational subsidies to private insurers to cover some of the high administrative
costs associated with agricultural insurance contract underwriting; and(iii)

subsidized reinsurance.

In the presence of public insurance programmes, private insurers have been argued to be
crowded-out given difficulties in innovating and introducing new risk management products.
In spite of the lack of conclusive evidence on the hypothesis of public crop insurance
schemes driving out private insurers, public agricultural insurance schemes have come toplay a critical role in the near past. This is in reference to these schemes substituting meansof transferring payments to farmers and maintaining farm income levels in the post-Uruguay
Round of trade negotiations regime wherein all signatories to the agreement are supposed toreduce and phase out direct support to farmers (Wenner and Arias 2003). It would be
imprudent to simply replicate the existing model of crop insurance found in developed
countries in a developing country context characterized by limiting fiscal and monetary
constraints. Without quantifying the cross-subsidization effects of agricultural insurance inthe presence of distortionary input and price subsidies would also render successful models
elsewhere ineffective in India. Nevertheless, many of the agricultural risks insured underpublic insurance programme are essentially uninsurable in nature and are generally costly
given the frequent occurrence of the loss events. In sum, traditional agricultural insurance
programmes are financial failures because of high administrative costs and unresolved,
adverse selection and moral hazard problems.
1.3Alternatives to Crop Insurance: Index-based Weather Insurance
The idea of crop insurance which emerged in India during the early part of the twentiethcentury was fiercely debated post-independence (Hazell et al 1986) and significantly
operational only in the nineties (Mishra 1996, Skees et al 1999). It is still evolving in terms ofscope, spread and structure. There are huge coverage gaps in terms of farmers benefitedand crops being covered under the state-sponsored and heavily subsidized National
4

Discussed at length later in this chapter

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Evaluation of Pilot Weather Based Crop Insurance Scheme

Agricultural Insurance Scheme (NAIS), a multi-peril, area based crop insurance scheme thatis mandatory for loanee farmers. Gross regional disparities in terms of farmers covered,premium collected, claims reported and claims settled. A financial innovation that promised
to overcome the problems with the existing crop insurance scheme was ‘index-based
weather insurance’.
The alternative index-based weather insurance products (micro insurance products) thatwere developed to overcome the defects in the traditional crop insurance schemes could
address the problems of moral hazard, adverse selection, high administrative costs,
inadequate indemnification levels and large units of insurance. In addition, it brought in aparadigm shift by reducing the high turnaround times and poor servicing and claims
management of the traditional crop insurance products (Manuamorn 2007). The first indexbased weather insurance was a rainfall insurance contract underwritten by ICICI-Lombard
General Insurance Company for groundnut and castor farmers of BASIX’s water user
associations in Mahabubnagar district of Andhra Pradesh in the year 2003.
Index-based products are contingent claims contracts for which payouts are determined byan objective weather parameter (such as rainfall, temperature, or soil moisture) that is highly
correlated with farm level yields or revenue outcomes. The weather parameters can be used
as a good proxy for the actual losses incurred by farmers. The underlying index used for an
index insurance product must be correlated with yield or revenue outcomes for farms acrossa large geographic area. In addition, the index must satisfy a number of additional properties
that affect the degree of confidence or trust that market participants have in that index; someof these properties are accuracy, reliability, and low susceptibility to measurement errors.
These protect the farmers against covariate weather shocks and not idiosyncratic household
specific weather risks. Because claims for indexed contracts are automatically triggered
once the weather parameter reaches a pre-specified level, the insured farmers receive timely
payouts. The automatic trigger reduces administrative costs for the insurer by eliminating theneed for tedious field-level damage assessment i.e. the field crop cutting experiments(CCEs). Because administrative costs are lower, premiums are relatively low (given low risk
loadings for reinsurance arrangements) and products are more affordable to farmers, inprinciple. The transparent, objective, and exogenous nature of the weather index does awaywith adverse selection and reduces moral hazard. Indexed products also facilitate risk
transfer to the international markets, because international re-insurers are likely to provide
better terms when the insurance is based on measurable weather events and not farm-levellosses. However, it should be borne in mind that the index-based alternatives provide partial
cover by protecting against specific perils like deficit rainfall or excess rainfall.
In view of the problems faced by crop insurance schemes in many countries, alternative risk
transfer instruments like rainfall insurance has received attention (Anderson et al 1989,
Walker et al 1990, World Bank 1992, Manuamorn 2007). Rainfall insurance, i.e., insurance
against both deficit and excess rainfall, and drought insurance, i.e. insurance against

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prolonged rain deficiency only have become two of the most popular alternatives to crop
insurance.
A fundamental difference of rainfall insurance from conventional crop insurance is that it isagainst the occurrence of a particular natural event as distinguished from loss in crop output
resulting from that event. It is more observable and easier to measure than yield.
Consequently, it can solve the problem of moral hazard - provided reliable and accurate
rainfall data are available - and reduce the cost of administration substantially because loss
assessment and inspection of fields would not be necessary. It is possible to have rainfall
insurance across a larger geographical area than yield insurance for specific crops which aregrown in particular regions. This can reduce the problem of covariate risk.
But one limitation of such a scheme could be lack of correspondence between rainfall
recorded by a designated rain gauge station and crop yield income outcomes of the insured
farmers. In addition, factors other than rainfall may affect crop production. One remedy may
be to allow farmers to take insurance associated with more than one recording stations.
However, it may not be necessary in areas with relatively lower and more variable rainfall
and dry-land agriculture. In such regions rainfall affects both area and crop income. Cropped
area declines if rainfall is much less than the normal. In such situations, farmers plant lessprofitable crops. Consequently, deviation in rainfall explains, through its effects on both areaand yield, more of variation in the crop income than the yield variable of particular crops(Walker et al 1990, p 259).
Rainfall insurance is yet to be introduced in a big way. This was suggested in the Australian
context (Lloyd et al 1986,) and some experiments were conducted (Anderson et al 1989)
Private insurers offer drought insurance in some countries, but these are available only tolarge-scale farmers (World Bank 1992). However, the need of such a scheme is much more
for small farmers. World Bank (1992) suggests a scheme for all rural households. It
envisages drought insurance which would be weather-station specific and open to all
households - small and large farmers, landless labourers, shopkeepers, artisans and so on.
All those insured against rainfall at a particular station would pay the same premium andreceive the same indemnity per unit of the sum insured. Drought insurance tickets could besold like lottery tickets. A person would be free to purchase tickets for any weather station,and even more than one stations. It is also envisaged that tickets to poor people, who often
might not be able to afford the premium, may be subsidized. Thus rainfall insurance is being
suggested in the recent literature as a response to unsatisfactory experiences with crop
insurance during the last four or five decades.
1.4 Agriculture Insurance Schemes in India
Crop Insurance is based either on the ‘area approach’ or ‘individual approach’. The area
approach is based on ‘defined areas’ which could be a block / mandal/ hobli/ firka or any
other smaller contiguous area. The indemnity limit originally was 60 per cent, 80 per centand 90 per cent corresponding to high, medium and low risks areas. The actual average

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Evaluation of Pilot Weather Based Crop Insurance Scheme

yield per hectare of the notified crop for the defined area is determined on the basis of Crop
Cutting Experiments (CCEs). In India, governmental efforts began in early seventies tocompensate losses due to the reduction in crop yields arising out of natural calamities. The
experiences on crop insurance schemes in India by both Government and private sectors
are discussed here.
The question of introduction of a crop insurance scheme was taken up for examination soonafter the Indian independence in 1947. Following an assurance given in this regard by the
Ministry of Food and Agriculture in the Central Legislature to introduce crop and cattle
insurance in the country, a special study was commissioned in 1947-48. The first aspect
regarding the modalities of crop insurance considered was whether the same should be onan ‘individual approach’ or on a ‘homogenous area’ approach. The former seeks to indemnify
the farmer to the full extent of the losses and the premium to be paid by him is determinedwith reference to his own past yield and loss experience. The 'individual approach' basis
necessitates reliable and accurate data of crop yields of individual farmers for a sufficiently
long period, for fixation of premium on actuarially sound basis. The 'homogenous area'
approach envisages that in the absence of reliable data of individual farmers and in view ofthe moral hazards involved in the 'individual approach', a homogenous area comprisingvillages that are homogenous from the point of view of crop production and whose annual
variability of crop production would be similar, would form the basic unit, instead of anindividual farmer.
The study reported in favour of a 'homogenous area' approach even as various agroclimatically homogenous areas treated as a single unit and the individual farmers in suchcases pay the same rate of premium and receive the same benefits, irrespective of theirindividual fortunes. The Ministry of Agriculture circulated the scheme, for adoption by theState governments, but the States did not accept.
In 1965, the Government introduced a Crop Insurance Bill and circulated a model scheme ofcrop insurance on compulsory basis to constituent State governments for their views. The billprovided for the Central Government framing a reinsurance scheme to cover indemnity
obligations of the States. However, none of the States was in favour of the scheme becauseof very high financial obligations. On receiving the reactions of the State governments, thesubject was considered in detail by an Expert Committee headed by the then Chairman,
Agricultural Price Commission in July, 1970 for full examination of the economic,
administrative, financial and actuarial implications of the subject.
The major initiatives in the space of crop insurance in India are described below:i.

Individual Approach Scheme

Different experiments on crop insurance on a limited, ad-hoc and scattered scale started
from 1972-73. In 1972-73, the General Insurance Department of Life Insurance Corporationof India introduced a Crop Insurance Scheme on H-4 cotton. Later in 1972, general

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Evaluation of Pilot Weather Based Crop Insurance Scheme

insurance business was nationalized and, by an Act of Parliament, the General Insurance
Corporation of India (GIC) was set up. The new Corporation took over the experimental
scheme in respect of H-4 cotton. This Scheme was based on “Individual Approach" and later
included Groundnut, Wheat and Potato and implemented in the states of Gujarat,
Maharashtra, Tamil Nadu, Andhra Pradesh, Karnataka and West Bengal. It continued up to
1978-79 and covered only 3110 farmers for a premium of Rs. 4.54 lakhs against claims of
Rs. 37.88 lakhs.
ii.

Pilot Crop Insurance Scheme (PCIS) – 1979

In the background and experience of the aforesaid experimental schemes for crop
insurance, a study was commissioned by GIC and entrusted to eminent agricultural
economist, Prof. V.M. Dandekar. Based on the recommendations of Prof. Dandekar, a Pilot
Crop Insurance scheme was introduced by GIC from 1979.
The important features of the scheme were:•

The scheme was based on "Area Approach"•

The scheme covered Cereals, Millets, Oilseeds, Cotton, Potato and Gram.

•

It was confined to loanee farmers only and on voluntary basis.

•

The risk was shared between General Insurance Corporation of India and State
Governments in the ratio of 2:1•

The maximum sum insured was 100% of the crop loan, which was later increased to150%•

50% subsidy was provided for insurance charges payable by Small / Marginal farmers
by the State Government & the Government of India on 50:50 basis.

PCIS-1979 was implemented in 13 States till 1984-85 and covered 6.27 lakh farmers forpremium of Rs. 196.95 lakh against claims of 157.05 lakh.
iii.

Comprehensive Crop Insurance Scheme (CCIS)The Comprehensive Crop Insurance Scheme (CCIS) was introduced with effect from 1st
April 1985 by the Government of India with the active participation of State Governments.
The Scheme was optional for the State Governments. The Scheme was linked to short term
crop credit and implemented on Homogeneous Area approach. 15 States and 2 UTs
implemented the Scheme until Kharif 1999. These were Andhra Pradesh, Assam, Bihar,
Goa, Gujarat, Himachal Pradesh, Karnataka, Kerala, Madhya Pradesh, Maharashtra,
Meghalaya, Orissa, Tamil Nadu, Tripura, West Bengal, Andaman & Nicobar Islands andPondicherry.
The States of Rajasthan, Uttar Pradesh, Jammu & Kashmir, Manipur and Delhi had initiallyjoined the Scheme but subsequently opted out after few years.

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The important features of the scheme were:•

It covered farmers availing crop loans from Financial Institutions for growing food crops& oilseeds on compulsory basis. The coverage was restricted to 100% of crop loan
subject to a maximum of Rs. 10,000/- per farmer.

•

The premium rates were 2% for Cereals and Millets and 1% for Pulses and Oil seeds.
50% of the premium payable by Small and Marginal farmers was subsidized equally byCentral and State Governments.

•

Premium & Claims were shared by Central & State Government in 2:1 ratio.

•

The Scheme was optional to State Governments.

•

The maximum sum insured was 100% of the crop loan, which was later increased to150%•

The scheme was a multi agency effort, involving Government of India, Departments ofState Governments, Banking Institutions and GIC.

The scheme covered 7,62,65,438 farmers, area covered was 12,75,70,282 hectares, total
sum-insured was Rs.2949 crore, total insurance charges were Rs.403.56 crore, total claims
was to the tune of Rs.2303.45 crore and the claims ratio was high figure of 1:5.71. Majorityof the claims under the CCIS were paid in the States of Gujarat - Rs. 1086 Crores (47%),
Andhra Pradesh (21%), Maharashtra (9%) and Orissa (8%).
iv.

Experimental Crop Insurance Scheme (ECIS)

While, CCIS was still being implemented, attempts were made to modify the existing CCIS
from time to time as demanded by the States. During 1997, a new scheme, viz. Experimental
Crop Insurance Scheme (ECIS) was introduced during Rabi 1997-98 seasons which was
implemented in 14 districts of five States. The Scheme was similar to CCIS, except that it
was meant only for all small / marginal farmers with 100% subsidy in premium. The Central
and State Governments in 4:1 ratio shared the premium subsidy and claims. The Scheme
was discontinued after one season due to its many administrative and financial difficulties.
The Scheme covered 4,54,555 farmers for a sum insured of Rs. 168.11 Crores and theclaims paid were Rs. 37.80 Crores against a premium of Rs. 2.84 crores.
v.

Pilot Scheme on Seed Crop Insurance (PSSCI)The scheme was launched with effect from Rabi 1999-2000. In the initial stage, it was for the
IXth Plan. During 1999-2000 and 2000-01, the financial assistance was provided to theidentified states i.e. Andhra Pradesh, Orissa, Gujarat, Haryana, Karnataka, Madhya
Pradesh, Punjab, Rajasthan, U.P and Maharashtra for the implementation of the Pilot
Scheme on Seed Crop Insurance scheme. The salient feature of the insurance scheme wasto cover the risk involved in seed production at field stage, loss in expected raw seed yield,

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Evaluation of Pilot Weather Based Crop Insurance Schemeloss of seed crop after harvest so that more number of the breeder/ institutions/organizations/ seed growers would take up and come forward in seed production.
The main objectives of the Pilot Scheme on Seed Crop Insurance were:•

To provide financial security & income stability to the Seed Growers in the event of
failure of seed crop.

•

To build confidence in the existing seed growers & stimulate participation of new
growers to undertake seed production programme of newly released hybrid/ improved
varieties.

•

To provide stability to the infrastructure established by the State owned Seed
Corporations/ State Farms.

•

To give a boost to the Modern Seed Industry to bring it under Scientific Principles.

All seed growers in public and private sectors of major seed producing states willing toparticipate were eligible to be covered under the scheme. Table 1 shows the 'breeder','foundation' & 'certified' seeds of the crops and the states covered. The identified states
opted for the crops covered are as shown in the list.
Table 1: PSSCI Scheme Coverage
State

Crops

Andhra Pradesh

Paddy, Maize, Jowar, Bajra, Sunflower, Cotton, Groundnut, Red Gram

GujaratBajra, Wheat, Gram, Cotton, Groundnut, Maize, Red Gram, Castor

Haryana

Paddy, Wheat, Gram, Red Gram, Cotton

Karnataka

Paddy, Maize, Jowar, Bajra, Sunflower, Cotton, Groundnut, Red Gram, Bengal
Gram, Black Gram, Green Gram, Ragi.

Madhya Pradesh

Paddy, Wheat, Gram, Soyabean, Sunflower, Cotton, Red Gram, Mustard.

Maharashtra

Paddy, Jowar, Bajra, Wheat, Gram, Soyabean, Sunflower, Cotton, Groundnut,
Red Gram, Green Gram, Black Gram.

OrissaPaddy, Groundnut, Red Gram , Cotton

Punjab

Paddy, Wheat, Gram, Red Gram, Soyabean, Cotton.

Rajasthan

Wheat, Gram, Soyabean, Groundnut, Red Gram, Cotton, Bajra, Castor, Mustard.

Uttar Pradesh

Paddy, Wheat, Gram, Soyabean, Sunflower, Red Gram, Cotton, Potato, Pea,
Mustard.

Source: AICIL

Only the ‘foundation’ & ‘certified’ seed produce that is offered to State Seed Certification
Agency (SSCA) for Certification was eligible for coverage. In case of Breeder Seed, thecoverage was subject to the production being carried out under the supervision of theconcerned Monitoring Committee. For the purpose of insurance coverage, seed areas underjurisdiction of a sub-office/ area-office of SSCA were identified as a unit for determination of
Average Yield and Sum Insured in respect of that unit area.

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Evaluation of Pilot Weather Based Crop Insurance Schemei) Risks Covered
The proposed Scheme sought to provide protection against those risks, which are beyond
control of the farmers. The following types of losses were covered:
A: At Field Stage:
A1. Failure of seed crop field either in full or in part due to the perils indicated below:
Natural Fire, lightning, storm, hailstorm, cyclone, typhoon, tempest, hurricane, tornado, flood,inundation, landslides, drought, dry spells, excessive rain, large scale incidence of pests anddiseases. Damages due to Frost were also covered under the Scheme.
The cover commenced from the sowing / transplanting and continued until the crop is
harvested. In a particular field, if only a portion of the field was affected by any of the above
mentioned perils resulting in rejection of such affected portion of the field, the growers werecompensated corresponding to the proportion of the area rejected.
The compensation payable was on the basis of the graded scale as described below:•

Failure of seed crop within one and half months of sowing, the compensation was
40% of the sum insured corresponding to the rejected area.

•

Failure of seed crop after one and half month of sowing and until the crop is
harvested, the compensation was 80% of the sum insured corresponding to therejected area.

•

However, to be eligible for compensation, failure/ damage (and subsequent rejection
thereby) should take place on a contiguous area of minimum "half an acre" in any
individual grower's farm. In other words, failure/ rejection confined to less than "half anacre" were not considered as a loss for the purpose of the Scheme.
A2. Loss in Expected Raw Seed Yield:

Following perils in addition to the perils mentioned under Para A1 above were covered:•

Prevalence of excessive rain, blowing of hot and/ or cold wind, excessive hot weather
during flowering or seed setting stage:•

Loss in expected raw seed yield due to the above perils was compensated if andwhen the same loss has occurred in general over a large contiguous area.

A3. Loss of Seed Crop after harvest:

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Damage to the harvested seed crop due to operation of the above-mentioned perils whilst
lying on the field until the crop is removed from the field for transportation to the processing
Plant was covered under the Scheme.
B. At Seed Certification Stage:
Losses due to seed lots having failed in 'Germination Test' due to operation of any of theinsured perils mentioned in Para A1 & A2 above is compensated. Failure in germination test
due to any factor/ reason other than the insured ones was not covered.
ii)

Exclusions under Seed Crop Insurance

Physical damage / losses / rejection of field / seed on account of following reasons were
excluded from the coverage:a) Poor crop stand due to either defective planting material or unfavourable conditions
prevailing during sowing period.
b) Non-maintenance of prescribed isolation distance.
c) Non-rouging at appropriate times and non-conformity of prescribed standards or noncompliance of any of the instructions of the Certification Agency.
d) When seed crop production has not been taken up in ideal conditions with proper
cultural practices.
e) Non-acceptance of crop due to non-synchronization of male and female plants.
f) Lodging of seed crop and resulting loss in yield except for the insured perils.
g) Loss or damage to seed crop affected by pests and/or diseases, which otherwise, would
have been controlled by adopting adequate plant protection measures.
h) Losses on account of Physical Purity, Genetic Purity, and admixtures of Other
Distinguishable Varieties (ODV) or due to 'other' reasons not covered under the
Scheme.
i) Loss of seed crop / seeds at field stage / laboratory stage due to theft.
j) Losses to seed crop whilst in transit.
k) Loss / damage due to operation of following perils directly or indirectly:(i) War, invasion, civil war, rebellion, conspiracy, persons acting maliciously.
(ii) Nuclear reaction, nuclear radiation or radioactive contamination.
l) Loss or damage due to:(i) Willful negligence of the insured.
(ii)Human action, birds and animals.
iii)

Sum Insured

Sum Insured was equivalent to preceding three / five year's Average Seed Yield certified in
respect of the identified unit area multiplied by 'Procurement Price' of the seed crop variety

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prevailing in the previous season by National Seed Corporation (NSC). The Sum Insured
may be increased up to 150 per cent of the average processed, tagged certified Seed Yield.
A producing agency opting for higher Sum Insured was required to pay a correspondingly
higher premium.
iv)

Salvage

All seed crops identified for coverage under the Scheme have salvage values. Salvage
values were calculated at a fixed percentage of Procurement Price (PP) as given below,
which will be deducted from the claim amount before payment. Alternatively, the insurer paid
the full amount of compensation (i.e., before deduction of salvage) and took over thepossession of the salvage. The deduction of salvage was applicable in case of losses inGermination test only.
Table 2: Salvage Value under Seed Insurance Scheme
Crop

Salvage as per cent of Procurement Price
Hybrids

Other Varieties

Jowar and Bajra

30

60

Maize

40

60

Paddy, Wheat, Gram and Groundnut

40

60

Sunflower

30

60

Soyabean and Tur

40

50

Cotton2020v) Loss Assessment Method
The certification agency officials, who periodically inspect the field, would intimate individual
grower-wise, the details of damages / losses and rejection thereof to the seed producing
organization and to the insurer. The insurer will arrange for surveying the loss and thecompensation will be estimated and paid as per graded scale mentioned earlier. The firstinspection of the seed crop was done by the State Seed Certification Agencies within 45
days in case loss is reported by the seed grower, otherwise if the crop is normal, the State
Seed Certification Agencies worked out the inspections as per their routine norms. The
amount of claim was proportionate to the area rejected.
Excess: 20 per cent of all admissible claims borne by the Insured.
a)Loss in Expected Raw Seed Yield

The seed producing organization and Certification agency both intimated the occurrence of
the insured peril(s) to the insurer. The insurer along with Certification official and / or an
independent surveyor would then inspect the affected area and collectively assess theAgricultural Finance Corporation

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Evaluation of Pilot Weather Based Crop Insurance Scheme

damage in terms of percentage loss. The estimation of percentage loss of yield of an insured
field was with reference to the average yield certified.
Excess: 20 per cent of all admissible claims borne by the Insured.
b)

Loss after Harvesting and until the Crop is ready for Transportation

Concerned seed producing organization / grower intimated the loss soon after the incident ofloss to the insurer giving all the detailed information. The Insurer then arranged for survey /loss assessment and the compensation was estimated and paid.
Excess: 20 per cent of all admissible claims borne by the Insured.
c)

Loss at Seed Certification Stage

Certification Agency and seed producing organization both intimated to the insurer theindividual farmer-wise Actual Quantity Rejected due to failure in germination test on accountof natural calamity along with the laboratory test results. The maximum amount of claim was
the procurement value of the rejected quantity less salvage value as referred in Salvage
value chart.
vi) Premium Rate
Crop-wise premiums are charged at the following rates:
Table 3: Premium Rates under Seed Insurance Scheme
Crop

Rate (per cent)

Crop

Rate (per cent)

Paddy

3.0

Wheat

2.0

Jowar

3.5

Bajra

5.0

Maize

5.0

Soyabean

5.0

Sunflower

2.5

Groundnut

2.0

Gram

5.0

Tur

5.0

Cotton5.0vii) Sum Insured
Sum Insured is equivalent to preceding three/five year's Average Seed Yield certified in
respect of the identified unit area multiplied by 'Procurement Price' of the seed crop variety
prevailing in the previous season by National Seed Corporation (NSC). The Sum Insured
may be increased up to 150% of the average processed, tagged certified Seed Yield. A
producing agency opting for higher Sum Insured would be required to pay a correspondingly
higher premium.

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Evaluation of Pilot Weather Based Crop Insurance Schemeviii) Risk Sharing
100% Grant by Government of India, Ministry of Agriculture for publicity and Training of
personnel for the states etc. through General Insurance Corporation of India.(GICI), Mobility/
telephone/ telex/telegram/fax/postage charges /stationary etc. for the state seed certification
agencies and seed producing organizations, TA/DA, workshop and miscellaneous
expenditure of Government of India officials. However, grants given to GICI are for risk
sharing beyond 200%, on sunset basis.
ix) Reinsurance
General Insurance Corporation of India negotiated suitable reinsurance arrangement in theinternational market to cover the losses exceeding 100 per cent of premium income. In casethe reinsurance arrangement also covered the Government of India's liability, the same wasadjusted while receiving the Government of India's share of claims liability.
vi. Pilot Farm Income Insurance Scheme (FIIS) 2003-04
Crop insurance in India has been an important risk-transfer arrangement against mostly nonmarket perils such as crop losses from natural disasters and pest attacks. Mitigating pricerisks to cover the farm revenues has not received much attention in the space of agricultural
insurance as price insurance is more effective for those products for which objective pricedata is available. The information asymmetry problems of moral hazard and adverse
selection need to be overcome and loss assessment should be based on a reference price(futures price, spot market price), which cannot be influenced by the farmer.
Farm Income Insurance Scheme (FIIS) is revenue based insurance scheme which wasintroduced on a pilot basis during the Rabi 2003-04 season. The pilot FIIS envisaged
addressing the income risk, using the interaction between yield risk and price risk so as tostabilize farmers’ crop income. The scheme was implemented during Rabi 2003-04 season
in 15 districts of 8 States for wheat and 3 districts of 3 States for rice on pilot basis. The pilot
was extended to 19 districts in four States for rice crop during Kharif 2004 season. Table 4shows the coverage and experience of the FIIS pilots in India. The details of the scheme
follow.
Table 4: FIIS - Coverage & Experience
SeasonCrops
No. of No.
ofStates Districts

Farmers
Insured

Acreage(ha.)

Sum Insured(Rs. million)

Premium(Rs. million)

Rabi
2003-04
Kharif
2004

11

18

1802061910272391.46140.62Claims(Rs.
million)
14.37

4

19

2348262107951808.28

143.85273.104150324018224199.74

284.47287.47

Wheat& RiceRice

Total

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i)

ii)

Objectives•

To provide financial support to farmers, in the event of loss in income from adverse
incidence of crop yield (on account of natural calamities, pests and diseases) andmarket price fluctuations.

•

To encourage the farmers to adopt prudent and progressive farming practices, interms of both agricultural technology and market economics.

•

To enhance food and livelihood security of the farming community.

•

To help stabilize farm incomes, particularly in disaster years.

Salient Features

Crops covered: Paddy (rice) and Wheat.
Farmers covered: All [Compulsory for Loanee farmers; and Voluntary for Non-Loanee
farmers].
Risks Covered: The Scheme provided “Comprehensive Risk Insurance” against loss in farmincome (Short fall in Actual Income [AI] over Guaranteed Income [GI]) in a Notified Area
arising out of adverse fluctuations in yield due to occurrence of any one or combination of
non-preventable natural perils such as Flood, Inundation, Storm, Cyclone, Hailstorm, Landslide, Drought, Dry spells, large-scale outbreak of Pests/ Diseases; and, adverse fluctuationof market prices, as measured against Minimum Support Price (MSP).
Sum Insured (i.e., Guaranteed Income) per hectare: Guaranteed Income (per hectare) =Average Yield of past 7 years × Indemnity Level × Minimum Support Price (MSP) of current
year. The Sum Insured will be Guaranteed Income (GI) per hectare multiplied by the numberof hectares sown with the crop. For this purpose, the acreage shown in the loan application(in case of loanee farmers) and proposal form (in case of Non-loanee farmers) will bereckoned as the acreage (hectares sown).
Premium Rates: Actuarial Premium Rates, State-wise, Crop-wise (irrigated and non-irrigatedcategories, if notified separately are treated as separate crops).
Premium Subsidy: The Premium Subsidy allowed was:
1. Small / Marginal farmers: 75 per cent of Premium
2. Other farmers: 50 per cent of Premium
The Premium Subsidy was borne by the Government of India and released to AICIL at the
beginning of the Season based on estimates.

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Scheme Approach: The Scheme operated on “Area Approach” basis.
• The Yield was calculated at the District / Taluka / Block / Circle etc. level.
• The Market Price was measured at District / State level.
Indemnity Procedure•

Actual Income = current season’s Actual Yield × current season’s Market Price.

•

Market Price of a crop = the current sales price of its “common (i.e., generic) variety”
in the Market.
These prices are recorded by the ‘Agricultural Produce Market Committees’ [APMC].

••

From the first arrival of grain in the Market, each APMC will record the Daily Modal
Price (DMP) of grain. This exercise will be done for 8 weeks.

•This DMP with quantity transacted will be sent to AICIL on a weekly basis.

•

Agricultural Marketing Board (AMB) / Mandi Board will work out the APMC-wiseweighted average of the DMP (weighted with quantity transacted) for the entire 8
weeks.
Finally, AMB / Mandi Board will submit the Market Price of the District based onweighted averages of all APMCs in the District.

•To limit the effects of external pressures on the market prices, a “capping” and “cupping”
range of 20 per cent of MSP was applied on the current season’s Market Price.
“Capping” is a limiting factor on the rising graph of prices.
“Cupping” is a limiting factor on the falling graph of prices.
Claims (i.e., ‘Shortfall in Income’) = Guaranteed Income – Actual Income.
Sharing of Claims
The product is new with unforeseen financial liabilities, as the Actual Income is based on
Market price, while Guaranteed Income is based on MSP, (a notional price without a link toMarket Price). Moreover, appropriate rating methodology is also not available at this stage.
In view of the above, all claims exceeding 100 per cent of premium will be borne by theGovernment of India. The premium for the purpose will be 100 per cent of premium less thecomponents of loading towards administrative and marketing expenses.
Areas for Pilot Project: The Crop-wise and State-wise Districts selected for pilot project
conducted during Rabi 2003-04 season are given in Table 5:

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Table 5: Crop-wise and State-wise Districts Selected for Farm Income Insurance Scheme
Crop
Wheat

Rice

State
Bihar
Chhattisgarh
GujaratJammu & Kashmir
Jharkhand
Madhya Pradesh
Maharashtra
Rajasthan
Uttar Pradesh
Uttaranchal
Assam
Karnataka
Tamil Nadu

District
Buxar
Durg and Rajnandagaon
Banaskantha
Jammu
Hazaribagh and Sahebganj
Hoshangabad, Tikamgarh and Raisen
Parbhani
Kota and Pali
Etawah, Kannauj, Mirzapur and Mathura
Dehradun
KamrupMysoreMaduraiNote: In these experimental districts / crops, NAIS stood suspended; in the rest of the country, NAIS continued.

To summarize, following were the salient features of FIIS:a)

The crops covered were rice and wheat.

b)

The scheme was based on the ‘homogeneous area’ approach and Insurance Unit (IU)
can be an administrative unit such as a village panchayat, mandal, revenue circle, block,taluka or district.

c)

The scheme is compulsory for borrowing farmers and voluntary for non-borrowing
farmers.

d)

The premium rates were actuarial, determined for each state at the district level.

e)

The Government of India subsidized the gross premium payable by the insured farmers.
The subsidy was 75 percent of the premium for small/marginal farmers and 50 percent
for other farmers.

f)

Two levels of indemnity, i.e. 90 percent for low-risk areas and 80 percent for high-riskareas.

g)

If the actual income (current yield X current market price) is lower than guaranteed
income (average yield of 7 years X level of indemnity [80% or 90%] X MSP), the insured
farmer received the compensation.

h)Current market price arrived at using the weighted average of all the Markets in theDistrict using daily modal price of 8 weeks from the first arrival of the grain in the market.
Since Market Prices are often subjected to extraneous pulls & pressures, for achieving abalance, a “capping” & “cupping” range of 20 percent was used on the Market Price toreduce the impact of greater price volatilities on the program.

i)

The Government procurement at MSP is suspended in the pilot districts for covered
crops.

j)

NAIS is suspended for the selected districts/crops where the pilot FIIS is implemented.

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vii.

National Agricultural Insurance Scheme (NAIS) or Rashtriya Krishi Bima Yojana(RKBY) [The current crop insurance scheme operational in India as a replacement of
the Comprehensive Crop Insurance Scheme (CCIS)]

The current crop insurance scheme in operation in India is discussed at length in Section 2of this report.
1.5 Global Experiences with Crop Insurance
Many countries have experimented with and implemented ambitious crop insurance
programs to enable farmers to cope with agricultural risks. Globally, billions of dollars are
spent annually on supporting such programmes by way of public subsidies. Given the highcosts and the alternative uses of these public funds, especially in developing countries, theneed to take a look at the benefits of crop insurance and its alternatives becomes imperative.
Public crop insurance programmes have been around for several decades in US, Japan,
Brazil, Sri Lanka, Mauritius, and Mexico. The U.S.5 Government involvement in crop
insurance began in 1938 after several attempts by the private sector failed to provide multiperil crop insurance. The main reasons for the failure of private efforts were: too broad acoverage of risks, insufficient data for sound actuarial appraisal, group coverage
inadequately tailored to the risks confronting individual farmers, and contracts written too latein the growing season. Two important aspects to be considered in the implementation ofcrop insurance were: first, the demand for crop insurance in the United States was related toboth the premiums charged and the average or expected indemnities. It would take a large
subsidy, probably in excess of 50 percent of premium, to get the majority of U.S. acreageenrolled on a voluntary basis. The second aspect was the effects of crop insurance,
particularly on alternative risk-reducing activities, such as crop diversification and expansionof farm size. It appeared that insurance encourages crop production in marginal areas, but
not dramatically. This was true even in areas that had no federal coverage before thedisaster payments programme established in the year 1974 (Hazell, Pomareda, and Valdes,
1986). Until 1996, the only form of insurance provided by the United States Department ofAgriculture (USDA) was traditional crop insurance that protected farmers against yield
losses.
The historical focus on yield insurance is understandable for at least three reasons. First, inmost areas yield risk for the major crops including corn, soybeans, cotton, wheat, grainsorghum, and barley, was much greater than price risk. Thus, yield insurance provided
significant financial protection to many farmers. Second, until the 1996 crop year, farmers
were able to count on Government commodity programmes for price insurance. The size ofsubsidy payments (deficiency payments) was inversely related to crop price, so that low
prices brought on higher payments. The elimination of U.S. Government deficiency
5

The crop insurance model in USA is discussed at length later

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Evaluation of Pilot Weather Based Crop Insurance Scheme

payments coincided with the introduction of revenue insurance programmes in 1996. Crop
Revenue Coverage (CRC) and Income Protection (IP) became available in that year.
Revenue Assurance (RA) became available in 1997, and Group Risk Income Protection(GRIP) was available in 1999 (Babcock and Hayes, 1997). Currently a variety of insurance
schemes are available in the United States. These include yield insurance, which pays an
indemnity when yield is low; price insurance, which covers low prices; and revenue
insurance, which covers low gross revenue (price multiplied by yield). In addition, there
exists “combined crop” revenue insurance, which pays when revenue from two crops (saycorn and soybean) is low (Babcock, 1999).
An important characteristic of the crop insurance scheme in the US is that crop insurance issubsidized by the Government but administered through private companies. Hail insurance
is not subsidized. So hail insurance is offered along with the subsidized Government policies
by most insurer companies. Rates are based upon the history of crop losses due to hail inthe county and competition also plays a factor in keeping rates low. Adverse selection is not
an issue because companies set rates higher for high-risk areas. Moral hazard is also less ofa problem, as hail is a natural event. The multi-peril insurance subsidized by the Government
is considered to be too expensive, if offered without subsidies. The subsidized insurance
programme administered through private companies is considered to be a significant
improvement over the previous unpopular programmes administered by the Government,
although it is not flawless (Ifft 2001).
In Canada, crop insurance was administered through an area approach, similar to that ofIndia. Turvey and Islam (1995) indicated that the area approach was not only inequitable but
also inefficient. The empirical research from 537 farms confirmed the belief that individual
crop insurance is better in terms of risk reduction, but premiums would also be higher. The
area approach in Canada was concluded to be inequitable, as benefits were not fairly
distributed. The most benefits to be accrued would be by the farmers with yields closest to
the average. The crop insurance in Canada was voluntary at this time, unlike the NAIS.
Adverse selection would be less of a problem at the individual level when insurance ismandatory. Cross-subsidization would be more of a problem, because the better farmers
having to purchase insurance would indirectly subsidize the worse farmers. In Alberta,
Canada, the Agricultural Financial Service Corporation (AFSC) developed a Satellite
Imagery Insurance based on data from satellites that used specific wavelengths of light toestimate growth conditions on native pasture. It compensated producers when the averageaccumulated Pasture Vegetation Index (PVI) in a township fell below a threshold value of 90
per cent of the township normal PVI value from previous years. AFSC also developed lackof-moisture insurance where Spring Soil Moisture was used to estimate moisture conditions
at the beginning of the growing season for pasture and silage producers. Rainfall information
was also collected for the months of May, June and July to determine the final insurance
payments (Stoppa and Hess, 2003).
Mexico’s first agricultural insurance program dates back to 1942. In 1961, the Crop andLivestock Insurance Act were passed, formally establishing the State run National Crop andAgricultural Finance Corporation50

Evaluation of Pilot Weather Based Crop Insurance Scheme

Live Stock Insurance Company (ANAGASA). ANAGASA began operations in 1963 andclients of state development banks, Banco Ejidal and Banco Agricola (late merged to form
Banco Nacional de Credito Agricola - BANRURAL), were obliged to purchase crop insurance
policies. The policies where multiple peril, premiums were subsidized and cultivated area
insured was large. Unfortunately, due to lax monitoring, actuarially unsound pricing, andfraud (filing of false claims), losses for ANAGASA were staggeringly high. At one point,
indemnity payments represented 70% of the loan recoveries of BANURAL. The numbers ofclaims for indemnification were astoundingly high. Eventually, the fiscal cost was deemedunacceptable and ANAGASA was closed in 1988.
In 1991, a new Government crop insurance company named AGROASEMEX was formed.
Unlike its predecessor, AGROASEMEX only reinsures local private insurance companies(only five offer crop insurance products) and about 200 mutual insurance funds (Fondos deAseguramiento or FONDOS). It serves as a technical adviser to the FONDOS, and manages
the Federal premia subsidy program for the FONDOS. 3 The FONDOS tend to be in lowincome regions of the country. The risks covered are drought, excess moisture, frost, hail,fire, wind, plant infestations, and livestock diseases, accidents, incapacity, and forced
sacrifices. The products offered are for investments, expected yield, and greenhouses.
Under this voluntary program, area insured has risen from 636,000 hectares in 1991 to 1.9
million in 2000. Similarly, livestock coverage has risen from 576,000 heads in 1991 to 9.7
million in 2000. The area, however, is much less than the area covered by its predecessor.
In 2000, AGROASEMEX insured 1.9 million hectares out of a total of 21.9 million cultivated(8%). However, AGROASEMEX’s program is more cost effective. For example, its ratio ofindemnity to reinsurance averaged 13.06% for the period 1991-96 (Hernandez, 1997).
Mexico also experimented with the use of weather indices to reinsure their crop insurance. In
2001, the Mexican agricultural insurance programme (AGROASEMEX) used the weather
index to reinsure part of their multiple crop insurance programmes. By using weather indicesthat were based on temperature and rainfall in the major production regions; a weather index
was constructed that was highly correlated with the Mexican crop insurance loss experience.
This method of reinsurance proved to be more efficient than traditional reinsurance (Stoppa
and Hess, 2003).
Agricultural insurance has been available in Uruguay at a very limited scale. Between 1913
and 1993, it was under state monopoly. The State Insurance Bank (Banco de Seguros delEstado) was the only entity permitted to issue policies. Since then only two privatecompanies have entered the marketplace, offering single peril policies. The limited coverageis due in part to an unofficial policy of virtually automatic post-disaster relief from theGovernment. When farmers face climatic and market shocks, normally they mobilize andlobby the Government for assistance. Therefore, they have little or no incentive to purchaseprivate insurance. In addition, the Banco de Seguros del Estado is perceived as not honoring
its contracts. When a policy issued by the Banco de Seguros del Estado states that anindemnity should be paid, technicalities are used to reduce or delay payment. As a result, reenrollment rates fall. Since 2001, the Ministry of Agriculture has been trying to rationalize theAgricultural Finance Corporation

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Evaluation of Pilot Weather Based Crop Insurance Scheme

system by undertaking studies to design a new, more rational insurance scheme to replace
the present anemic private sector efforts and the very expensive and nontransparent “postdisaster emergency payments and debt forgiveness” schemes. Spanish insurancecompanies are advising the Uruguayan Government. A law is being proposed in Uruguaythat would have the Government subsidize up to 60% of farm premiums for a multiple peril
policy that covers partial loss, introduce an area yield product, and to establish a separate
catastrophic emergency disaster fund open to only those that purchase crop insurance. The
major impediments are lack of a set of complementary and well coordinated supporting
institutions, the lack of a clear legal and regulatory framework, and the need to develop a“new vision about how to handle agricultural risk”. On the positive side, Uruguay has longand reliable series of historical data on weather and agricultural production as well as acadre of well-trained professionals in the subject matter (Wenner and Arias 2003).
The current crop insurance programme in Japan originated in the Agricultural Loss
Compensation Law enacted in 1947 (Hazell, Pomareda, and Valdes, 1986). At the time of its
introduction, Japan desperately needed to increase cereal production, and this necessitatedencouraging the expansion of rice production in riskier areas. In Japan, subsidized crop
insurance was enacted on a compulsory basis, partly to prevent new owner or farmers fromreverting to tenant status in disaster years through distress land sales. Also, the subsidy wasbiased toward those farms located in the riskier areas. The programme was successful inattaining its objectives, though several adjustments had to be made over time. Originally,
insurance coverage was based on the average yields for each village. This failed toadequately protect high - yield farmers, while it over-protected low-yield farmers. In response
to widespread criticism, the coverage was changed in 1957 to reflect the average yields ofindividual plots. Farmers were also offered some flexibility in the amount of coverage theypurchased. Crop insurance became increasingly irrelevant to the needs of many small farms
which became part time units as Japan prospered in the post-war period. Since crop
insurance was compulsory, it became necessary to increase each crop's minimum acreageat which insurance had to be purchased.
In the Latin American region, Brazil's national crop insurance programme called ‘PROAGRO’was established in 1973 as a voluntary programme to assist farmers in repaying their loans
in the event of certain natural disasters. It initially provided coverage of up to 80 per cent of
the amount of a loan for a standard premium of 1 per cent. Loss ratios (indemnities divided
by premiums) were high and climbed to 40 in 1975. The programme has survived with theaid of large Government subsidies. In 1980, the subsidy from the central bank represented
58 per cent of PROAGRO's total revenues. Lopes and Bias (in Hazell, Pomareda, andValdes 1986) attributed PROAGRO's poor performance to three basic causes. First, the low
premium rate charged and the high cost of administration. They calculated that the premium
should not have been less than 6 per cent. Second, because the programme was voluntary,
it attracted only a few participants, who tended to be high-risk producers. Third, theprogramme was too specialized in wheat and upland rice. A number of substantial changes
have been made to PROAGRO since 1980. The programme now is compulsory for all farm
production loans. The premium increases with the proportion of the loan covered, theAgricultural Finance Corporation

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Evaluation of Pilot Weather Based Crop Insurance Scheme

frequency of claims, and the use of traditional rather than recommended technologies.
Finally, farmers can no longer claim indemnities for rice when the crop is under sown withpasture, previously a common source of abuse. Although the loss ratio declined to less than
3 in recent years, it remains to bee seen if the scheme will remain solvent financially. In spiteof the limitations of this scheme, some variant of the programme is likely to continue as partof the Government's efforts to assist farmers and as a system for protecting the banking
system in the form of credit-linked insurance. In contrast to PROAGRO, Sao Paulo's
successful insurance scheme for cotton is in operation since 1939. The scheme iscompulsory and has operated with an average loss ratio of 0.96 for the last two decades.
One merit of state crop insurance as demonstrated in Brazil is that it shows the possibility of
future transfer of federal programmes to state agencies or to private insurance companies.
There are important similarities in the evolution of crop insurance in the United States, Brazil,
and Japan. The three have gone through considerable adjustments, learning from their own
experiences. After heavy losses in the beginning years, programme administrators haveintroduced new rules, including higher premium rates. Subsidies have been essential, andthey are provided by the Government on the grounds of broad social objectives. In Brazil, forexample, crop insurance to some extent protects the banks that serve agriculture, which
allocate credit to farmers at negative real-interest rates. To become self-financing, these
programmes would have to raise premium rates substantially. However, as shown for theUnited States and suggested for Brazil, this could cause such a drop in participation that the
programmes would have to be discontinued. Gudger and Avalos (1982) take the view of apractitioner called upon to assist in planning during the initial operation of an agricultural
insurer. They use the Costa Rican experience to illustrate what frequently goes wrong in thedesign and operation of agricultural insurance. They provide evidence that historicalexperience with multiple-risk insurance in the private sector is not encouraging. The questionof whether crop insurance should be public or private is important to rise, even if only toclarify the objectives and premises of public programmes.
There are a number of such crop insurance programmes in Switzerland, Chile, Spain, andother countries. These schemes are characterized by small size, limited clientele, and mostcritically, an inability to sustain catastrophic losses without concessional reinsurance fromthe Government. Interesting compromises are mixed-capital ventures, like Ecuador's, andnon-profit, mutual crop insurance, like Bolivia's. In the latter, what began as a Government
institution is evolving toward a mutual agency, in which the insured themselves are theowners of the company. All three types of programmes - private sector, mixed capital, andnon-profit mutuals—have a rather short history, and thus their viability is not certain.
However, they bear careful observation to see if they can indeed reach a mass market, or ifthey will instead remain confined to relatively few large-scale farmers (Peter Hazell, Carlos
Pomareda, and Alberto Valdes, 1986).
Crop insurance in South Africa was started in 1929 when a group of farmers started a poolscheme. Subsidized multi-peril insurance was offered for some time, but for the past fifteen
years no subsidies have been given. Hail is the main peril covered and many other perils are

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Evaluation of Pilot Weather Based Crop Insurance Scheme

also covered. Historical data and past claims play a role in determining the premiums anddamage assessment is the biggest challenge for crop insurers. Crops at different stages areaffected differently by hail, making knowledge essential for insurers. There are several
players and new ones are continuously targeting this market. Several crops are covered,including maize, wheat, sunflowers, and citrus fruits. The South Africa case illustrates how
private individuals can offer crop insurance that is beneficial to farmers and how crop
insurance can still exist after subsidies are withdrawn.
The financial experience with publicly-provided, multiple-peril crop insurance has been
disastrous. In all cases, programmes were heavily subsidized and governments not only paid
part of the premium, but also most of the delivery and service costs, and they covered
aggregate losses even when the losses exceed targeted levels over long periods of time. In
order to be profitable, a purely private insurer would have to structure contracts so thatpremiums collected exceeded the average pay-outs (indemnities plus administrative costs).
Hazell quantifies the condition for sustainable insurance as follows:(A + I) / P < 1
where, A = average administrative costs; I = average indemnities paid; and P = averagepremiums paid.
Table 6 shows experience with crop insurance in some select countries. The loss ratio
exceeds 2 in every case. Two extremes were noteworthy: in Brazil, the ratio of indemnities topremiums was very high while the ratio of administrative cost to premiums was relatively low
during 1975-81; while in Japan, the situation was reversed during 1985-89. Pomareda(1982) explored ways in which improved financial management can help reduce the cost of
agricultural insurance. Pomareda's analysis suggested that the premiums needed to coverindemnity and administrative costs for all-risk insurance should be in the order of 20 per
cent. This was high compared to non-agricultural insurance, and high relative to farmers'
demand for insurance. Crucial ways to reduce the cost of agricultural insurance appear to be
improvements in actuarial practices, better management of investment portfolios, access toreinsurance, and reductions in administrative costs. An important lesson learnt from thesetrends and problems in crop insurance schemes worldwide is that one must invest a great
deal in administrative cost and monitoring before having a crop insurance programme thatwill be actuarially sound.
Table 6: Financial Performance of Crop Insurance Programmes in Some Select Countries
Country
Brazil
Costa RicaJapan
MexicoPhilippines
USA

Period1975-81
1970-89
1947-77
1985-89
1980-89
1981-89
1980-89

I/P
4.29
2.26
1.48
0.99
3.18
3.94
1.87

A/P
0.28
0.54
1.17
3.57
0.47
1.80
0.55

(A+I)/P
4.57
2.80
2.60
4.56
3.65
5.74
3.42

Source: Skees 2003

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Evaluation of Pilot Weather Based Crop Insurance Scheme

The ratio I / P measures the average returns to farmers’ own investment in the insurance;i.e., the number of dollars received on an average per dollar of premium they pay from theirown pocket. Apart from Japan, most farmers appeared to make a killing from crop insurance,
receiving two to four times, as much money back as they pay in. Surprisingly, they still do notline up to buy it and it typically has to be made compulsory. A likely reason for this is adverse
selection. Because farmers are often grouped into risk categories when the premium rates
are calculated, but receive benefits that are tailored to their individual losses, then farmers ineach group facing lower than average risks may end up paying too much for the average
benefits they receive. But sometimes, farmers are reluctant to buy insurance even when it isprofitable because they can expect to receive alternative payments from the Government incatastrophic years without paying any premium up front (e.g. emergency drought relief
programmes). I / P ratios reported above are unlikely to have improved in more recent years.
Indeed, Mishra reported that India’s I / P ratio increased to 6.1 for the period 1985-94, andSkees (1999) reported that the US programme has an expected I / P of 4 for 1999.
Skees, Hazell, and Miranda (The World Bank) suggest the following guidelines for improving
Government sponsored insurance products:i) Make the insurer responsible for its own financial affairs, and deny it automatic access toGovernment funds when they incur losses. Subsidies are not necessarily ruled out,
particularly for important target groups, but they should be fixed in advance on a prorated basis.
ii) Only insure “insurable” risks to the maximum extent possible, e.g., specific perils like hail
damage. Where moral hazard cannot be avoided, then use deductibles and other coinsurance arrangements.
iii) Premiums should be based on sound, actuarial calculations, and adjusted over time toreflect actual loss payments.
iv) The insurer should develop a rational insurance portfolio for managing risk, and should
not be tied rigidly to the lending portfolio of an agricultural development bank. They
should be required to purchase realistic levels of re-insurance in the national orinternational insurance markets.
v) The insurance should be voluntary and in competition with the private sector.
vi) To avoid adverse selection, premium rates should be tailored to the indemnity paymentsthat individual farmers receive, to the largest extent possible.
vii) Administrative costs must be controlled.
In Netherlands, mutual insurance concept has attained popularity as it has regulatory
approval. Mutual crop insurance products like AVIPOL, Potato Pol were in vogue there formore than a decade and found to be effective. The role played insurers and reinsurers like
Interpolis and Eureko-Re gives a good support to farmers and their aggregations. They also
support activities like risk analysis, risk modeling, risk management and providing advice,
and assistance for establishing mutual insurance.

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Evaluation of Pilot Weather Based Crop Insurance Scheme

In summary, traditional agricultural insurance programmes are financial failures because ofhigh administrative costs and unresolved, adverse selection and moral hazard problems.
Until now, many agricultural insurance programmes in the world have not been able to fully
cover their own indemnity payments (I) and administrative costs (A) with the collected
premiums (P).
1.6Relevant Cases of Public-Private Partnership in Crop Insurance
We discuss two important cases of public-private partnership which stand out in the global
scenario:
Spanish Model
Spain has a rich experience in agricultural insurance. Different systems with a varying
degree of involvement of the state were tested between the 1920s and the 1970s. Overall
success, however, remained limited and participation rates disappointing. The current
system is built on that experience. It was set up in 1978 and continues to evolve. The basic
feature of the system is that all insurable agricultural risks are covered by the private sector
and all types of policies are subsidized by the state. Most policies are of the type “multiplerisk”. In the year 2000, about 30 per cent of Spanish producers participated in the system
and about 30 per cent of crop and 10 per cent of animal production were covered.
The system is based on an intricate partnership between the private and the public sector.
The customers of the system are farmers who can take out agricultural insurance individuallyor obtain coverage through co-operatives and professional organizations. Participation in thesystem is voluntary. Besides the customers, the key-players of the system are:•

ENESA (Entidad Estatal de Seguros Agrarios), attached to the Ministry of Agriculture,
Fisheries and Food. Its president is the under-secretary of the Ministry and its director isappointed by the Minister of Agriculture. All stakeholders of the system, including
farmers, are represented in this organization.

•

AGROSEGURO (Agrupacion Espaflola de Entidades Aseguradoras de los Seguros
Agrarios Combinados), a pool of sixty private insurance companies which participate in asystem of co-insurance. According to this system, the companies share the total risk
underwritten in a given year by all members in proportion to their participation in theequity of AGROSEGURO. AGROSEGURO, on behalf of its members, assumes the dayto-day running of the programme, i.e. fixing and collecting premia, assessing losses,
paying compensations, controlling farmers etc.

•

CCS (Consorcio de Compensación de Seguros), a public enterprise with own resources,
operating as a re-insurer (under the control of the Ministry of Economy). Re-insurance by
CCS is obligatory.

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Evaluation of Pilot Weather Based Crop Insurance Scheme

For any given year, ENESA takes the lead in publishing the annual plan. On the basis of theframework set out in the plan, AGROSEGURO fixes the detailed conditions for all insurance
products, in particular the regionally differentiated premium rates which vary according to risk
exposure and also include administrative and re-insurance costs. Once the conditions for thevarious products are set, they are then commercialized through the networks of theinsurance companies, which are members of the pool of AGROSEGURO. Obligatory reinsurance is provided by CCS, additional private re-insurance is provided by privatecompanies for viable lines for coverage going beyond the level provided by CCS.
Subsidies from the state and the regions are paid out by ENESA and channeled through
AGROSEGURO to the insurance companies. Public subsidies amount to up to 41 per centof the premium. For the period of 1980 — 1999 taken together the claims/premium ratio was
113 per cent. Losses are covered by the insurance industry and CCS. A key feature of the
Spanish system is the participatory approach. All stakeholders are represented in ENESA,
which enables taking strategic decisions and fixing the framework for the system annually ina need based approach.
American Model
The Federal Crop Insurance Corporation (FCIC) was created in 1938 as a wholly owned
Government corporation. It is currently administered by the Risk Management Agency(RMA). The RMA was set-up in 1996 to administer the agricultural insurance programmes
and other non-insurance-related risk management and education programmes that help
support U.S. agriculture. The RMA regulates and promotes insurance programme coverage,
sets standard terms — including premium rates — of insurance contracts, ensures contract
compliance, and provides premium and operating subsidies. Crop insurance policies aredelivered — sold, serviced, and underwritten — by private insurance companies.
Companies that qualify to deliver crop insurance must annually submit plans of operation for
approval by FCIC. The plan provides the FCIC with information on the ability of the company
to pay potential underwriting losses and on the allocation of company’s crop insurance
business to the various risks sharing categories for the purpose of reinsurance.
In addition to re-insurance, the companies are paid a subsidy by FCIC for administrative,
operating, and loss adjustment costs. The levels of administrative and operating subsidy andthe terms of re-insurance are specified in the Standard Reinsurance Agreement (SRA),
which applies to all companies delivering FCIC-reinsured policies. Private companies share
the risk with FCIC by designating their crop insurance policies to risk sharing categories,
called reinsurance funds.
Companies retain or cede to FCIC portions of premia and associated liability (potential
indemnities). FCIC assumes all the underwriting risk on the ceded business and various
shares of the underwriting risk on the retained business, determined by the particular
category and level of losses. Companies can further reduce their underwriting risk on

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retained business through private reinsurance markets. Insurance companies may develop
new insurance products, which have to be submitted to the FCIC for approval. They can alsooffer private coverage without Government support that supplements the crop insurance
programmes.

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Evaluation of Pilot Weather Based Crop Insurance Scheme

Chapter 2
NATIONAL AGRICULTURAL INSURANCE SCHEME (NAIS) / RASTRIYA KRISHI BIMAYOJANA (RKBY)
The crop insurance scheme currently being implemented in India is the National Agricultural
Insurance Scheme (NAIS) which started from Rabi 1999-2000 season. Agriculture Insurance
Company of India Ltd. (AICIL) took over the implementation of National Agricultural
Insurance Scheme which until the financial year of 2002-03 was implemented by the General
Insurance Corporation of India. The details of the scheme are explained below:
2.1Objectives
The objectives of the NAIS (RKBY) are as under:(i)to provide insurance coverage and financial support to the farmers in the event of
failure of any of the notified crop as a result of natural calamities, pests and diseases;(ii) to encourage the farmers to adopt progressive farming practices, high value inputs andhigher technology in agriculture; and(iii) to help stabilize farm incomes, particularly in disaster years.
2.2Benefits Expected from Scheme
The Scheme is expected to:i)be a critica1 instrument of development in the field of crop production, providing
financial support to the farmers in the event of crop failure;ii)

encourage farmers to adopt progressive farming practices and higher technology inagriculture;iii)

help in maintaining flow of agricultural credit;iv)

provide significant benefits not merely to the insured farmers, but to the entire
community directly and indirectly through spill over and multiplier effects in terms of
maintaining production and employment, generation of market fees, taxes etc. andnet acceleration to economic growth; andv)

streamline loss assessment procedures and help in building up huge and accurate
statistical base for crop production.

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2.3Salient Features of the Scheme
Crops Covered
The crops in the following broad groups in respect of which (a) the past yield data based on
Crop Cutting Experiments (CCEs) is available for adequate number of years and(b)requisite number of CCEs are conducted for estimating the yield during the proposedseason:1. Food crops (Cereals, Millets and Pulses)
2. Oil seeds
3. Sugarcane, Cotton and Potato (annual commercial / annual horticultural crops)
4. Other annual commercial / annual horticultural crops subject to availability of past yielddata will be covered in a period of three years. However, the crops which will becovered during the year will have to be spelt before the close of preceding year.
States and Areas Covered
The Scheme extends to all States and Union Territories. The States / UTs which are opting
for the Scheme are required to take up all the crops identified for coverage in a given year.
Exit Clause: The States / Union Territories once opting for the Scheme will have to continue
for a minimum period of three years.
Farmers Covered
All farmers including sharecroppers and tenant farmers growing the notified crops in the
notified areas are eligible for coverage. The Scheme covers following groups of farmers:A) On a compulsory basis: All farmers growing notified crops and availing Seasonal
Agricultural Operations (SAO) loans / KCC loans and Jewel loans for the purpose ofcultivation of the notified crop, from Financial Institutions (FI), i.e., Loanee Farmers.

B) On a voluntary basis: All other farmers growing notified crops (i.e., Non-Loanee
farmers) that opt for the Scheme.
Risks Covered and Exclusions
Comprehensive risk insurance will be provided to cover yield losses due to non-preventablerisks, viz.:a)

Natural Fire and Lightningb) Storm, Hailstorm, Cyclone, Typhoon, Tempest, Hurricane, Tornado, etc.

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Evaluation of Pilot Weather Based Crop Insurance Schemec)Flood, Inundation and Landslide.

d) Drought and Dry spellse)Pests / Diseases etc.

Losses arising out of war and nuclear risks, malicious damage and other preventable risks
will be excluded.
Sum Insured / Limit of Coverage
The Sum Insured (SI) may extend to the value of the threshold yield of the insured crop at
the option of the insured farmers. However, a farmer may also insure his crop beyond thevalue of threshold yield level up to 150 per cent of average yield of notified area on paymentof premium at commercial rates. In case of Loanee farmers the Sum Insured would be atleast equal to the amount of crop loan advanced. Further, in case of Loanee farmers, theInsurance Charges will be additionality to the scale of finance for the purpose of obtaining
loan. In matters of Crop Loan disbursement procedures, guidelines of RBI / NABARD will bebinding.
Premium Rates
Premium rates for different crops to be insured under NAIS are given in Table 7. Transition
to the actuarial regime in case of cereals, millets, pulses and oil seeds would be made in aperiod of five years. The actuarial rates will be applied at District / Region / State level at theoption of the State Government / Union Territory.
Table 7: Premium Rates under NAIS
S. No. Season

1.

2.

3.

Crops

Premium rate
3.5 per cent of Sum Insured
Bajra and Oilseeds
Actuarial rate, whichever is less.
KharifOther crops (cereals, other millets and 2.5 per cent of Sum Insured
pulses)Actuarial rate, whichever is less.
1.5 per cent of Sum Insured
Wheat
Actuarial rate, whichever is less
Rabi
Other crops (other cereals, millets, 2.0 per cent of Sum Insured
pulses and oilseeds)
Actuarial rate, whichever is less
Kharif and Annual Commercial /Actuarial rates.
Rabi
Annual Horticultural crops

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or
or
or
or

Evaluation of Pilot Weather Based Crop Insurance Scheme

Premium Subsidy
Fifty per cent subsidy in premium in case of loanee farmers and 55 per cent in case of nonloanee farmers are allowed in respect of small and marginal farmers, to be shared by theGovernment of India (5 per cent) and the concerned State / Union Territory (UT)
Government (45 per cent). The premium subsidy was required to be phased out on sunset
basis in a period of three to five years subject to the review of financial results and the
response of farmers at the end of the first year of the implementation of the Scheme.
The definition of Small and Marginal farmer is as follows:•

Small Farmer: A cultivator with a land holding of 2 hectares (5 acres) or less, as
defined in the land ceiling legislation of the concerned State / UT.

•

Marginal Farmer: A cultivator with a land holding of 1 hectare (2.5 acres) or less.

Sharing of Risk
Risk is shared by Implementing Agency (IA) and the Government in the following manner:
Food Crops and Oilseeds: Until complete transition to actuarial regime in a period of five
years takes place, claims beyond 100 per cent of premium will be borne by the Government.
Thereafter, all normal claims, i.e., claims up to 150 per cent of premium will be met by IA andclaims beyond 150 per cent will be paid out of Corpus Fund for a period of three years. After
this period of three years, claims up to 200 per cent will be met by IA and above this ceiling,out of the Corpus Fund.
Annual Commercial Crops / Annual Horticultural Crops: Implementing Agency will bear all
normal losses, i.e., claims up to 150 per cent of premium in the first three years and 200 per
cent of premium thereafter subject to satisfactory claims experience. The claims beyond 150
per cent of premium in the first three years and 200 per cent of premium thereafter will be
paid out of Corpus Fund. However, the period of three years stipulated for this purpose will
be reviewed on the basis of financial results after the first year of implementation and theperiod will be extended to five years, if considered necessary.
To meet catastrophic losses, a Corpus Fund will be created with contributions from theGovernment of India and State Government / UT on 50:50 basis. A portion of Calamity Relief
Fund (CRF) will be used for contribution to the Corpus Fund. The Corpus Fund will be
managed by Implementing Agency (IA).
Area Approach and Unit of Insurance
The Scheme would operate on the basis of ‘Area Approach’ i.e., Defined Areas for eachnotified crop for widespread calamities and on an individual basis for localized calamities

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Evaluation of Pilot Weather Based Crop Insurance Schemesuch as hailstorm, landslide, cyclone and flood. The Defined Area (i.e., unit area of
insurance) may be a Gram Panchayat, Mandal, Hobli, Circle, Firka, Block, Taluka etc. to be
decided by the State / UT Govt. However, each participating State / UT Government isrequired to reach the level of Gram Panchayat as the unit in a maximum period of three
years. Only Andhra Pradesh has succeeded in this endeavor so far.
Individual based assessment in case of localized calamities, to begin with, would be
implemented in limited areas on experimental basis, initially and will be extended in the lightof operational experience gained. The District Revenue administration will assist the
Implementing Agency in assessing the extent of loss.
Seasonality Discipline
The broad seasonality discipline followed for Loanee farmers are as under:
Table 8: Cut –Off Dates Fixed for NAIS
Activity

KharifLoaning period

April to September

October to Next March

Rabi

Cut-off date for receipt of Declarations

NovemberMay

Cut-off date for receipt of yield data

January / March

July / September

The broad cut-off dates for receipt of proposals in respect of Non-Loanee farmers will be as
under:
Kharif season: 31st July
Rabi season: 31st December
However, seasonality discipline may be modified, if and where necessary in consultation with
State / UT and the Govt. of India.
Estimation of Crop Yield
The State / UT Govt. plan and conduct the requisite number of Crop Cutting Experiments(CCEs) for all notified crops in the notified insurance units in order to assess the crop yield.
The State / UT Govt. is required to maintain a single series of Crop Cutting Experiments(CCEs) and resultant yield estimates, both for Crop Production estimates and Crop
Insurance. Crop Cutting Experiments (CCEs) will be undertaken per unit area / per crop, ona sliding scale, as indicated below:
A Technical Advisory Committee (TAC) comprising representatives from National Sample
Survey Organization (NSSO), Ministry of Agriculture (MoA) (Government of India) and
Implementing Agency (IA) will be constituted at national level to decide the sample size of
CCEs and all other technical matters pertaining to threshold yield / actual yield, etc. The

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number of CCEs to be conducted is given in Table 9.
Table 9: Number of Crop Cutting Experiments to be Conducted under NAIS
S. No.
1.
2.
3.

Minimum number ofRequired to be Done

Unit Area
Taluka / Tehsil / Block
Mandal / Hobli/Firka / any other smallerarea comprising 8 - 10 villages
Gram Panchayat comprising 4-5 villages

C.C.Es

16
unit

10
08

The yield data is furnished to IA by the State Government / UT in accordance with the cut-off
dates fixed for all crops and areas notified, based on the total number of Crop Cutting
Experiments (being not less than the minimum prescribed) conducted. The standard
procedures for assessing the yield in respect of multiple picking crops is prepared by IA in
consultation with the National Sample Survey Organization (NSSO) and circulated among
implementing States / Union Territories.
A Committee comprising representatives of State / UT Government, National Sample Survey
Organization (NSSO) and IA is required to be set up at the State level to monitor / supervise
and advice in matters relating to adequacy and quality of CCEs.
Loss Assessment in Case of Localized Calamities
Loss assessment and modified indemnity procedures in case of occurrence of localized
perils, viz., hailstorm, landslide, cyclone and floods, where settlement of claims will be
formulated by IA in coordination with the concerned State / Union Territory. Settlement ofsuch claims will be on individual basis between IA and insured. This procedure will be
experimented in two districts and will be extended to other areas in the light of operational
experience gained. The insured farmers who experience crop losses due to occurrence of
these localized perils will give immediate notice to the financial institution / notified office ofIA and in any case within 48 hours along with particulars of crop insured and extent andcause of damage.
On receipt of loss the intimation, IA will depute Loss Assessors to the area for assessment ofcrop loss. The District Revenue administration will assist IA in assessing the extent of croploss.
IA will also develop Loss Adjuster Cadre and for this purpose a few Officers will be trained inloss assessment procedures. The services of unemployed Agricultural Graduates and retired
Agricultural Department Officials may also be utilized for loss assessment after imparting
initial training.

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64Evaluation of Pilot Weather Based Crop Insurance Scheme

Levels of Indemnity and Threshold Yield
Three levels of indemnity, viz., 90 per cent, 80 per cent and 60 per cent corresponding to
Low Risk, Medium Risk and High Risk areas will be available for all crops (cereals, millets,
pulses and oilseeds and annual commercial / annual horticultural crops) based on
Coefficient of Variation (C.V) in yield of past 10 years' data. However, the insured farmers ofunit area may opt for higher level of indemnity on payment of additional premium based on
actuarial rates.
The Threshold Yield (TY) or Guaranteed Yield for a crop in an Insurance Unit is the moving
average based on past three years’ average yield in case of rice and wheat and five years’
average yield in case of other crops, multiplied by the level of indemnity.
Nature of Coverage and Indemnity
If the ‘Actual Yield’ (AY) per hectare of the insured crop for the defined area [on the basis ofrequisite number of Crop Cutting Experiments (CCEs)] in the insured season, falls short ofthe specified ‘Threshold Yield’ (TY), all the insured farmers growing that crop in the definedarea are deemed to have suffered shortfall in their yield. The Scheme seeks to provide
coverage against such contingency.
‘Indemnity’ is calculated as per the following formula:(Shortfall in Yield / Threshold yield) × Sum Insured for the farmer.
{Shortfall in Yield = Threshold Yield - Actual Yield for the Defined Area}
Once the Yield Data is received from the State / Union Territory Government as per theprescribed cut-off dates, claims are worked out and settled by IA.
Implementing Agencies (IA)An exclusive organization AICIL has been set up for implementation of RKBY. Till AICIL took
over the RKBY, the ‘G.I.C. of India’ used to function as the Implementing Agency.
Reinsurance Cover
Efforts are required to be made by IA to obtain appropriate reinsurance cover for the
proposed RKBY in the international Reinsurance market.
2.4Operational Modalities
A 'State Level Coordination Committee on Crop Insurance' (SLCCCI) is formed in all theimplementing States / UTs for the purpose of overseeing implementation of the Scheme,
headed by the Agricultural Production Commissioner (APC) or by an Official of equal rank ofAgricultural Finance Corporation

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Evaluation of Pilot Weather Based Crop Insurance Scheme

the State and includes senior officers of the State, viz., Secretary (Agriculture), Secretary(Co-operation), Secretary (Finance), Director, Bureau of Statistics and Economics, Registrarof Co-operative Societies, representative of Ministry of Agriculture, Financial Institutions
including NABARD, RBI, State Apex Co-operative Bank, Convener, State Level Bankers’
Committee (SLBC) and Implementing Agency (IA).
Notification
At the beginning of each crop season, the State Government / UT administration notifies the
Crops and Defined Areas to be covered during the season in accordance with the decision
taken at the SLCCCI meeting.
The State Government / UT should notify the smallest possible units as defined areas (i.e.,
insurance units), which is preferably, the Village or the Gram Panchayat. In any case, theStates / UTs will reach the level of Gram Panchayat within three years.
Collection of Proposal and Insurance Charges
The present Nodal Banks system under CCIS continues for NAIS (RKBY) as well, wherein IA
is not required to deal with all the loan disbursing points and instead, deals only withdesignated Nodal points, mostly at district level.
Loanee Farmers (Compulsory Coverage)
For loanee farmers, the modalities will be the same as in the existing CCIS. A farmer opting
for Sum Insured higher than the amount of loan availed by him will be treated at par with
non-loanee farmer and relevant cut-off dates for submitting declarations apply. Further, a
farmer may also insure his crop beyond value of threshold yield up to 150 per cent of
average yield of notified area on payment of premium for balance sum insured at commercial
rates.
In respect of loanee farmers availing sum insured beyond amount of loan availed, the detailsof those farmers availing sum insured beyond value of threshold yield will be furnishedseparately in a schedule in the Declaration.
Non-Loanee Farmers (Optional Coverage)
Those farmers desirous of joining the Scheme fill up Proposal Form of the Scheme andsubmit the same to the village branch of a Commercial Bank (CB) or Regional Rural Bank
(RRB), or PACS (DCCB) with the requisite insurance charge / premium amount after
opening an Account in their name or in an existing Account in their name.
The Nodal Banks in turn submit to the IA the Crop-wise and Notified Area-wise Crop
Insurance Declarations in the prescribed format, (separately for non-loanee farmers) along

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Evaluation of Pilot Weather Based Crop Insurance Schemewith the Insurance Charges / Premium, within the stipulated time.
In respect of optional coverage (non-loanee farmers), the entire amount of insurance
charges / premium on the basis of the proposal of the farmer are deposited with the Branch /PACS within the stipulated dates and in turn, banks consolidate, prepare a Declaration andforward the same to IA with premium.
Receipt of Proposals Directly from Non – Loanee Farmers
On an experimental basis, (subject to infrastructure of IA), non-loanee farmers may submit
proposals personally to IA with requisite insurance charges / premium. The IA will thenconsolidate these proposals and convert them into Declarations. However, it is mandatorythat non-loanee farmers personally submitting proposals to IA should hold a bank account in
the service area / designated bank branch to receive compensation, if any.
Option for Higher Sum Insured
Food Crops and Oilseeds: Sum Insured is worked out by multiplying the threshold yield of
the crop with Minimum Support Price (MSP) or the market price (where MSP is not available)
in respect of previous year. A farmer is eligible to cover up to the value of threshold yield of
the crop at a given premium rate. Additionally, a farmer may extend the sum insured up to
150 per cent of the value of the average yield of the crop on payment of premium at
Commercial (actuarial) rate for the part of the sum insured exceeding value of thresholdyield.
Annual Commercial / Horticultural Crops: Sum insured may extend up to 150 per cent of thevalue of average yield of the crop at commercial (actuarial) rate for the entire sum insured. Incase of Loanee farmers, the minimum sum insured is the amount of loan availed.
Seasonality Discipline - Submission of Declarations by Banks
Loanee Farmers
Banks will send to IA every month consolidated Crop Insurance Declarations in respect ofloan disbursed to the loanee farmers for each crop and each Defined Area. The details of
SAO loans disbursed for insurable crops during a month will be declared to IA in the form ofconsolidated Declarations before the end of the succeeding month. The detailed monthly
cut-off dates in respect of such declarations will be as follows:
Those loanee farmers who would like to avail a sum insured of more than the amount of loan
availed will indicate their choice to the Financial Institutions at the beginning of the season
and for these farmers, the cut-off dates will be those applicable for Non-Loanee farmers.

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Table 10: Monthly Cut –Off Dates for Declarations – Season wise under NAIS
Kharif season
Month ofCut-off date for Receipt of
Loaning
Declarations by IA
April
May
June
July
AugustSeptember
Final

31st May
30th Junest
31 Julyst
31 August30th September
31st Octoberth
30 November

Month of
Loaning

Rabi season
Cut-off date for Receipt ofDeclarations by IA

OctoberNovember
DecemberJanuary
February
March
Final

30th November
31st Decemberst
31 Januaryth
28 / 29th February
31st March
30th April
st
31 MaySource: AIC

Non-Loanee Farmers
The broad Cut-Off dates for receipt of Proposals by the Banks / IA, in respect of thesefarmers will be: (a) Kharif season: 31st July and (b) Rabi season: 31st December
In respect of these farmers, the last date of receipt of the consolidated proposals at IA, will
be one month after the last date for receipt of proposals at the Nodal Branch. However,within these broad parameters suggested above for all categories of farmers, the seasonality
discipline may be modified in consultation with State / UT Government and Government ofIndia, depending on local conditions and crop season.
Important Conditions Applicable for Coverage of Riska)

Loans given for unsown areas will not be covered by the Scheme, because,
indemnity claims will arise under the Scheme, only after the crop has been sown andin the event of crop failure. Mere disbursement of loans by the financial institutions /submission of Proposal by a Non-Loanee farmer will not entitle him for compensation
under the Scheme.

b)

In the areas where crop is sown but, withered away / damaged on account ofadverse seasonal conditions / pest and / or diseases and also where there is no
possibility of reviving the crop, no further loaning should be made by the financing
institutions. Any further loaning in such cases will not be covered by the Scheme.

c)

The Scheme covers notified crops until harvesting stage only. Losses caused tocrops which are spread in the field for drying after cutting / harvesting are excluded
from the scope of the Scheme.

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68Evaluation of Pilot Weather Based Crop Insurance Scheme

Procedure of Settlement of Claims
Once the yield data is received from the State Government as per the cut-off-dates decided,
the claims are worked out as per Declarations received from FIs for each notified area andapproval is obtained. Loss assessment and modified indemnity procedures in case ofoccurrence of localized perils, such as hailstorm, landslide, cyclone and flood wheresettlement of claims will be on individual basis, will be formulated by IA in consultation with
State / U.T. Government. (Important: In case of Loanee farmers’ full amount of loan iseligible for normal rates irrespective of the value of TY and / or AY).
Claims Approval: Claims will be approved by IA. However, the Government may at theiroption, scrutinize / examine a claim falling within their risk liability. Disputed claims / substandard claims, if any, will be referred to a Committee consisting of representatives of
Ministry of Agriculture (GOI), concerned State Government and Insurance Agency.
Settlement / release of claims in the States / UTs which exceed set risk sharing limits of IA
will be subject to receipt of funds from the Government.
Publicity / Awareness and Review
This Scheme requires adequate publicity in all the villages of the notified district. Besides
audio-visual media, the services of Agricultural Extension Officers of the State / UT should
be utilized. It is equally important to train people who are going to be involved in collection ofpremium, processing of Declarations, Proposal forms etc. in banks to avoid any confusion
and misunderstanding. Training programmes and workshops, visit of IA Officers to the banks
will help in clarifying the doubts, redressal of grievances and clearing bottlenecks in smooth
implementation of the Scheme. Pamphlets will be distributed to all villages in participating
States / UTs. A short film covering the salient features of the Scheme will be made by the IA
for this purpose.
2.5 General
The FIs will be paid, service charges @ 2.5 per cent of the premium collected in respect ofboth loanee and non-loanee farmers at the end of the season.
Correct premium rates should be ascertained from the Notification issued by IA and premiumcomputation (Sum insured × Premium rate) should be done accurately. In respect of Smalland Marginal farmers (i.e., farmers with a land holding of less than 2 hectares) only net
premium (full premium less subsidy) need to be remitted. Remission of excess premium will
not entitle for increase in sum insured / liability, at a later date.
Nodal banks must ensure coverage of all crop loans and shall obtain full and accurate
particulars from all the FIs within their jurisdiction. They must ensure coverage of proposals
received from all non-loanee farmers within their jurisdiction as per prescribed cut–off dates.

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Evaluation of Pilot Weather Based Crop Insurance Scheme

Claims under this scheme will be settled only on the basis of yield data furnished by the
Directorate of Economics and Statistics arrived at through regular crop estimation surveys
for production estimates (i.e., planned Crop Cutting Estimates) and not on any other basis
such as Annawari, declaration of drought, declaration of floods, Gazette notification, etc. by
any Department / Authority.
2.6 Working Example of Sum Insured and Premium6
The tables below illustrate how the sum insured and premium are operationalized under the
NAIS.
Table 11: Sum Insured Limits and Premium Rates for Paddy / Rice
= Min. Support Price of RiceState Threshold Yield= State Average Yield
1930 Kg / Ha
2412 Kg / Ha
Rs. 7.35/ Kg
Value of Threshold Yield = Rs. 14200 / Ha
Value of Actual Yield = Rs. 26600 / Ha
Normal (Flat) Premium Rate =2.5 per centActual Premium Rate = 3.55 per cent=

Table 12: Sum Insured and Premium
Categorya)Compulsory
Coverageb) Optional
Coverage - up tovalue ofThreshold Yieldc) Optional
Coverage – up to
150 per cent ofValue of Average
Yield

6

Particulars

Loan Amount

Farmer “A”(Loanee)
(Rs.)
12000

Farmer “B”( Non - Loanee)
(Rs.)
Nil

Full Premium @ 2.5 per cent

300.00Nil

Subsidy 50 per cent of full premium

150.00Nil

Net Premium
Full premium from Rs.12000 to 14200 =Rs.2200@ 2.5 per cent (for Loanee
Farmer)Normal coverage for Non-Loanee farmer

150.00
55.00

Nil
-

-

355.00Subsidy 50 per cent of Full Premium

27.50

177.50Net Premium

27.50

177.50

Full Premium from Rs. 14200 to 26600 =Rs.12400 @ 3.55 per cent440.20440.20Subsidy 50 per cent of Full Premium

220.10220.10Net Premium

220.10220.10Total Net Premium (a + b + c)

397.60397.60Adapted from TNAU’s report on the ‘Evaluation of Crop Insurance in Tamil Nadu’

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Evaluation of Pilot Weather Based Crop Insurance Scheme

Example: Farmer “A”, a loanee farmer and farmer “B”, a non-loanee farmer each own 1
hectare under “Paddy / Rice” cultivation. (Being small and marginal farmers, they are eligiblefor 50 per cent subsidy in premium). Subsidy will vary from season to season.
Table 13: Sum Insured, Premium & Subsidy under NAIS
Particulars

Farmer “A”(Loanee)

Farmer “B”(Non - Loanee)

Amount of Loan

Rs.15,000

Nil

Amount of Coverage

Rs.20,000

Rs.16,000

Applicable PremiumRate

2.5 per cent (Normal Rate) up to Rs.
Rs.15,000.
3.55 per cent (Actuarial Rate) for balanceRs.5,000.
Rs.375.00 at Normal Rate + Rs.177.50 at
Actuarial Rate, i.e., Rs.552.50 in all.

2.5 per cent (Normal Rate) up to Rs.
Rs.14,200
3.55 per cent (Actuarial Rate) for
balance Rs.1, 800.
Rs.355.00 at Normal Rate + Rs.64.00
at Actuarial Rate, i.e., Rs.419.00 inall.
50 per cent of Full Premium,i.e.,
Rs. 209.50.
Rs.209.50

Full Premium
AmountSubsidy
Net Premium
Payable50 per cent of Full Premium,Rs. 276.25.
Rs.276.25

i.e.,

2.7 Performance of NAIS
The crop yield insurance scheme has been largely unsuccessful with low coverage and highclaims to premium ratio. There are problems with both the design and implementation of theschemes. Crop Insurance to be a meaningful policy risk management tool, would have toreach out to a majority of farmers. At present only about 15 percent of farmers and 17
percent of cropped area. The diffusion of this risk management instrument has been slow
and the coverage low in some of the States important from the point-of-view of agriculture.
Product improvement, insurance education & communication, effective insurance delivery
mechanism can contribute to higher levels of penetration.
We discuss the performance of NAIS in this section.
2.7.1 Coverage under NAIS
The details on number of farmers covered under NAIS from 1999 to 2007, cultivated area
covered, sum insured by farmers, premium paid by them, subsidy released by theGovernment , total claims made and the number of farmers benefited during the period
through the scheme are presented in Table 14.
Maharashtra stands first in terms of the number of farmers covered under NAIS from 1999 to2007 with 17.2 per cent of the total number of farmers covered in India followed by Andhra
Pradesh with 15.0 per cent. In terms of claims made, Gujarat topped the list with 24.9 percent of the total claims settled in India followed by Andhra Pradesh with 17.2 per cent. At thenational level, the number of farmers benefited accounted for 25.3 percent of the total

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Evaluation of Pilot Weather Based Crop Insurance Scheme

farmers insured. The shares of claims made to the sum insured during 1999 – 2007 for India
was 8.9 per cent respectively. In 2005-06, the number of farmers covered in India under
NAIS was 167.2 lakhs and this accounted for 13.8 per cent of the total number of farmhouseholds (1208.2 lakhs during 2000-01).
The Working Group constituted by the Planning Commission proposed 2.5 times increase inpenetration by 2012, which appears difficult one to achieve despite multi-dimensional
improvements in the program and delivery mechanism.
Table 14: State wise Coverage of National Agricultural Insurance Scheme(16 Seasons from Rabi 1999-2000 to Kharif 2007)
S.
No.

States

Farmers
covered(‘000s)Area( ‘000 ha)

Sum Insured(Rs. Crores)

Premium
Subsidy
Total Claims(Rs. Crores) (Rs. Crores) (Rs. Crores)

Farmers
Benefited(‘000s)1

A.P

16536.8

25702.1

24486.1

692.0

82.5

1749.33122.0

2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19

Assam
Bihar
Chhattisgarh
Goa
GujaratHaryana
H.P
J&K
Jharkhand
Karnataka
KeralaM.P
Maharashtra
Meghalaya
OrissaRajasthan
SikkimTamil Nadu

20
21

TripuraU.P

93.5
2688.74919.46.2
8342.1
392.9
139.5
18.0
2963.17770.1273.013642.4
18959.5
14.2
8202.49107.2
1.4
1027.3(0.93)
9.7
9724.073.3
3192.410343.2
10.1
19866.3
440.9
97.4
23.9
1403.913316.4
229.836229.1
18765.5
17.0
8426.820394.5
0.8
1659.4(0.94)
6.0
12525.8

91.3
3911.03241.82.2
16066.6
317.7
83.3
13.8
866.0
8803.7344.1
12619.4
10782.0
14.5
7964.58768.6
1.5
1665.5(1.46)
10.1
9945.9

2.1
90.6
84.1
0.0
683.0
10.2
1.9
0.3
22.0
283.6
7.2
395.0
399.8
0.9
200.1
244.9
0.0
36.1
(1.04)
0.3
205.0

0.3
9.9
5.5
0.0
41.9
0.3
0.3
0.0
1.3
17.9
1.5
18.2
46.8
0.2
31.2
4.4
0.0
3.8
(1.25)
0.0
21.8

4.2
518.0
174.6
0.0
2537.3
17.6
6.0
0.1
127.2
1227.619.0
519.8
1007.8
0.3
446.7
765.5
0.0
156.5
(1.54)
0.5
514.8

19.4
821.3
992.6
0.7
3102.0
58.2
70.1
1.4
747.9
3434.255.9
2710.95530.41.4
1526.91859.00.1
270.7(0.97)
2.6
2536.522
23

Uttaranchal
West Bengal

69.6
5535.573.7
2864.2

104.3
4031.6

1.8
104.9

0.2
17.4

5.2
377.7

18.7
1075.324

And & Nic.

1.0

1.5

1.2

0.0

0.0

0.0

0.1

25

Pondicherry
Total

20.7
110458.2

31.1
175695.1

41.2
114177.9

0.8
3466.6

0.1
305.5

1.5
10177.2

3.827962.1

Note: Figures in parenthesis indicate percentages to total,

Source: AIC

There are two conspicuous features of the NAIS performance. The cumulative totals for theseven seasons, Rabi 1999-2000 to Rabi 2002-03, show a claim to premium ratio of 4.27 andGujarat alone accounts for almost 43% of total claims. Among crops, groundnut accounts for
36% of claims and paddy for another 27%. Even in the case of annual commercial andAgricultural Finance Corporation

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Evaluation of Pilot Weather Based Crop Insurance Scheme

horticultural crops, which are supposed to be charged on an actuarial basis, the claim topremium ratio is 2.29. These crops account for 18% of total claims and 34% of total
premium. Food crops and oilseeds have a claim to premium ratio of 5.31.
There are two factors that can help explain the performance of crop yield insurance geographically uniform premiums set below actuarial levels, and no risk sharing by the
implementing agency (Sinha 2007).
Uniform premium below actuarial level
There were two rates under the CCIS and four rates under the NAIS, based on crop
categories, but making no distinction for geographical areas. The schemes do make a
distinction in the level of indemnity. There are three levels of indemnity — 90%, 80%, 60%,
corresponding to low/medium/high risk areas for all crops, based on coefficient of variation inyield of past 10 years of data. These relatively uniform premium rates in the presence ofsignificant differences in yield volatility across different regions and crops can be expected togive rise to the problem of adverse selection.
For example, in case of rice the coefficient of variation of yield per hectare based on last 20
years of data, ranges from 6% for Punjab to 21% for Bihar, with an all India average of 13%.
The difference is more pronounced in the case of groundnut. The coefficient of variation ofyield, calculated using the last 20 years of data varies from 54% for Gujarat to 17% forMaharashtra and Karnataka. The high coefficient of variation for Gujarat may explain thelarge share of Gujarat in the total claims payment. The all India coefficient of variation forgroundnut is 14%. The large coefficient of variation on all India basis for individual crops
implies that even after geographical diversification, significant variability remains at the all
India level for individual crops. Moreover, the coefficient of correlation across crop yields ishigh, indicating limited diversification across crops. This implies the presence of significant
systemic risk in yield variability and the need for reserves or reinsurance for an actuariallybased system. An additional advantage of actuarial rates is that it will facilitate timely
payments of claims. Currently, the payments are delayed because of delays in payments by
the State Governments following claims. With actuarial rates, the implementing agency will
receive premium and subsidies up-front. Of course, the implementing agency will now haveto maintain reserves and factor in the costs of managing such reserves.
No Risk sharing by the Implementing Agency
The second major problem with the current crop insurance system is that the Implementing
Agency (IA) — the GIC till 2002-03 and the Agricultural Insurance Company since April 2003
— has no financial stake in the schemes. They are reimbursed for their administrative
expenses and neither bear any of the risks nor earn any returns from the schemes. The
major task of the implementing agency is the management of financial flows between thegovernments and the financial institutions dealing with farmers.

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The IA also has little incentive and no means to expand crop insurance coverage. It iscompletely dependent on rural financial institutions - co-operative credit societies, regional
rural banks and the rural branches of commercial banks. Co-operative credit societies havebeen playing the leading role in the disbursement of production credit to agriculture
accounting for about 53% of total short-term credit to agriculture in 2001-02. However, theco-operative credit system is suffering from serious problems as pointed out by the Working
Group on Agriculture Credit, Cooperation and Crop Insurance for formulation of the Tenth
Five Year Plan.
While some of the cooperative banks are functioning well, the cooperative credit structure in
general faces wide ranging problems like resource crunch, mismanagement, poor
governance, low level of member participation, lack of credit discipline, high level of erosionof net worth and lack of professionalism in the banks. Moreover, some of the disparities incrop insurance coverage across states may be partly due to the disparities in the functioningof the credit system as pointed out by the Working Group.
The growth of the cooperatives across the country has witnessed regional, sectoral andsectional skewness. There are wide disparities in the performance of credit institutions bothin regard to deposit mobilization and deployment of credit and these are reflected in the
coverage of crop insurance in India which is mandatory for the loanee farmers i.e. those whohave taken sanctioned credit limits for seasonal agricultural operations in a crop season.
The scheme is presently working on an administered rate regime, with direct governmental
financing for claims exceeding the premium and subsidizing the premium of small / marginal
farmers (only to an extent of 10 percent though). The Government’s direct financial support to
NAIS since its inception is given in the Table 15 below:
Table 15: Government’s Direct Financial Support to NAIS
Year

Premium Subsidy(INR millions)

Claims
Subsidy(INR millions)

Total Direct Financial
Support (INR millions)

2000-01
2001-02
2002-03
2003-04
2004-05
2005-06
2006-07
Total

543.41518.96483.58281.88224.54237.69321.71
2611.77

10589.63
3586.2216423.02
9411.92
8083.149339.1812780.80
70213.91

11133.04
4105.18
16906.6
9693.88307.68
9576.87
13102.51
72825.68

Source: AIC

The overwhelming view is in favour of placing the program, in an actuarial regime, in which,the insurance company receives premium based on commercial rates and is responsible forall indemnities.

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Evaluation of Pilot Weather Based Crop Insurance Scheme

Chapter 3
WEATHER INSURANCE IN INDIA
Weather risk is the most significant agricultural production risks although risks of other inputs
such as soil, seeds, fertilizers, and management practices contribute to yield volatility.
Around sixty per cent of variations in yield can be attributed to various weather related
shocks, be it those arising from erratic distribution of the volume and timing of rainfalldeficiency or excess rainfall, high or low temperature, excessive wind speed or high relative
humidity and so on. Farmers in India have relied on traditional risk management strategies
which fail under covariate weather shocks and the existing area yield insurance schemes(NAIS) are marked with sever deformities. Under these circumstances, a financial innovationin the form of weather insurance was introduced in the year 2003 as the ‘index-based rainfall
insurance’. Since then the weather insurance products have promised to overcome thelimitations of the traditional crop insurance scheme and ‘weather proof’ the income streamsof millions of agricultural households. The basic features of index-based weather insurance
contracts have already been discussed in Section 1.1.3 earlier. In this section we discuss thestate of weather insurance in India.
Weather Insurance is an insurance product based on a weather index which provides
insurance for losses arising due to vagaries of weather. These weather indices could be
deficit / excess rainfall, extreme fluctuations of temperature, relative humidity and / or a
combination of above. Detailed correlation analysis is carried out to ascertain the way
weather impacts yields of the crops to arrive at compensation levels. The basic idea is toestimate the percentage deviation in crop output due to adverse deviations in weather
conditions. Hence, it is a financial protection based on the performance of specified index inrelation to a specified trigger.
The general insurance companies have experimented with several weather insurance
schemes for agriculture during the last seven years. As discussed earlier, these products areeasy to administer, are designed taking into consideration the local agro-climatologicalproperties, do not entail long term-liabilities on the governments, are rated based on
actuarial principles and offer high level of flexibility in terms of coverage and indemnity level.
We now discuss all the weather insurance schemes that have been introduced in India tilldate.
3.1Pilot Weather Based Crop Insurance Scheme (WBCIS)
From Kharif 2007 season, a Weather Based Crop Insurance Scheme (WBCIS) has been
piloted across India to explore the effectiveness of Weather Based Crop Insurance as analternative to the National Agricultural Insurance Scheme (NAIS) which is marred with many
limitations as discussed in the previous chapter. WBCIS is intended to provide insurance
protection to the cultivator against adverse weather incidence, such as deficit & excess
rainfall, frost, heat (temperature), relative humidity, etc., which are deemed to adversely

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impact the Rabi crops during its cultivation period. The Scheme shall run as a Pilot in
selected areas, in selected States, for selected crops.
We discuss the main features of the scheme in this section.
Scope
Insurance coverage under the Scheme would be provided for crops raised between October/ November and April (Rabi season). If the cultivation period of a crop does not strictly fallwithin the above season span or crosses over to the next season, in such cases, the sowing
period of the crop shall determine the season in which it has to be categorized.
Area Approach
The Scheme operates on the principle of “Area Approach” in selected notified Reference
Unit Areas. Area Approach signifies that a “Reference Unit Area” shall be considered as a
Unit-Area of Insurance for the purpose of acceptance of risk and assessment ofcompensation as well. Therefore, all insured-cultivators of a Notified Crop in the notified
Reference Unit Area shall be deemed to be on par so far as their terms of insurance
coverage and assessment of compensation are concerned.
Coverage of States
To start with, the Scheme shall be implemented during Rabi 2007-08 seasons in thefollowing States: Bihar, Chhattisgarh, Haryana, Madhya Pradesh, Punjab, and Rajasthan &Uttar Pradesh. Any addition/deletion of States may be considered by the Government ofIndia/ Agriculture Insurance Company of India Ltd. (AIC), based on the willingness and thelevel of preparedness of the concerned State Government.
Geographical Coveragei) “Reference Unit Areas” are linked to specific Reference Weather Stations.
ii) “Reference Weather Stations” are those which are commissioned for providing“Weather Data” for the purpose of assessment of compensation.
iii) “Reference Unit-Area” is a geographical area around a Reference Weather Station,
pre-notified by the State Level Coordination Committee on Crop Insurance i.e.,
SLCCCI, which is deemed to be reflective of the Reference Weather Station’s Weather
Data. To the extent practicable, such Reference Unit-Area shall be restricted to 25 km.
radius around the Reference Weather Station in case of Rainfall, and 100 km. radius incase of other weather parameters like Frost, Heat (Temperature), Relative humidity,etc.

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Evaluation of Pilot Weather Based Crop Insurance Scheme

Crops Covered
Cereals, Millets, Pulses, Oilseeds and annual commercial / horticultural crops grown during
Rabi season The main crops could be wheat, barley, gram, lentil, mustard, potato, onion,
cumin, coriander, fenugreek, isabgol etc. In respect of each Reference Unit Area, the crops
to be covered would be notified by the SLCCCI, whereupon would be referred to as “Notified
Crops”. The crops listed above are only indicative & not exhaustive; any addition/deletion
may be considered by AIC.
Cultivators Eligible for Coverage
All the cultivators (including sharecroppers and tenant cultivators) growing any Notified Cropin any Reference Unit Area shall be eligible for coverage. The Scheme shall be:
1. Compulsory: For All LOANEE APPLICANT CULTIVATORS i.e. those who have
Sanctioned Credit Limit from a Financial Institution [FI] for a Notified Crop in a
Reference Unit Area.
2. Voluntary: For NON-LOANEE CULTIVATORS i.e. those who do not have
Sanctioned Credit Limit from any FI for a Notified Crop in a Reference Unit Area.
Financial Institution, for the purpose of the Scheme, includes all District Central Cooperative
Banks and also the PACS affiliated to them, all Commercial Banks and all Regional Rural
Banks, as defined in National Agriculture Insurance Scheme (NAIS).
Perils Covered
Deficit Rains, Un-seasonal / Excess Rains, Frost, Heat (Temperature), Relative humidity, etc
are the weather perils, which are deemed to cause “Adverse Weather Incidence”, leading tocrop loss, would be covered under the Scheme: The specific “Adverse Weather Incidence”
with its timing / duration applicable to a particular Notified Crop shall be notified by the
SLCCCI. The perils listed above are only indicative & not exhaustive; any addition/ deletion
may be considered by AIC, based on the availability of relevant data. For the purpose of thisScheme, “Adverse Weather Incidence” would be as defined hereinafter.
Risk Period (Insurance Period)
Risk period would be from “Sowing Period” to “Maturity” of the crop. Risk period, dependingon the duration of the crop and the weather parameters chosen, could vary with individual
crop and Reference Unit Area, and would be notified by the SLCCCI before thecommencement of risk period.

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Seasonality Discipline
Risk (insurance coverage) would be accepted till commencement of the risk period. No
insurance coverage is allowed once the risk period begins.
Coverage Procedurei) Insurance coverage of Loanee Applicant Cultivators shall be through the existing
network of Financial Institutions [FI] at the grass-root level, using Nodal Bank system asin National Agricultural Insurance Scheme [NAIS].
ii)

Insurance coverage of Non-Loanee Cultivators shall be through the existing network of
FIs at the grass-root level; the Insurance Intermediaries and Authorized Representativesof AIC.

iii)

Nodal Bank Branches shall be paid by AIC, a Service Charge of 5% on the actualPremium amount remitted by them, being in the nature of sharing the incidental
management expenses incurred by them for servicing the Scheme.

iv) Insurance Intermediaries engaged by AIC for covering non-loanee cultivators would be
paid remuneration at an appropriate percentage on the actual amount of premium
collected through them from the farmers.
Sum Insured
Sum Insured is broadly equivalent to the ‘cost of cultivation’ and is pre-declared by AIC andnotified in the Notification. The Sum Insured for an individual cultivator shall be the product of
the cultivator’s declared ‘area under cultivation’ (in hectare) for that Notified Crop and the
Sum Insured per hectare as mentioned in the Notification. ‘Area under cultivation’ would
always be expressed in terms of ‘hectare’. An individual Cultivator’s ‘area under cultivation’(in hectare) for a Notified Crop in a notified Reference Unit Area shall be declared by him as
follows:i) Loanee Applicant Cultivators: ‘Area under cultivation’ for the Notified Crop as alreadydeclared by him in the Loan Application Form for the purpose of fixing his “Maximum
Borrowing Limit [MBL]” by the Lending FI.
ii)

Non-Loanee Cultivators The cultivator shall declare the ‘area under cultivation’ for each
Notified Crop in the Insurance Proposal Form.

Premium Rates
Actuarial Premium Rates for each season for each Notified Crop and each notified
Reference Unit Area shall be calculated by AIC using standard Premium Rating

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methodology and the same shall be declared in the Notification before commencement of
the season which shall be binding on all.
Premium Sharing & Subsidy
Of the total Premium, while a part thereof shall be payable by the insured cultivator, and thebalance shall be borne by the Central Government and State Government on 50:50 basis
and paid up-front to AIC as Premium Subsidy. It is clarified that, as far as AIC is concerned,
risk incepts only upon receiving of FULL PREMIUM by it as detailed hereunder. The detailed
structure of Premium Payable by the Insured is show in Table 16 below.
Table 16: Premium Slabs for WBCIS
Food Crops & Oilseeds

Crops Premium Payable By
Insured

Wheat

1.5%
or
Actuarial
whichever is less
2.0%
orActuarial
whichever is less

Other crops (other Cereals, Millets, Pulses, &Oilseeds)
Annual Commercial/Horticultural Crops
Sl. Premium Slab1
2

Up to 2%>2 – 5%3>5 – 8%

4

>8%

Rate,
Rate,

Subsidy
No Subsidy
25%, subject to minimum net
Premium of 2.00% payable byfarmer40%, subject to minimum net
Premium of 3.75% payable byfarmer50%, subject to minimum net
Premium
of
4.80%
and
maximum net premium of 6%
payable by farmer

Compensation (Payout)a. AIC shall be responsible for all Payouts arising out of “Adverse Weather Incidence”
strictly in terms of the Scheme terms & conditions read with the relevant Premium &Payout tables. However, this responsibility of AIC shall attach only when the ‘Risk has
incepted’; that is, AIC has duly received the FULL PREMIUM, directly from the Insured
his own part, AND ALSO the corresponding Premium Subsidy part from theGovernments.
b. Pay-out shall arise ONLY in case of Adverse Weather Incidence. Adverse Weather
Incidence is equivalent to the deviation between “Trigger Weather” and “Actual
Weather” Data recorded at a “Reference Weather Station” during the specified timeperiod. Trigger Weather is a pre-defined Weather Parameter applicable to a Notified

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Evaluation of Pilot Weather Based Crop Insurance Scheme

Crop in a notified Reference Unit Area. In case of Adverse Weather Incidence (AWI),
all the insured cultivators growing the Notified Crop in the Reference Unit Area shall bedeemed to have suffered the same level of AWI and the same proportion of crop-loss,
and become eligible for the same rate of Payouts.
c. Pay-Out Disbursement
 Pay-outs would normally be made by AIC to the Nodal Banks within 45 days ofclosing of insurance period subject to receipt of the necessary actual weather
data.
 So far as the Insured is concerned, the Pay-out procedure shall be automatic; thatis to say, Payouts would be automatically computed by AIC on the basis of Actual
Weather Data received, and the Payout would be automatically credited to theInsured’s Bank Account through the concerned Nodal Bank /FI.
 Franchise up to 5% would be applied while processing the Payouts – whichmeans, any actual Payout which is less than 5% of the Sum Insured shall not be
paid-out. The franchise clause is factored-in while calculating the Actuarial
Premium
3.1.1. Operational Modalities
The operational modalities are the guidelines for administering the scheme and shall holdjoint validity with the scheme and subject to amendment if & when deemed fit.
1)

Scheme acceptance by the State Governmenti)

CONSENT for Scheme – The “Consent” must be conveyed to GOI and AIC in writing,which shall imply the acceptance of all the provisions in totality as laid out in the
Scheme & its Operational Modalities, as well as other guidelines issued from time to
time.

ii)

Nodal Department of State Government – The State Govt. shall designate a
Department, may be the same Department looking after implementation of NAIS, for the
purpose of implementation of the Scheme.

iii)

Authorization to SLCCCI – In the States/UTs already implementing the NAIS, the same
SLCCCI may be authorized to oversee the implementation of the Scheme on theirbehalf, in addition to NAIS.

iv)

Composition of SLCCCI – If deemed necessary for the purposes of the Scheme, thepresent composition of SLCCCI, may be augmented by the inclusion of representatives
from the State Agriculture University and the “India Meteorological Department [IMD]”.

v) Constitution of SLCCCI – In the States/UTs not implementing NAIS at present, the
SLCCCI should be constituted on the pattern suggested above.

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2) Selection of Crops & Reference Unit Areasa)

At least 2 months in advance of the season, the State Government shall submit to AIC,
a proposed list of Crops, and corresponding to each, the list of Reference Unit Areas tobe notified for the season. This submission must be supported by:•Historical Weather Data (in the daily format) in respect of each Reference Weather
Station (RWS) at least for the past 25 years, which may be facilitated by them fromthe Data sources.

•

For first-time Notifications, 10 years’ Yield Data for the similar administrative unit
Area.

b)

AIC shall recommend the list to SLCCCI after verifying the sufficiency of the Data
submitted.

c)Thereafter, the SLCCCI shall notify the Reference Unit Areas and the Crops
corresponding to each Reference Unit Areas.

3)

Notification for a Seasoni) The Notification shall be issued by SLCCCI on behalf of the State Government and shallact as the Specific Guidelines for implementation of the Scheme in the State during aparticular season.
ii) The State Government shall circulate the Notification to all concerned Departments, aswell as the State Head Offices of all the concerned Institutions/Organizations that havebeen assigned a role in the administration of the Scheme in the State.
iii) The Notification shall be binding on all the Agencies concerned with the administering of
the Scheme. It shall be the duty of the recipient Offices to circulate the Notification down
the line, well in time for proper implementation of the Scheme in that season.
iv) The Notification shall include, inter-alia, the following:• The Declaration of implementation of the Pilot Scheme in the State during a particularseason.
• The List of Notified Crops [NC] and their corresponding Reference Unit Areas [RUA].
• Source of Current Season Weather Data, i.e. Reference Weather Station (RWS) forthe purpose of Compensation-computation.
• Back-Up Weather Station [BWS] corresponding to each RWS.
• Seasonality Discipline (i.e. Cut-off Dates) applicable to the State and/or Reference Unit
Areas [RUA].
• Premium Rates and the applicable Premium Subsidy for each Notified Crop in every
Reference Unit Area.
• Payout structure (i.e. Benefit Table) for each Notified Crop in every Reference Unit
Area.

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Evaluation of Pilot Weather Based Crop Insurance Scheme• Any other directive or guideline specific to the State.
v) However, in case of any conflict between the “Scheme/Operational Modalities” and the“Notification”, the former shall prevail.

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4)

Insurance Coveragei.

Loanee Applicant Cultivator [LAC]

S. No.

Action Point

Action By

Elaborations(a)Loan application for a Notified
crop in a notified RUASanction of the Credit Limit

Cultivator

In accordance with the Banking rules andprocedures.
As per Bank’s policies and rules

(b)
(c)
(d)

Computation of Sum Insured
Debit of the Premium against
the sanctioned Credit Limit(e)

Remittanceofcollective
Premium to the Nodal Bank
BranchRemittance of consolidated
Premium to AIC

(f)

ii.

(c)
(d)

(e)

iii.

Nodal
Branch ofBank

As intimated by AIC/ as per Notification
Compulsory and automatic within cut-off date of
Risk Acceptance period as mentioned in the
Notification.
The Premium amount must accompany insured“Cultivators’ List” for identification purpose andmust reach the Nodal Bank within 7 days.
Premium draft must accompany LAC Declaration
Form duly filled in and signed, along with insured“Cultivators’ List” and must reach AIC within 15
days of cut-off date of Risk Acceptance period.

Non-Loanee Cultivator [NLC] – Through Banking Network

S. No.
(a)

(b)

Bank
–
Loan
disbursing
BranchAction Point
Proposal in the prescribed
Form for a Notified crop in a
notified Reference Unit
Area
Computation of Sum
InsuredDeposit of the Premiumwith the Bank Branch
Remittance of collective
Premium to the Nodal
BranchAction By
Cultivator

Remittance of consolidated
premium to AIC

Nodal Branch of
Bank

Bank Branch
Cultivator
Bank Branch

Elaborations
In accordance with the Scheme rules
and procedures to be submitted within
the Risk Acceptance Period as
mentioned in the Notification.
As per notification / intimation from AIC
The Cultivator must have an account inthat Bank Branch.
The Premium amount must accompany
insured“Cultivators’
List”
for
identification purpose and must reach
the Nodal Bank within 7 days.
Premium draft must accompany NLC
Declaration Form duly filled in andsigned, along with “Cultivators’ List” andmust reach AIC within 15 days of cutoff
date of Risk Acceptance period.

Non-Loanee Cultivator [NLC] – Through Insurance Intermediaries

In this case, the Insurance Intermediary shall act as a guide and facilitator for the NLC: He shall advise the NLC as to the benefits and necessity of the Scheme.

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Evaluation of Pilot Weather Based Crop Insurance Scheme He shall guide the NLC through the procedures; filling up the necessary forms andcollect the requisite Premium.
 He shall remit the consolidated Premium to AIC accompanied by the individual
Proposal Forms and summary details in NLC Declarations.
5)

Weather Data Submission

Sl.

Action Point

Action By

Elaborations(a)Historic
WeatherData
in respect of each
proposed RUA
Current Season
Weather
Data[CSWD]

IMD, Third-Party(facilitated by State
Govt.)

Well before the start of a season, to be considered
for Notification

IMD, State Govt.,
Third-Party

As far as possible, Reference Weather
Station should be an area can be serviceable by a
Weather Station, i.e. Tehsil / Block

(b)

6)

Compensation (Payout)

S.
No.
(a)

(b)
(c)
(d)

(e)
(f)

Action Point
Calculation of Adverse
Deviation in Weather
Parameters
Computation of Payout
Rate
Computation of Payout
AmountRemittance of Payout
Cheque / Demand Draft to
the Nodal Bank Branch
Onward Remittance of
Payout to the root Branches
Credit of individual
Compensation amount to thebeneficiaries’ accountAction By

Elaborations

AIC

AIC
AIC

NC-wise, in each Reference Unit
Area
Nodal Branch-wise, for each BankAIC

To be accompanied by payout details

Nodal Branch of
Bank
Grass Root /Lending Branchesof the Banks /Intermediary

To be accompanied by individual
cultivator’s payout details
Mandatory display of Payout detailsof the cultivators on the Notice Board

Note: The liability of AIC shall cease upon remitting the Payout amount to the Nodal Bank with the corresponding details

7)

Review of the Scheme

The Pilot Scheme would be reviewed by AIC/GoI from time to time and suitable
modifications / improvements would be considered.
Roles of Various Agencies/ Institutions/ Govt. Departments/ Committees
For the successful implementation and administration of the Scheme, the roles of the various
Agencies/Institutions/Government Departments/ Committees are spelt out herein.

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Evaluation of Pilot Weather Based Crop Insurance Scheme

Government of India•

Liaison with the State Governments for giving their “CONSENT” for the Scheme andissue necessary directives from time to time for overall implementation of the
Scheme.

•Issue Directives to all Financial Institutions [FIs] through “Reserve Bank of India[RBI]” and “National Bank for Agriculture and Rural Development [NABARD]”, forsmooth and effective implementation of the terms and conditions of the Scheme andits Operational Modalities.

•

Liaison with India Meteorological Department [IMD] and other alternative Weather
Data Sources to facilitate providing of Weather Data on near real-time basis to AIC.

•

Facilitate providing of necessary Agronomic inputs from the Ministry of Agricultureand “Indian Agricultural Research Institute [IARI]” to AIC, as necessary for finalizing
the Insurance structure, fixing the Payout Triggers, etc.

•

Pay to AIC its contribution of the projected Premium Subsidy at the beginning of
every crop season, based on fair estimates submitted by AIC and settle the balanceof actual Premium Subsidy for the season, based on submission of seasonal
Premium & Subsidy figures by AIC to enable them release the Payouts, if any, to the
Nodal Banks.

•

Consider the Review Report of AIC, as and when submitted, and also consider themodifications/improvements recommended therein by AIC.

•

Create extensive Awareness and Publicity of the Scheme amongst the rural
population through its own channels.

State Government•

Give its “CONSENT” for the Scheme in writing to GOI and AIC.

•

Designate a Department which is connected to Crop Insurance activity in the State,to act as the Nodal Department for the purpose of the Scheme.

•

Authorize the SLCCCI to oversee the implementation of the Scheme on its behalf, inaddition to NAIS. The existing composition of SLCCCI may be augmented suitably.
In the States/UTs not implementing NAIS at present, the SLCCCI should beconstituted.

•

Issue necessary Directives to all Agencies/Institutions/Government. Departments/
Committees involved in the implementation of the Scheme, conveying its
acceptance of the Scheme; the authorization/constitution of SLCCCI to act on its
behalf; and acceptance of provisions of the Scheme and the Operational Modalities.

•

Submit to AIC at least 2 months in advance of a season, a proposed list of Crops,
and the list of RUAs to be notified for the season. The requisite supporting data mustalso be submitted.

•

Liaison with State Offices of India Meteorological Department [IMD] and otheralternative Weather Data Sources to facilitate providing of Weather Data on nearreal-time basis to AIC.

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Evaluation of Pilot Weather Based Crop Insurance Scheme•

Facilitate providing of necessary Agronomic inputs from the Directorate ofAgriculture and the State Agricultural University to AIC, as necessary for finalizing
the Insurance Contract structure, fixing the Payout Triggers, etc.

•

Pay to AIC its contribution of the projected Premium Subsidy at the beginning of
every crop season, based on fair estimates submitted by AIC and settle the balanceof actual Premium Subsidy for the season, based on submission of seasonal
Premium & Subsidy figures by AIC to enable them release the Payouts, if any, to the
Nodal Banks.

•

Set up Monitoring and Review Committees as may be required from time to time tomonitor and review the performance of the Scheme.

•

Create extensive Awareness and Publicity of the Scheme amongst the rural
population through its Agriculture and Extension Departments.

State Level Co-Ordination Committee on Crop Insurance [SLCCCI]•Act on behalf of the State Government for the implementation of the Scheme in theState.

•

Issue necessary Directives to all Financial Institutions [FIs] through the State Officesof “Reserve Bank of India [RBI]” and “National Bank for Agriculture and Rural
Development [NABARD]”, for smooth and effective implementation of provisions of
the Scheme and its Operational Modalities.

•

Before the commencement of each season, issue the SCHEME NOTIFICATION as
per the time-schedule prescribed. The Notification must be sent to ALL
Agencies/Institutions/Government. Departments/Committees involved in theimplementation of the Scheme.

•

Act as the Referee Forum to resolve issues arising in connection with
implementation of the Scheme within the purview of the Scheme and its Operational
Modalities.

•

Review progress of the Scheme and issue necessary corrective or improvement
directives through its Notification.

Financial Institutions [FI]•All FIs shall continue with the existing system of Nodal Bank-Branches under“National Agricultural Insurance Scheme [NAIS]” to service the Scheme. In the States
not implementing NAIS, the Nodal Bank-Branches system shall be created by the FIs
with due consultation with AIC.

•The Nodal Bank-Branches of each FI would act as the dealing point for its
subordinate Branches within its jurisdictional area. These Nodal Bank-Branches andtheir jurisdictional area shall be designated by their Head / Controlling Office), andinformed to AIC.

•

Commercial Banks would consider designating Nodal Bank-Branches at the level of
one or a cluster of Districts. Preferably, the controlling Branch in that area may bedesignated as the Nodal Bank-Branch.

Agricultural Finance Corporation86Evaluation of Pilot Weather Based Crop Insurance Scheme•

Cooperative Banks may designate District Central Cooperative Banks [DCCB] as
Nodal Bank-Branch.

•

Regional Rural Banks [RRB] may designate their Head Office as Nodal Bank-Branch.

•

The Nodal Bank-Branch shall be responsible for discharging their assigned roles
under the Scheme on behalf of its FI in its designated jurisdictional area.

•

Notifications, as well as other directives, guidelines, etc., shall flow in the channel of
SLCCCI/AIC - Nodal Bank-Branch - Service (subordinate) Bank Branches/ PACs.
Premium and Compensations remittance to and from AIC shall also follow the samechannel.

The functions of the Nodal Bank-Branch would, inter-alia, include:•

Communicate Notifications, as well as other directives, guidelines, etc. to all Service(subordinate) Bank Branches within their jurisdictional area.

•

Ensure that the Lending Branches within their jurisdictional area sanction additionalloan component to the loanee cultivator (whose loan has already been sanctioned)
towards Premium payable by him.

•

Ensure that, for both Loanee Applicant Cultivators and Non Loanee Cultivators
separately, Premium and related data is remitted to the Nodal Bank-Branch withintime, and payouts received are credited into the Beneficiary Accounts within 7 daysof receiving. Further to ensure that the payout details are displayed on the Noticeboards of the Service Branches.

•

Submit to AIC (within time), separately for both Loanee Applicant Cultivators and Non
Loanee Cultivators, Notified Crop-wise, Reference Unit Area-wise Declarations in theprescribed format, along with the consolidated Premium payable as per the same.
Further to remit to Service (subordinate) Bank Branches Compensation amounts anddetails, to be credited to the Beneficiary Accounts there.

•

Allow AIC access to all relevant Records and Registers at the offices of the Nodal
Bank Branch and Service (subordinate) Bank Branches within their jurisdictional
area.

Under the administrative mechanism, the FIs are designated as the terminal service points
for the Cultivators. Hence, it is their duty to ensure compulsory coverage of all eligible
Loanee Applicant Cultivators and all desirous Non Loanee Cultivators. In case of any lapse
on the part of any FI to provide this service to the Cultivators, any deprivation arising there
from to the Cultivator shall be made good by that FI.
Agriculture Insurance Company of India Limited [AIC]1)

Administrative Agency & insurer of the Scheme entrusted with the role of running the
Schemeincoordination
withtheGovernments
andAgencies/
Institutions/Committees involved in the implementation of the Scheme.

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Evaluation of Pilot Weather Based Crop Insurance Scheme

2)

Actuarial Premium Rates – Preparation, Underwriting – responsibilities of processing
and acceptance of risk.

3)

Compensation – responsibility for processing, approval of Payout.

4)

Co-insurance – creating a Pool with other Insurance companies, if required forsharing the risk.

5)

Re-insurance – negotiating arrangements in the international market.

6)

Awareness and Publicity – extensive efforts to create awareness and generate
publicity for the Scheme at the grass-roots level. Efforts especially to enhance
confidence levels in the Scheme through interactive clarification of its features andOperational Modalities.

7)

Database – developing Crop-yield and Weather databases, as also related agriinsurance databases.

8)

Information sharing – providing Returns/Statistics to the Governments.

9)

Review – of the Pilot Scheme and recommendation of improvements to the Govt. ofIndia.

Cultivators•

Loanee Applicant Cultivators [LAC] – COMPULSORY Coverage. All LACs who havebeen sanctioned Credit Limit for any NC in any RUA should insist on insurance
coverage as per the provisions of the Scheme.

•

Non-Loanee Cultivators [NLC] – VOLUNTARY Coverage. All NLCs desirous oftaking insurance coverage under the Scheme for any Notified Crop in any Reference
Unit Area may approach the nearest Bank Branch/Primary Agricultural Cooperative
Society [PACS] / Insurance Intermediary within the cut-off date. The Non-Loanee
Cultivator should have an Account in that Bank Branch/PACS.
 The Non-Loanee Cultivator must insist on getting the form and service from the
Bank Branch/Intermediary. He must fill-up the Proposal form completely in theprescribed format. If asked for, he must provide documentary evidence regarding
hisinsurableinterest
incultivatingtheland/crop(e.g.
ownership/tenancy/cultivation rights) proposed for insurance.
 The Non-Loanee Cultivator must submit the form and deposit requisite Premium
to the Bank Branch / Insurance Intermediary.
 The NLC, in his own interest may keep abreast of the developments from the
Notice Board of the Bank Branch regarding compensation.

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Chapter 4
EVALUATION STUDY ON WBCIS
4.1Terms of Reference
The terms of reference of the study are:i)

To study the scope of the cover under weather insurance in terms of –a) perils coveredb) basis riskc) design of product to appropriately and adequately capture the riskii)

To compare the overall effectiveness of Weather Insurance vis-à-vis National
Agricultural Insurance Scheme (NAIS) in terms of the scope, benefits, transparency,
settlement of claims, etc. in the light of 4 seasons’ of implementation of WBCIS as apilot.

iii)

To examine and review the Weather Insurance and Weather Station density in thecontext of minimizing basis risk.

iv)

To assess the reliability & accuracy of weather data provided through automaticweather stations, particularly private data providers.

v)

To examine the scope for improving weather data system, data collection and data
availability.

vi)

To study and examine whether Weather Insurance could be a substitute for NAIS in
the long run.

vii)

To examine whether Weather Insurance and NAIS could operate simultaneouslywith an option for farmers to choose either of them.

viii)

To examine the possibilities for integrating Weather Insurance with NAIS (likeadvance payouts, double trigger products, etc.).

ix)

To analyze and compare the features of Weather Insurance products offered byvarious insurance companies including ‘bench-marking’ standards; and offeringsuggestions for improvement in the scheme.

x)

To assess the impact and usefulness of private sector participation in the scheme byproviding competitive environment & underwriting practices followed by them tosatisfy the basic requirements of verifying insurable interests of the proposer in thesubject matter of insurance.

xi)

To examine the issues involved in scaling-up Weather Based Crop Insurance
Scheme (WBCIS).

xii)

To examine the efficacy & effectiveness of Weather Insurance in mitigating
weathers risks at micro level (farmers), meso (banks, input suppliers, intermediariesetc.) and macro level (relief agencies, Government etc.)

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To review and suggest the legal and regulatory environment for index insurance,
particularly weather insurance.

4.2 Universe of the Study
The study was conducted in 2 Districts each of 4 States [2 States each from Kharif and Rabi]
where the Pilot Weather Based Crop Insurance Scheme (WBCIS) was implemented. For the
purpose of meaningful evaluation, comparison & analysis, another 2 States [1 State eachfrom Kharif and Rabi] were selected where the Pilot WBCIS was not implemented, to provide
some counterfactuals (though limited given the non-randomized nature of generating thesecounterfactuals) by serving as a Control Group. In order to capture the situation of states
which have WBCIS partially or have witnessed other initiatives in Weather Based Crop
Insurance, 2 States were added to the universe of the study.
4.3 Sample Size for Questionnaire Survey
A sample of 1000 farmers who availed Weather Insurance from the implementing insurancecompanies in the selected States comprise of the beneficiary group for the field survey.
Further, 200 non-beneficiary farmers i.e. those who did not avail of the Weather Insurance
Scheme were chosen from both implementing and non-implementing states, to serve as aControl Group for comparative analysis and purposive evaluation. In addition, 75 farmers[from 2 selected States where the Weather Based Crop Insurance Scheme (WBCIS) was
not implemented but where weather insurance has been tried through other pilots] wereselected for detailed interaction to ascertain their views/experiences regarding weather
insurance.
4.4 Profile of Farmer Sample
The sampling procedure made sure the respondent farmers were a representative group
comprising of beneficiary and non-beneficiary farmers drawn from various socio-economic
categories [SC/ST/OBC/Women/General, Small Farmer/Marginal Farmer/Large Farmer,etc.]. The cropping patterns of the study regions was also be taken into account during
sample selection based on feedback from agricultural experts and our partnerintermediaries.

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4.5 Sampling Methodology
The study was conducted on the set parameters applied for conducting socio-economic
impact evaluation process. A multistage, systematic and purposive sampling methodology
was adopted for the study, as detailed below:a)Selection of States: The first stage sampling unit is States. The study was
conducted in 4 States [2 States each from Kharif and Rabi] where the Pilot Weather
Based Crop Insurance Scheme (WBCIS) was implemented. The States wereselected, in consultation with the Ministry of Agriculture, in relation to the number offarmers covered under the scheme both in Kharif and Rabi as well as the extent ofcoverage under various crops. It was also ensured that the States so selected arefrom different Agro-climatic Zones for a fair assessment of the performance of thescheme and to arrive at meaningful conclusions.
For the purpose of comparison & analysis, another 2 States [1 State each from Kharif
and Rabi] were selected where the Pilot WBCIS was not implemented, to serve as aControl Group. The Control Group States so selected were from different agroclimatic Zones.

b)

Selection of Districts: At the second stage, 2 Districts were selected from eachState where the Pilot Weather Based Crop Insurance Scheme (WBCIS) was
implemented for at least two consecutive seasons [i.e. 8 districts in all]. The Districts
were selected where the participation of farmers under the scheme has been the
maximum.
In addition, 1 District each from the Control Group States where the Pilot WBCIS was
not implemented [i.e. 2 Districts in total] were selected for interaction with farmers
and other stakeholders to know their perspective regarding the scheme and its
prospects, if implemented.

c)

Selection of Beneficiary/Non-beneficiary Farmers: A representative group of1000 beneficiary farmers comprising of 250 farmers from each of the 4 sample
States [125 farmers per district] was selected for a detailed interaction/interview. For
this purpose, an agency-wise, crop-wise and season-wise list of the beneficiary
farmers was compiled for each selected district. The farmers, thereafter, weregrouped category-wise i.e. Marginal Farmers, Small Farmers, Large Farmers,
Women Farmers and SC/ST farmers. From each category, beneficiary farmers wereselected on a random basis to make up the requisite number.
Similarly, 200 non-beneficiary farmers i.e. those who did not avail of the Weather
Insurance Scheme comprising of 50 farmers each from the 2 sample States where
the scheme was implemented [25 farmers per district] and 2 sample states where thescheme has not been implemented, were chosen for comparative analysis. It was

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also ascertained from the non-beneficiary farmers i.e. control group farmers as towhy they did not prefer to obtain weather insurance cover if they were aware about
the scheme.
In addition, 75 farmers [from 2 selected States where the Weather Based Crop
Insurance Scheme (WBCIS) was not implemented but where weather insurance has
been tried through other pilots] were selected on a random basis for detailed
interaction to ascertain their views/experiences regarding weather insurance.
4.6 Selection of Other Stakeholdersi)Banks: A total of 6 Banks [2 Banks each from Commercial Banks, Regional Rural
Banks and Cooperative Banks] which implemented the Pilot Weather Based Crop
Insurance Scheme (WBCIS), in the Sampled States were selected.

ii)Intermediaries: A total of 20 intermediaries [16 MFIs/NGOs/Experts and 4 Insurance
Brokers] who are key stakeholders in the value chain of WBCIS were selected for
detailed interaction.

iii) Technical Agencies: The Indian Agricultural Research Institute, New Delhi and 4
State Agricultural Universities [1 from each Sampled State] were covered for detailed
interaction and feedback with regard to the Scheme and its operation.
4.7Study Tools
The Study was designed to start with preliminary discussions with the officials of Departmentof Agriculture in the Ministry of Agriculture, GoI and with the Agriculture Ministries in thesampled states.
The farmer survey provides the basic feedback for impact assessment and evaluation. The
survey schedules have, therefore, been so designed as to lead to meaningful data
particularly on well defined quantitative parameters relating to effective delivery of intended
benefits, equity in extension of these benefits among different social and economic
categories of farmers as also gender-equity. Separate survey schedules have been
designed for beneficiary and non-beneficiary farmers keeping in view the terms of referenceof the study and the key indicators for impact assessment.
The schedules were customized to capture information and data relating to, inter-alia,
awareness of the weather insurance scheme, knowledge of various products on offer by
insurance companies, satisfaction/acceptance level, opinion of farmers on premium rates
and subsidy, claim payouts (if any) received by them, timeliness in receipt of payouts,
transparency in scheme operation, knowledge of weather station and its location, perception
about the reliability & accuracy of weather data, extent of support from agencies at the field

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level, extent of impact on farm income, opinion regarding NAIS, constraints/problems faced
in obtaining insurance cover, scope/suggestions for improvement in the scheme etc. The
field surveys commenced upon receipt of approval of the study schedules/questionnaires by
the clients and field testing thereof.
Schedules for the inter-active phase with various stakeholders were in the form of Structured
Check Lists and Open-ended Questionnaire for each category of interviewees/agencies.
Besides the primary data and information collected from the survey, the in depth social
auditing of the scheme was undertaken by involving all the stakeholders’ viz. policy planners,
agricultural universities, researchers, insurance intermediaries / voluntary agencies,
insurance companies, farmers’ organizations/interest groups, beneficiary and nonbeneficiary farmers, etc.
Relevant information from the State Government and implementing insurance companies
was obtained on financial and physical performance of the Weather Insurance Scheme(crop-wise, season-wise and year-wise). Besides, the constraints and problems faced in theimplementation process were collected and collated through key informant’s check-lists andinterview guides. Thus, the study tools for this important evaluation exercise include:
i)
ii)
iii)
iv)
v)
vi)
vii)
viii)

Farmer Beneficiary Schedule
Farmer Non-Beneficiary Schedule (i.e. Control Farmer Schedule)
Farmer Schedule for Non-Implementing States
State Schedule
Schedule for Implementing Banks
Schedule for Implementing Insurance Companies
Schedule for Technical Agencies (IARI & SAUs)
Schedule for Insurance Intermediaries (MFIs / NGOs / Insurance Brokers)

4.8 Field Survey, Data Collation and Analysis
The study team comprising of insurance experts and research associates undertook visits tosampled States/Districts for interaction with concerned officials of the State Government,
Insurance Companies, Implementing Banks, State Agricultural Universities, Insurance
Intermediaries and other stakeholders. The field survey was carried out by trained research
associates and supervisors under the close guidance of the Core Team. The primary andsecondary data collected from the field as well as different stakeholders was collated andanalyzed using statistical analytical tools and inferences drawn thereon. By using statistical
tools like multivariate analysis, correlation analysis, regression and test of hypothesis for thedifferences between farmer categories, namely beneficiary and non-beneficiary, SC/ST andgeneral, small and marginal farmers vs. large farmers, etc. significant inferences were drawn
on the basis of the statistical evidence and the critical hypothesis were tested. An attempt
was made to understand the specific impacts of the programme in terms of expected
outcomes. This has enabled us in identifying dimensions of efficiency and effectivenessparameters of the programme.

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Chapter 5
KEY FINDINGS FROM PRIMARY AND SECONDARY RESEARCH
5.1 Summary of Findings from Primary Research with Farmers
The following spider chart (REFERENCE CHART 1) summarizes the key findings from theprimary data collected from farmer beneficiaries of WBCIS. This chart will be used further in
the next chapter for validating the conclusions emerging from the primary research with
farmers.
REFERENCE CHART 1
The following spider chart indicates the corresponding percentage of respondents of a given
category who are not satisfied on various aspects (16 in our case) related to WBCIS. The
categories of respondents have been taken as overall sample, small and marginal farmers,
non-loanee farmers, and graduate farmers. These categories can be expected to represent
a judicious balance of preferred farmers, demanding farmers and informed farmers.

Based on the spider chart above, the aspects of WBCIS with the maximum ‘not satisfied’
respondents are indicated below. The values in parentheses denote the percentage of ‘notsatisfied’ respondents averaged across the four categories:

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1.

Location of Weather Station (80.8)

2.

Mechanisms for Grievance Redress (56.5)

3.

Convenience in Enrollment (56.5)

4.

Resolution of Queries (53.3)

5.

Responsiveness of Intermediary (45.3)

Considering the above five aspects with the highest level of ‘not satisfied’ respondents, it canbe easily seen that the first aspect deals with basis risk while the remaining four aspects
deal with service delivery and convenience.
Referring again to above spider chart, the aspects of WBCIS with the minimum ‘not satisfied’
respondents are indicated below. The values in parentheses denote the percentage of ‘notsatisfied’ respondents averaged across the four categories:
1. Reliability of Weather Data (16.8)
2. Protection Tool against Crop Losses and Climate Change (17.3)
3. Effective against Political Risk and Manipulation (19.3)
4. Weather as Basis for Crop Insurance (20.8)
5. Usefulness as Alternative to NAIS (25.0)
Considering the above five aspects with the lowest level of ‘not satisfied’ respondents, it canbe easily seen that the first and third aspects deal with transparency and reliability of
WBCIS, the second and fourth aspect deal with the protection ability of weather insurance
while the fifth aspects deals with its usefulness as an alternative to NAIS.
For the sake of completeness, it would be helpful to know the aspects of WBCIS that lie at
the middle of the spectrum of ‘not satisfied’ respondents. Out of the 16 aspects of WBCIS on
which satisfaction of farmer beneficiaries are sought, the following six define the mid-range
responses. The values in parentheses denote the percentage of ‘not satisfied’ respondents
averaged across the four categories:
1. Quantum of Sum Assured (44.3)
2. Explanation on WBCIS Policy (37.3)
3. Time Delay in Claim Settlement (33.8)
4. Period of Risk Coverage (29.8)
5. Types of Risks Covered (26.8)
6. Design of WBCIS Policy (25.3)

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5.2Summary of Findings from Interactions with Other Key Stakeholders
Primary research with other key stakeholders in the crop insurance sector has provided
critical insights for improving the effectiveness of crop insurance schemes in India. Detailed
interactions were conducted with a wide spectrum of stakeholders including state agencies,
intermediaries, insurers, social sector organizations, research institutions and subject matter
experts. The main points from interactions with other key stakeholders are systematicallysummarized in the following section.
Issue

Responses from Other Key Stakeholders• Unanimous view that weather insurance cannot be affordable forsmall and marginal farmers without subsidy• Lack of willingness to pay exists among medium large farmers evenwhen they may have the ability to pay for weather insurance• Since WBCIS is like a social good targeted at the farming community,
the service tax applicable on it is not justified. Affordability of WBCIS
can improve significantly with the withdrawal of service tax

Affordability of
Weather Insurance• Premium rates in excess of 10% are simply not justifiable unlessfarmers are sensitized about viewing crop insurance as an essential
input in agricultural production• Many experts pointed out the covariant nature of weather and a hostof loadings on the pure risk premiums as the key reasons for highactuarial premiums• Reinsurers have been identified as the key stakeholders that play animportant role in determining the pricing for insurers who directly
underwrite the risk
• Premium subsidy under WBCIS has helped weather insurance comeat par with NAIS (area-yield insurance) in affordability• Huge gaps exist in explaining the working of WBCIS policies to clientswith low educational attainment and limited exposure to sophisticatedfinancial products• Practitioners have decried the inadequate investments towards
sensitizing farmers and field sales personnel about policy
mechanisms and limitations

Information
Availability & Ease of
Enrollment• The lack of system for systematic resolution of farmers’ queries has
been widely recognized. This is believed to cause disinterest among
farmers which gets heightened if questions regarding highpremiums/pricing of weather insurance are not dealt properly• Awareness among the channel partners and facilitators, includingbanks, is also not at an expert level, making WBCIS productcommunication a big challenge• Substantial scope exists for usage of ICT and other technologies toimprove the product communication and enrollment process

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Evaluation of Pilot Weather Based Crop Insurance Scheme• Financial literacy and insurance education is important forsustainability of agricultural insurance schemes• WBCIS pilots are very important for reaching out to a larger section of
the farming community which is either unaware of the NAIS or has had
unsatisfactory/bitter experience with it• NAIS has to be ideally employed for covering low frequency, extreme
severity loss incidences which can be easily detected• Coverage of Non-Loanee Cultivators has to be incentivized throughhigher commissions/margins for intermediaries• Universal coverage of entire farming community, at least for extreme
loss events, should be attempted within the next 3 years• Simple/plain vanilla weather insurance policies need to be clearly
differentiated from more sophisticated/exotic policies through proper
branding and product communication
Coverage• Gaps in terms of coverage of Rabi crops and other horticultural andhigh value commercial crops demand attention• Horticulture and livestock insurance are important insurance products
for agriculture whose penetration is very low. These should
complement crop insurance or weather insurance• Agricultural labourers should be offered rainfall insurance by AIC, as
the income of these workers depends considerably on rainfall. Initiallysuch coverage may be channelized through reliable intermediaries
and can be scaled up after successful trials• Maximum sum assured under weather insurance needs
special/immediate attention as farmers can hardly ever get the
maximum sum assured indicated for each phase of their policy• States in order to lower the financial incidence of premium subsidy are
generally allowing sum insured, which is much below the production
cost. Farmers do feel the need for higher sum insured to reflect theincrease in production cost components (labour, seeds,
agrochemicals, technology, value-added inputs etc).

• The agriculture insurance sector should strive at introducing
simple/plain vanilla products under WBCIS which farmers, state
Government officials, and functionaries at the grass root levelshould be able to evaluate objectively

Product Design• More technically complex weather insurance products should
be offered to farmers as options under which they can choose
the level of risk coverage based on risk exposure and ability topay• Weather parameters, other than rainfall and temperature, need
to be included in weather insurance policies to provide
credence to underlying weather insurance indices.
• Weather insurance modeling should integrate agrometeorological and agronomic information. Current state ofproduct development calls for more concerted inter-disciplinary
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integrated research on crop-weather modeling for weather
insurance should be supported which involves expertise fromrelevant disciplines like agronomy, agro-meteorology, financial
economics and statistics.
• Crop data generation for a given location should be in tandemwith weather data collection.
Product Design• It has been commonly observed that almost identical covers arebeing sold for a variety of crops. This disregards the specificagro-meteorological requirements for a crop. Further, it raises
doubts on the ability of weather insurance to realistically reflect
the impact of a particular weather parameter on crop production• Other parameters need to be gradually incorporated into
weather insurance, but having multiple perils covered under asingle weather insurance product would complicate the matterfor farmers and other key stakeholders. Most insurers also
resist drastic changes in product design as marketing agentsneed to be retrained regularly, thus escalating theadministrative costs

Settlement Process• Repeat take-ups of weather insurance have been suffering due
to the claims not being declared timely. Data from weather
stations, especially those managed by IMD, is delayed on mostoccasions without due/prior notice. This leads to unrest by
farmers and breeds skepticism in farmers, particularly in light ofthe promise for reduced settlement period under weather
insurance• Payout experiences lead to spurt in sales and repeatedpatronage in benefited locations and proximate areas whichhave earlier experienced low or no payouts• All stakeholders acknowledged unanimously that a standardized
protocol for response of insurance intermediaries on farmer
grievances and resolution of doubts of farmer-clients regarding
claim settlement is conspicuous by its absence. Lack of proper
grievance redress and query resolution pulls down the credibility
and promise of weather insurance.

Weather as Basisfor Crop Insurance
and Key
Infrastructure
Required• From field research and pilots, the location of the weather
station for claim settlement has come out as the most importantfactor for farmer-clients to believe weather insurance astrustworthy• The density of AWS and IMD observatories holds the key tobetter pricing of risk products with passage of time and enabling
the introduction of weather insurance based on otherparameters

Agricultural Finance Corporation98Evaluation of Pilot Weather Based Crop Insurance Scheme• Weather infrastructure should be enhanced on high priority as
any investment towards generation of weather data should be
looked upon as an investment in public good with substantial
payoffs in terms of ability to design more robust products inmedium-long run. In the short term, it will substantially benefit
the credibility of WBCIS due to more representative weather
stations• Services of Private, Third-Party Weather Data Providers are
critical for authentic and timely settlement of claims in many
areas and in situations where no other alternative is available

Weather as Basisfor Crop Insurance
and Key
Infrastructure
Required• Some farmers do entertain suspicion that the data provided byprivate / third-party data providers may not be accurate, andhence believe that more of public weather stations are required,instead of private weather stations.
• Warehouse of daily rainfall data for weather insurance is also
very important for disaster management as well as weather
advisory service.
• 5-10 km radius should be the limiting boundary for underwriting
weather insurance.
• The location of weather station for claim settlement has to be
representative of the loss exposure of all the underlying farmer
clients. Therefore it has to be finalized in consonance with the
distribution of farmer clients covered under an insurance
contract• Due to the high flexibility of location, proven reliability andtimeliness of data supply by third-party weather stations, they
are gaining increasing ground for Weather Insurance pilots. The
agreements with Third-Party Weather Data providers have to be
fairly meticulous for ensuring better returns from installation of
farmer-location-specific weather stations• The best way to reduce basis risk is by intensifying the networkof rainfall measurement and monitoring systems. This network
can double up as an important weather and crop advisory
platform in the areas where weather insurance is sold as well.
IMD stations for weather insurance pilot may not be entirely
reflective of the rainfall pattern in the insured areas.
Appreciating this fact, AIC has attempted to underwrite rainfall
insurance for some projects based on data from other
Government weather stations managed by agricultural
universities, KVKs, GSDMA, irrigation deptt. etc.

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Evaluation of Pilot Weather Based Crop Insurance Scheme• On the important question of whether doing away with NAIS
makes sense, the unanimous response has been in thenegative• Comprehensive coverage of losses is the key advantage of
NAIS which in case of WBCIS is its faster claim settlement• NAIS suits low frequency and extreme severity losses. WBCIS
can be attuned to medium frequency and medium severity
losses• On the idea of having NAIS and WBCIS available as separate
options, the consensus was that having only one option madesense from practical considerations. It would be chaotic for
Relationshipfarmers and implementing agencies to deal with both cobetween WBCIS andexisting. The major problems can be:
NAIS
i. When there are claims under one and not under the other,farmers would most likely complain and a major hue and cry
will occur. Administrative issues will also erupt.
ii. Wide variations in premiums by way of the concessional
subsidy have already created problems in farmers adopting
insurance. While premium subsidy for NAIS is being
gradually reduced, WBCIS is being supported by aconsiderable proportion of subsidyiii. Another important difference between the two schemes
making the substitutability unreasonable is the difference inthe payout frequencies and magnitudes of WBCIS and NAIS•

Horticultural and specialized crops cannot be covered under
NAIS due to lack of standardized yield database. WBCIS canact as an effective risk management solution for these crops
which face specific weather perils needing specialized risk
coverage•

WBCIS is the comfortable option from official point of view
given its lower administrative hassles and objective claim
settlement. From the farmers perspective it is not that
beneficial given NAIS’s comprehensive coverage against
multiple perils•

Ideally farmers should be given the option of choosing
between NAIS and WBCIS. Given the low awareness andlimited understanding of crop insurance working amongmajority of farmers, it will be prudent to delay the transfer ofchoice to farmers. The perceptions of farmers, based on theirexperiences under crop insurance, have a significant
influence on their overall opinion regarding the farmer-welfare
schemes of the Government

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5.3Key Inputs from Interactions with Insurers
5.3.1 Key Inputs from Interaction with AIC•

Pilots of WBCIS by state governments have contributed much-needed scale toweather insurance in India. Perceived advantages of WBCIS include lessadministrative burden, reasonably speedy claim settlement and objective assessmentof claims•

States with lower level of perceived risks in crop production due to assured irrigation
are also susceptible to certain risks like unseasonal/excess rains, unfavorable
temperature rise•

Some of the states where Government has not been supporting WBCIS possibly
because of its weak financial position, have also seen interest for weather insurance
from farmers, mainly those undertaking cultivation of cash crops, horticultural crops or
seeds

•

Private agribusiness companies and corporate entities promoting contract farming are
likely to play an instrumental role in making inroads for weather insurance in suchstates•Facilitators like Sajjata Sangh, Microensure, WRMS etc can also likely to play a vitalrole in scaling up of weather insurance in India•

Weather insurance has been used for sales promotion by seed companies. After its
successful trial for 2-3 years, it could become an essential component of seedpackages from leading seed companies
From the farmers’ perspective, weather insurance is not considered ‘comprehensive’insurance, and hence not a substitute for NAIS w.r.t. many crops.

••

Owing to the technical limitations of weather insurance products, addressing suchdemands requires hybrid or double-trigger crop insurance products. One of the moreworkable alternatives could be to break-up the total sum assured under crop
insurance into two equal components: the first component (50%) will be settled on thebasis of weather-based index whereas the second component (50%) will be settled
on the basis of area yields•

The component of crop insurance requiring area yields can take estimates of area
yield from both CCEs and Remote-Sensing (RS) technology. The dependence on theestimates from RS technology can be gradually increased with improvement in its
resolution and accuracy•

Differentiation in WBCIS can happen through improved service, convenience inenrollment, timely settlement and greater transparency•

Farmers are not equipped to understand the nuances of weather insurance productdesign. Benchmarking of weather insurance product, therefore, is vital as more andAgricultural Finance Corporation

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more insurers are allowed to participate in the Government supported WBCIS. This
would help in realizing the best value for the Government funds•

Government has to envisage and assign the proper role for crop insurance in India.
To provide further impetus to crop insurance, other types of ad-hoc relief andagricultural support provided to farmers can be routed through the more efficient routeof crop insurance•

Spanish model of crop insurance can provide useful directions to Indian crop
insurance programme, particularly in terms of shaping the structure of public-private
partnerships and involving different stakeholders as per their specific strengths,
interests and requirements•

More focused efforts and investments may be directed towards development oftechnical and research competencies for improvement of weather insurance•

To optimize basis risk in weather insurance for plain areas, the coverage of insured
farmers for rainfall parameter must be limited to 5 km. The corresponding coveragefor temperature or other weather parameters may be relaxed to 10 to 15 km. For
undulating areas or areas with microclimates, the above limits need to be suitably
reduced.

General Suggestions from AIC•

Agriculture Insurance Law: Agriculture insurance is specialty insurance, anddifferent from traditional general insurance in many respects. As an illustration,
agriculture insurance, particularly crop insurance programme is conceived as a‘multiple-agency’ approach in which Rural Financial Institutions (RFIs), State
Government, Central Government etc. are actively involved, with the Government
providing significant financial support. Moreover the programme is compulsory forloanee farmers. The programme, thus, is seen more as a social instrument of theGovernment rather than a commercial instrument. A programme of this nature andmagnitude is unlikely to be effectively administered unless backed by a statute or law.
It may be worthwhile to note that the countries like United States of America, Canada,
Spain, Japan, Philippines etc. where crop insurance is being used as an integral partof ‘agriculture risk management’, a separate act / enactment is in force, andfacilitating smooth implementation of the programme.

•

Incentives for Sustainable Agriculture Practices: While subsidies are must in
agriculture insurance, it would be equally important to build risk management
stipulations and incentives into the programme for sustainable agricultural practices,like integrated pest management, low Green House Gases (GHG) crops, drought
proofing, etc.

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5.3.2 Key Inputs from Interaction with ICICI Lombard•

ICICI Lombard has implemented new features to gain the trust and confidence of
farmers who have traditionally believed that their money is safe only with the public
insurance companies. Integrating proposal form and cover note in one document toissue insurance policy on spot, has been one such feature. Some other salientfeatures include usage of internet platform for policy purchase and issue, different
distribution strategies for different geographies, direct person-to-person training for
new sales personnel etc.

•

WBCIS has emerged as a good proposition for our business. In order to meet thegrowing demand under WBCIS, ICICI Lombard can seamlessly employ more people
for marketing WBCIS to interested farmers•

ICICI Lombard employs a mix of distribution strategy based on geography. Its sales
force goes to the specific geography and identifies the influencers (lead farmers,
panchayat representatives, grassroots organizations, banks etc) to effectively reachout to the local farmers and bring under the coverage of WBCIS•ICICI Lombard has implemented a novel method for on-the-spot enrollment in WBCIS
using a Point-of-Sale (POS) application. This method can go a long way in eliminatingintermediation delays and other loopholes in current distribution process of WBCIS
while reducing transaction costs for channel partners. After piloting this unique system
during Rabi 2009-10 season, ICICI Lombard plans to replicate it across the country by
Rabi 2010-11 season. It has many other advantages like secure, online data-sharing
between insurer and channel partners, real-time update on changes by insurer,
instant issue of policy etc.

•

Weather stations have to be properly mapped and their numbers have to be
increased as per the local needs. Quality and formats of weather data must be
standardized•

Premium subsidy from Government payable to ICICI Lombard under WBCIS is routed
through AIC. This circuitous route through a competing insurance company can be
obviated to streamline the funds flow and minimize transaction costs for concerned
parties. Timely receipt of premium subsidy is not only necessary for timely claim
settlement but also for meeting the regulatory requirements under Section 64 VB ofthe Insurance Act, 1938•

Government should also try to ensure equal opportunity for all insurers participating in
WBCIS. Since state governments themselves assess and verify insurance products to
be introduced under WBCIS, they should place equal confidence in private and public
insurers•

For ensuring sincere and sustained efforts for promotion of WBCIS, all new insurers
applying for inclusion in WBCIS must be subject to achievement of certain criticalbusiness size in selling non-subsidized weather insurance. The investment made in

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attaining the critical business size for commercial weather insurance would help the
new insurer to understand and assimilate the key requirements for promoting WBCIS
effectively•

Despite the interest of farmers, some states do not promote crop insurance becauseof the paucity of funds. Some states undertake crop insurance pilots as a goodwill
gesture rather than as a initial step for a strategic programme for agricultural
production risk management•

Service delivery is a key differentiating factor in crop insurance; even more due to its
nature of a social good•

Crop insurance models of US and Spain are excellent models to draw insights for the
public-private partnership model to strengthen crop insurance in India•

Government support in the form of premium subsidy is indispensable for WBCIS toattain widespread penetration in India. The caps on actuarial premiums and premiumsubsidy have to be hiked up for providing satisfactory coverage to cultivators in areas
prone to high and frequent risk exposure

5.3.3 Key Inputs from Interaction with IFFCO-Tokio•

For IFFCO-Tokio, the farmer is not only a client for crop insurance but also ashareholder. Therefore it becomes difficult to sell WBCIS policies to such farmers
unless they understand and accept the limitations of WBCIS. Unlike the case of otherinsurers, IFFCO Tokio provides a forum for farmers where they can stand up andmake their case if they are dissatisfied with their experience of WBCIS•IFFCO-Tokio takes utmost care in ensuring that the farmers buying WBCIS are fully
aware of the basis risk in it. A fair proportion of farmers under the scope of WBCIS
coverage by IFFCO-Tokio understand the difference between the loss estimated at
the weather station and the actual loss in the fields. With growth in weather station
density and improvement in product design, IFFCO-Tokio is hopeful that it would be
pitch WBCIS more effectively to the farmers associated with IFFCO - one of its
promoter organizations•

For 2010-11 season, IFFCO-Tokio is targeting 4 states aggressively which should
help in increasing its total weather insurance portfolio from 70,000 farmers to
1,25,000 farmers. Most of the existing clients of IFFCO-Tokio are non-loanee
cultivators•

IFFCO-Tokio supports the idea of entry barriers for new insurers seeking participation
in WBCIS. Unless new insurers understand the unique challenges in selling weather
insurance, they would not be able to sell WBCIS policies reliably and sustainably•

Government should promote the development of a centralized data centre for WBCIS
and other requirements in agricultural extension, research and development. Weather

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data from such centralized data centre should be priced reasonably to give thrust to
product development and research in weather insurance•

In order to further bring down the cost of crop insurance for farmers, WBCIS policies
should be exempted from service tax. Government has already waived service tax formany policies focused at rural and poor segments of the society•

IFFCO-Tokio finds merit in collaborative possibilities under WBCIS. These include
standardized product for a given location, data sharing among insurers, and collective
research for weather insurance product and services improvement•

Local reinsurance capacity within India has to be increased to counter the dominationand skewed control of international reinsurers•

IMD is not able to match up with the increasing requirements of insurers, particularly
after rapid expansion of WBCIS coverage. The certification of private weather stations
by IMD has to be encouraged by Government for promoting public-private partnershipin weather data management

5.4Findings from Secondary Data Provided by Insurers
Based on the secondary data on business performance of WBCIS provided by the three
insurers, namely Agriculture Insurance Company of India (AIC), ICICI Lombard General
Insurance Company (ICICI Lombard) and IFFCO-Tokio General Insurance Company(IFFCO-Tokio), this section analyzes the patterns of coverage, claims scenario, benefits
accruing to farmers and growth of the weather insurance business in India, mainly under
WBCIS.
5.4.1

Farmers Insured

Table 17 indicates the coverage of farmers by the three main insurers participating in
WBCIS. AIC is the leader in providing weather based crop insurance in India followed by
ICICI Lombard. IFFCO-Tokio has a marginal share of the market for weather based crop
insurance having started late in 2008 with a meager coverage of only 200 farmers. There
has been a phenomenal rise in the farmers insured by AIC under WBCIS, from around44000 in Kharif 2007 to over 1.65 lakh farmers in the Kharif 2008 season. The biggest jumpin coverage occurred in the Kharif 2009 agricultural season with AIC’s policies covering
more than 11 lakh farmers across India. Though coverage in the earlier Rabi seasons was
low at an absolute level compared to Kharif coverage, AIC has consolidated its Kharif 2009
performance with more than 8.5 lakh insured farmers during Rabi 2009-10 season.
ICICI-Lombard’s coverage of farmers is not as spectacular as that of AIC, though theirmarketing efforts saw a rise in the number of farmers insured from around 18,000 in Rabi
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Lombard for their moderate growth during the initial seasons of WBCIS has been the time
taken by them to gain the confidence of Government Agencies responsible for WBCIS in
their respective states. Kharif coverage by ICICI Lombard commenced in the year 2008 withinsurance of 18000 farmers under WBCIS. In spite of the significant efforts of AIC and ICICI
Lombard to increase coverage under WBCIS, the cumulative coverage attained in Kharif
2009 was less than 12 lakh farmers, which indicates the wide schism between the farmers
yet insured and those yet to be insured for an agrarian community of India’s size anddiversity.
Table 17: Farmers Insured under WBCISAICICICI Lombard

IFFCO-Tokio--

Kharif 2007

43790

Rabi 2007-08

6271677468

Kharif 2008

16519918359

13

Rabi 2008-09

16997323229

435

Kharif 2009

1133975

26642

Rabi 2009-10

869829245384-194-

5.4.2 Farmers Benefited
A very interesting aspect of the number of farmers benefiting from the WBCIS by way ofclaims paid out is that AIC leads in this aspect by leaps and bounds in comparison to its
private counterparts. In Kharif 2009 around 80 per cent of the farmers insured received
payouts while for ICICI Lombard this figure stood around 50 per cent. None of the farmers
insured by IFFCO-Tokio were benefited. This becomes a significant indicator of the value
farmers would ascribe to WBCIS or weather insurance product because Kharif 2009 saw
country-wide droughts or drought like situations, and those farmers who do not receive
payouts in a drought year start devaluing the need and utility of weather based crop
insurance, irrespective of the superiority of product design. Table 18 depicts the farmers
benefited in various seasons. It is evident that AIC has maintained reasonable level ofclaims attuned to the extant crop conditions.
Table 18: Farmers Benefited under WBCISAICICICI Lombard

IFFCO-Tokio--

Kharif 2007

35275

Rabi 2007-08

1877904937

Kharif 2008

1044834479

13

Rabi 2008-09

112001

8343

433

Kharif 2009

88821013539

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5.4.3 Area Insured (in Hectares)
In congruity with the higher coverage by AIC, the Area Insured (in hectares) is alsosignificantly higher than ICICI Lombard and IFFCO-Tokio. In Kharif 2009, AIC covered
slightly less than 15 lakh hectares of crops in India under WBCIS compared to around one
lakh hectares covered by ICICI-Lombard and a paltry 120 hectares by IFFCO-TOKIO (Table
19). The corresponding coverage of area insured under WBCIS in Rabi season has been
erratic for AIC (falling from slightly less than 10 lakh hectares in Rabi 2007-08 to below twolakh hectare mark in Rabi 2008-09 and then rising to more than 12 lakh hectares in Rabi
2009-10). The growth in season-wise area coverage of ICICI Lombard had been more
uniform till Rabi 2009-10 season when it rose phenomenally to more than 6 lakh hectares,
an increase of more than 8.5 times over the previous Rabi season. The remarkable growth
in area insured by both the leading insurers (under WBCIS) is an evidence of the thrust
received by weather insurance in India in the form of Government support to WBCIS.
Table 19: Area Insured under WBCIS (in Hectare)
AIC

ICICI Lombard

IFFCO-Tokio--

Kharif 2007

50075

Rabi 2007-08

98455333701

Kharif 2008

17865542537

12Rabi 2008-09

19282072433

185

Kharif 2009

1460977104989118

Rabi 2009-10

1228094637653--5.4.4 Total Premiums (in Lakh INR)
In terms of the premium collections, AIC takes up the largest chunk of premium collections
pie owing to its higher share of farmers insured. During Kharif 2009 season, the total
premium collected by ICICI Lombard is less than ten per cent of the total premium collectedby AIC while the premium collection of IFFCO-Tokio is abysmally low at Rs.3 lakh. ICICI
Lombard has managed to narrow the gap in WBCIS premiums between it and AIC byreaching a premium level of almost 50% of the premiums earned by AIC during Rabi 200910.
An interesting piece of evidence that is revealed by glancing through Table 20 is the higher
per farmer premium earned by ICICI-Lombard on an average. For instance, in Kharif 2009
the premium collected per farmer (i.e. Total Premium Collected divided by Total Number ofFarmers Insured) by ICICI Lombard stood at around Rs.5400 while it was Rs.1750 for AIC
and Rs. 1550 for IFFCO-Tokio. This is on account of the higher value of average area
insured per farmer for ICICI Lombard which means that it is able to insure a larger acreage
from each farmer whom it is able to bring under its coverage.

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Table 20: Total Premium Income under WBCIS (in Lakh INR)
AIC
Kharif 2007

703.1

Rabi 2007-08

13845.1

ICICI Lombard

IFFCO-Tokio

-

325.1

-

Kharif 2008

3168

494.1

0.3

Rabi 2008-09

3589.91091.6

3.6

Kharif 2009

19894.51440

3Rabi 2009-10

16115

7994

-

5.4.5 Total Claims (in Lakh INR)
The claims data from the three insurers shows a skewed distribution. AIC has the highest
average claims ratio (claims to premium collected) vis-à-vis ICICI Lombard and IFFCOTOKIO (Table 21). In Kharif 2009 which was the worst year (over the last three years ofevaluation) in terms of moisture stress (water stress arising out of drought or drought likesituations), the claims to premium ratio for AIC was at a high level of 81 per cent compared
to 36 per cent for ICICI Lombard and zero per cent for IFFCO-TOKIO. During the Rabi 200910 season, ICICI Lombard has made up for its Kharif 2009 aberration through a higher
claims ratio of 66%, which though lower than the corresponding 82% claims ratio for AIC,
indicates the ability of ICICI Lombard to pay out large quantum of claims in seasons with
substantially high risk coverage.
The differences in the claims ratio for the three insurers may point out the need for adifferential subsidy regime. An insurer with high claims ratio (high probability of pay outs andlower premium) when compared with another with lower claims ratio (lower probability of pay
outs and higher premium) deserves a higher dosage of price and non-price subsidies. By
homogenizing the premium subsidies, the take-up of the products having poor claims ratio
might be incentivized to the same extent as the one having better payout probability (and atlower premium). This might bring in a higher than proportional share of profits to the insurer
having lower claims ratio over the future cropping season which is detrimental to farmers’
welfare.
Table 21: Total Claims Made under WBCIS (in Lakh INR)AICICICI Lombard

IFFCO-Tokio--

Kharif 2007

524.1

Rabi 2007-08

10071.5

148.2

Kharif 2008

1439.8165.1

0.3

Rabi 2008-09

2610.2730.1

1.2

Kharif 2009

16126.6

475

Rabi 2009-10

13367

5285

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5.4.6 Growth of Weather Insurance Portfolio of AIC and ICICI Lombard
The discussion on the performance of WBCIS so far reveals significant differences among
the three insurers and brings out huge gaps in coverage both in terms of number of farmers
covered or total acreage (across crops and regions). Though AIC emerges as the best
performing insurer in terms of its ‘value for money’ weather based crop insurance coverage
provided to the Indian farmers, it should not be forgotten that it was pioneering experiment of
ICICI Lombard in association with the World Bank and BASIX that saw the launch of the firstindex-based weather insurance scheme, which forms the core rationale for having WBCIS in
the first place.
Table 22 shows the growth of the weather insurance portfolio of the two major weather
insurance providers in India. As discussed earlier, ICICI Lombard started its weather
insurance business in the year 2003-04 by way of its collaboration with BASIX, while AIC
introduced weather insurance in its agricultural insurance portfolio in the following year
(2004-05). It was only in the year 2005-06 that AIC’s weather insurance business (in termsof acreage insured) overtook that of ICICI Lombard. The marketing year 2007-08 saw aspectacular jump in the coverage by AIC, crossing the 25 lakh acres mark, only to fall below
the total coverage of 10 lakh acres during the next year. On the contrary ICICI Lombard
continued to grow in a modest manner during the period, though at a substantially lower
level compared to AIC. Section 6.4 discusses at length the indicators of growth in theweather insurance business in India. In the 2009-10 marketing year, both the insurers
registered an almost parallel growth in their coverage performance owing to aggressive
marketing campaigns and increased state support. While AIC exceeded 65 lakh acres
insured land mark, ICICI Lombard crossed the 10 lakh acres barrier for the first time, ending
up at slightly more than 20 lakh acres. The cumulative coverage of the two insurers
exceeded 85 lakh acres during 2009-10 which is more than six (6) times the total coverageof slightly less than 15 lakh acres during the previous year (2008-09). It would not be anoverstatement to aver that WBCIS has lent a new lease of life to weather insurance in India.
A short discussion on this aspect of growth of weather insurance portfolio is also taken up in
Section 6.4.2 in the next chapter.

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Table 22: Weather Insurance Coverage of AIC & ICICI Lombard (in Acres)Marketing Year
2003-04
2004-05

AIC

7-

ICICI Lombard
1054

5545

5736

2005-06

247026107586

2006-07142048125185

2007-08258657087002

2008-09

9286884589602009-106722677

2002477

5.4.7 Other Observations
The keen interest of the insurers and their sincere efforts in supporting the Government for
promotion of crop insurance have certainly given boost to WBCIS in India. Under the WBCIS
regime so far, the financial support of the Government has been well complemented by afairly lenient regulatory environment; regarded conducive during the early stages of a new
product or service. The result of favorable collaboration between Government and insurers isevident in the impressive leaps, in terms of farmer and area coverage under WBCIS.
Despite the presence of only three active insurers, the competitive landscape under WBCIS
is not placid or less fiercely contested than it would, if there were higher number ofcompeting insurers. With due regard to the inherent strengths of each other, both AIC andICICI Lombard have indicated loopholes and weakness in the institutional design andprocess control of WBCIS as an enabling factor behind the quantum leap of their maincompetitor in terms of key business parameters under WBCIS. Even disregarding theinnuendoes of the leading insurers, it is quite obvious that the relatively flexible stipulations
related to underwriting and process evaluation under WBCIS, need to be reviewed rigorously
and tightened, if required.
While AIC has drawn rave reviews from its channel partners (both WBCIS and Non-WBCIS
portfolio) by virtue of its open and empowering approach, particularly during weather
insurance product development; ICICI Lombard has demonstrated excellent responsivenessand transparency in sharing all vital data, related to its business statistics. This tops up theremarkable initiatives undertaken by ICICI Lombard for reducing inefficiencies due tointermediation and streamlining distribution. Some of the measures introduced by ICICI
Lombard for improving customer-centric processes like enrollment, distribution,communication etc. and ensuring better operational control may be relevant for replication
under the umbrella programme of WBCIS. Despite business statistics for WBCIS notstrongly supporting its cause, IFFCO Tokio displayed admirable clarity on how it intends to
7

Portfolio for AIC in terms of Total Area Insured from 2007-08 onwards has been equated to Total Area
Insured by AIC under WBCIS. The area insured under Non-WBCIS portfolio of AIC has been ignored becauseof lack of reliable data.

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scale up its WBCIS coverage and the crucial groundwork necessary for it before its quest for
scaling up its weather insurance portfolio. The understanding and commitment of IFFCOTokio team regarding weather insurance could be regarded to be in good harmony with thebasic goals and orientation of WBCIS.

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Chapter 6
EMERGING SCENARIO AND KEY CONCLUSIONS
The multi-pronged and detailed field research for evaluation of WBCIS has thrown up avariety of perceptions, experiences, judgments, projections and perspectives that haveenriched the evaluation exercise. In terms of product life cycle, weather insurance (WI) canbe regarded as still going through its introduction stage or in terms of the diffusion of theinnovation, it is still in a stage of infancy.
Chart 1: Product Life Cycle Curve

Both the supply and demand sides for this product are evolving with the supply side currently
being at a higher level of understanding and sophistication compared to the demand side.
Equally important, if not more important, the viewpoints of non-transactional actors (entities
influencing the transactions between the supply and the demand side) have to be carefullyinterpreted as these actors have a balanced association with both supply and demand side
entities and are usually endowed with a utilitarian orientation. The non-transactional actors
mainly include welfare entities like Agencies and Organs of the State, Civil Society
Organizations (Social/Voluntary Organizations, Scientific and Research Institutions, Media
Bodies etc) and non-partisan individuals (volunteers/independent experts). However, theresponses of a diverse set of stakeholders in the value chain of WI, including bothtransactional and non-transactional entities, are heterogeneous and intermingled withappreciation, criticism, skepticism, hopes and pragmatism. A systematic analysis of thedifferent aspects related to the design and delivery of WBCIS scheme is presented in thefollowing sections.

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6.1Scope of Weather Insurance
6.1.1 Perils Covered
Indications from Farmer Beneficiaries of WBCISThe satisfaction of farmer beneficiaries of WBCIS with the perils covered in weather
insurance is illustrated in the following chart. Less than one-third of respondents from theentire sample are not satisfied with the perils covered under their WBCIS policy. More than
three-fourth (76%) of the respondents from OBC category (constituting almost half (49%) of
the entire sample) have expressed a sense of satisfaction with the coverage of perils.
Though nearly 45% of respondents from the general category are not satisfied with the set of
perils covered in WBCIS policies, this aspect of WBCIS does not figure among those 8
aspects of WBCIS (out of a total of 16 aspects) having the highest percentage of ‘notsatisfied’ responses, on the basis on Reference Chart 1. Therefore the concern among
farmer beneficiaries regarding the coverage of perils in WBCIS policies may be regarded asmoderate and should be addressed in the medium to long term.
Chart 2: Response on Coverage of Perils under WBCIS

Indications from Other Key Stakeholders (Insurers, Specialists, Scientific Community)
Increasing the number of perils in a WBCIS is not a constraint for insurers as they havedemonstrated in case of policies for horticultural crops which require risk coverage for more
complex weather events and other weather parameters in addition to rainfall andtemperature. As is the usual case elsewhere, the quality of coverage under a peril is more
important than the number of perils being covered. The issue of insufficient coverage of
perils has not come out as a significant concern during interactions with other key
stakeholders.

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6.1.2 Basis Risk
Indications from Farmer Beneficiaries of WBCISThe satisfaction of farmer beneficiaries of WBCIS with location of weather station is depicted
below. More than three-fourth (77%) of respondents from the entire sample are not satisfied
with location of the weather station. Both the OBC and General categories, which accountfor nearly 88% of respondents in the entire sample, have reinforced the negative opinion
regarding the location of weather station, with more than 75% respondents ‘not satisfied’with it. The location of weather station has the greatest bearing on the risk inherent in aweather insurance contract once key parameters of the contract have been set. In otherwords, basis risk during the operation of a weather insurance policy is largely a function of
the location of the weather location.
Chart 3: Response on Location of Weather Station

Indications from Other Key Stakeholders (Insurers, State Agencies, Specialists)
All stakeholders in weather insurance irrespective of whether they represent the supply side,
the demand side, or the non-transactional side, unanimously support the minimization ofbasis risk. Insurers that were earlier wary of using non-IMD stations have now become
acceptable to settling weather insurance claims on the basis of data from the rain gauges
maintained by various state Government agencies across India. Despite the high credibility
and technical rigor associated with the weather data provided by IMD – the designated State
Agency for this purpose, insurers have to avail the services of third-party weather data
providers for addressing the dire shortfall of weather stations which can minimize basis risk
in insured locations. The spurt in demand has triggered a race among key players for setting
up new weather stations that may ultimately lead to an unsystematic network of weather
stations with suboptimal spatial distribution.

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6.1.3 Design of Weather Insurance Products
The design of weather insurance product is like to a black box which has weather data as an
input and a term-sheet as an output, with the intermediate process continuing to remain amystery for even seasoned personnel dealing in sales and marketing of weather insurance.
For a common man, the simplest evidence of a good weather insurance design is its claim
payout during seasons which are adverse or devastating on a widespread level. One suchadverse season was the Kharif 2009 season which has been regarded as possibly the worst
Kharif season since 1972. Western India, in general, and Rajasthan, in particular, bore thegreatest brunt of a devastating drought last year. The claim ratios (claims paid against
premium collected) of AIC and ICICI Lombard for their weather insurance portfolio in
Rajasthan during Kharif 2009 season were nearly 98% and 34% respectively. Loss ratios(total claims against sum assured) of more than 81% could be seen for certain locations
insured by AIC which is a healthy sign of the ability of weather insurance product to pay in
distressing times.
Indications from Farmer Beneficiaries
The satisfaction of farmer beneficiaries of WBCIS with the design of weather insurance
policy is illustrated in the following chart. Less than 30% of respondents from the entire
respondent sample are not satisfied with the design of their WBCIS policy. The design of
WBCIS policy does not figure among those 8 aspects of WBCIS (out of a total of 16 aspects)
having the highest percentage of ‘not satisfied’ responses, as indicated by Reference Chart
1. Therefore, the concern among farmer beneficiaries regarding the design of weather
insurance policy can be regarded as moderate and may be addressed in the medium to longterm.
Chart 4: Response on Design of WBCIS Policy

Due to the typical structure of a weather insurance term sheet, farmer customers may notfind it easy to unravel the technicalities in the design of weather insurance. The task ofAgricultural Finance Corporation

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appraising and approving the designs of weather insurance products may hence be
entrusted to the regulatory agencies, designated expert committees, and entities in the nontransactional space working to ensure a fair transaction between the supply side and thedemand side of weather insurance. Owing to the practically limitless number of designs
possible for weather insurance, the task of appraising a diverse portfolio of weather
insurance designs and their contextual suitability is a specialized task that unfortunately has
not been able to attract the level of attention and technical rigor which it truly deserves. The
challenges in comprehensively evaluating weather insurance products are compounded by
the fact that weather insurance products lie at a crossover of agriculture, statistics,
meteorology, and financial risk management, each of which is a specialized field ofknowledge with limited expertise available. Therefore the task of identifying resource
persons with good understanding of more than one or two of the above fields is quite
challenging and requires substantial efforts to bring such rare expertise on board.
Indications from Other Key Stakeholders (Insurers, Specialists, Scientific Community)
One of the common criticisms of weather insurance has been its limitation of insignificantly
compensating the insured farmers for even the worst of crop seasons (e.g. Kharif 2009).
The following case study of a discerning customer aptly highlights how the issue ofsuboptimal indemnification by rainfall insurance could be brought out during its very first trial.
For the less prudent, the time lag to sense this limitation of weather insurance may beslightly higher, 4 to 5 years by a conservative measure. Therefore the issues pertaining todesign of weather insurance need to be addressed within the medium time horizon (1-3
years).
Such a situation for even an apparently well-designed weather insurance contract may be
the result of the inadequacy in any or both of following parameters namely, the maximum
sum assured, and the maximum probable loss. The term ‘total sum assured’ in weather
insurance contract may be deemed anomalous in the sense that even when a farmer has
lost the entire crop during a particular stage, the compensation accruing to that farmer under
the policy may not be the maximum sum assured under the weather insurance contract. It
will be rather simply the addition of the sum assured of the weather insurance covers
operative during that stage. The consequent indemnity may only be a fraction of the
maximum sum assured under the weather insurance policy bought by the farmer. The
maximum probable loss denotes the highest cumulative payout (sum of payouts of all
constituent covers) among all the cumulative payouts simulated historically from a weather
insurance contract. The quantitative difference between maximum sum assured andmaximum probable loss for a weather insurance contract represents a theoretical gap
between the maximum payout committed by that contract and the actual payout that could
be expected from that contract even in considerably adverse years.

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Evaluation of Pilot Weather Based Crop Insurance Scheme“Rainfall Insurance is transparent and paid out at the right time but claims are small compared tomy actual loss” - Govindbhai Somabhai Chavda, Shahpur, Mangrol, Junagadh“Providences are stronger than deliberations.” Few things would epitomize it better than Govindbhai’s trial ofrainfall insurance. It was one of those routine trips from his village to Mangrol during which Govindbhai saw agroup of his fellow-villagers listening attentively to some talk by an outsider. A part-time farmer but a school
teacher by occupation, Govindbhai could not douse his curiosity and ventured further to gain more details. Never
could Govindbhai or anyone else have imagined that he would walk out with a stake much bigger than any of them
who had been deliberately called to participate.
Govindbhai ended up having a rainfall insurance coverage for 5.5 acres. The role of premium subsidy by AKRSP (I)
did play a big role in Govindbhai’s decision as he clearly put it, “Subsidy by AKRSP(I) was a key motivator as I
would have been reluctant to take chance with entire premium burden on myself.”
The higher stake and the occupational traits (being a school teacher) made Govindbhai drill deeper into the nuancesof his rainfall insurance. His keen observation was apparent when he pointed out, “The cheques for payment of
rainfall insurance claims did not have an at-par facility which cost me some precious rupees.” Despite his incisive
eye, he attributes his experience of rainfall insurance to sheer luck, “I just came to know about weather insurance bychance. In the end, it turned out to be a good bet for me.”For a part-time farmer, one may imagine that Govindbhai would have been content with getting an indemnity
payout of nearly three times the premium paid by him. In his words, “Claim received by me is a small portion, about
50%, of the actual loss suffered by me.” He goes on to articulate his situation better, “After I had invested INR
22,000 for 5.5 acres of my crop, I spent Rs 3500 as premium to insure my investment. Why should I be satisfied
with a total payout of INR 10,000 only when my actual loss in term of my investment is much higher at INR
17,500? If I start considering the loss in terms of potential revenue, the payout seems even smaller.”
The explanation that basis risk and the covariant nature of rainfall insurance result in distortions of payouts vis-à-vis
the actual losses of an individual farmer fails to placate Govindbhai. He avers, “I do not wish to be paid out innormal years but whenever I suffer a big loss, I should a get a reasonable claim.” Will weather insurance ever beable to address Govindbhai’s requirement?

Insurers have expressed their commitment to weather insurance products which fulfill the
expectations of farmers and other key stakeholders. All of the insurers are working with atleast one specialized external agency for technical support on product development. AIC
and ICICI Lombard both have undertaken efforts in the past to tap the expertise of IARI India’s leading and internationally acclaimed research institution in agriculture. Despite theapparent synergies, the desired outcomes could not be achieved because of mismatch inresource requirements (time, investment in research, manpower etc) required to make asignificant breakthrough. The limited size of weather insurance portfolio, reasonable
uncertainties regarding its scalability and high claim ratios were some key deterrents for
insurers to sustain investments in weather insurance product research and development witha long-term view. In the absence of long-term orientation and commensurate investment, it
may be difficult for premier research institutions to put their best experts for weather
insurance research and development, forgoing other socially and economically attractive
research assignments.
6.2Transparency in WBCISThe issue of transparency in crop insurance can be visualized from multiple standpoints.
Transparency in the context of WBCIS includes constructs like reliability of weather stations,

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freedom from manipulation, explanation on weather insurance policy, resolution of queries
and grievances. These constructs were investigated during the questionnaire survey offarmer beneficiaries and also during the interactions with other key stakeholders.
Indications from Farmer Beneficiaries on Reliability of Weather Stations
The satisfaction of farmer beneficiaries with the reliability of weather stations is illustrated in
the following chart. Less than one-fifth (19%) of respondents from the entire respondent
sample are not satisfied with the reliability of their weather station. Furthermore, the reliabilityof weather stations figures at the topmost place among those 5 aspects of WBCIS (out of a
total of 16 aspects) having the lowest percentage of ‘not satisfied’ responses, as indicated by
Reference Chart 1. Therefore the low level of concern among farmer beneficiaries for the
reliability of weather stations lends credence to the transparency and reliability of WBCIS.
Chart 5: Response on Reliability of Weather Stations

Indications from Other Key Stakeholders on Reliability of Weather Stations
The reliability of weather stations has been regarded as unquestionable by most key
stakeholders. Government agencies in those states, where low or no significant claims haveoccurred under WBCIS, have expressed low satisfaction with reliability of weather stations.
This is not worrying as insurers are becoming more acceptable to the idea of using data fromthe weather set-up maintained by agencies of the state Government, provided the set-up
meets basic criteria. With the installation of more weather stations of better quality andtamper-free operation, such isolated instances of dissatisfaction regarding reliability of
weather stations would be further minimized.
Analysis of Satisfaction Level for IMD & Private Weather Stations
Gaps and weaknesses in the Indian agricultural extension system, the emerging state of
WBCIS in India and the low level of awareness of crop insurance among farmers

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necessitated the development of a farmer beneficiary schedule which could be simple toadminister while covering the broad aspects of WBCIS and crop insurance. More thansixteen aspects of WBCIS were included in the farmer beneficiary schedule out of which only
one aspect pertained to satisfaction about the reliability of weather data. During the pre-testof farmer beneficiary schedule, it was validated that farmers could respond effectively only tothose items which were broad in nature. The question regarding the ownership of settlement
weather station, in terms of public (IMD) versus private, was discarded while retaining thequestion regarding the proximity of settlement weather station from the farmer’s insured
field. Since proximity of weather station was of considerably higher importance due tounderlying basis risk, most farmers took note of this aspect in contrast to their low interest
and awareness regarding the entity managing the weather station.
Even during the administration of the farmer schedule, the survey team did not get any
specific inputs from farmers which could enable discrimination between IMD and private
weather station. The views of other stakeholders (including insurers) regarding weather dataservices of IMD and private weather stations have been discussed in detail in the main
report.
Indications from Farmer Beneficiaries on Effectiveness of WBCIS against Manipulation
The satisfaction of farmer beneficiaries with the effectiveness of WBCIS against
manipulation and political risk is illustrated in the following chart. Slightly more than one-fifth(21%) of respondents from the entire sample are not satisfied with the effectiveness of
WBCIS to deal with manipulation and political risk. Furthermore, the effectiveness of WBCIS
against manipulation and political risk figures at the third place among those 5 aspects of
WBCIS (out of a total of 16 aspects) having the lowest percentage of ‘not satisfied’
responses, as indicated by Reference Chart 1. Therefore the response of farmers
strengthens the belief regarding the effectiveness of WBCIS to deal with manipulation andpolitical risks.
Chart 6: Response on Effectiveness of WBCIS against Manipulations

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Indications from Other Key Stakeholders on Effectiveness of WBCIS against
Manipulation
With weather data being public information, most key stakeholders could not envisage how
the payouts under WBCIS may be manipulated to suit the interests of specificconstituencies. IMD and even the third-party weather data providers have been able tomaintain the authenticity of their weather data remarkably well. As in case of the previous
construct, there were isolated instances of dissatisfaction regarding effectiveness of WBCIS
against manipulation. These emanated mainly from Government agencies in those states,
where low or no significant claims have occurred under WBCIS during their limited
experience.
Indications from Farmer Beneficiaries on Explanation of WBCIS Policy
The satisfaction of farmer beneficiaries with the explanation of WBCIS policy at the time ofpurchase is illustrated in the following chart. Slightly more than one-third (34%) respondents
from the entire sample are not satisfied with the explanation offered to them about theworking of the WBCIS policy. The lack of proper explanation on policy working may be borneout of either poor customer service orientation or a deliberate intent to conceal. Looking at
the other service related aspects of WBCIS, it is more likely that poor customer service
orientation may be the reason behind the inability to offer proper explanation on WBCIS
policy.
Chart 7: Response on Explanation of WBCIS Policy at Time of Purchase

Indications from Farmer Beneficiaries on Resolution of Queries regarding WBCISThe satisfaction of farmer beneficiaries of WBCIS regarding the resolution of their queries on
WBCIS is illustrated in the following chart. Nearly half (49%) of the respondents from theentire sample are not satisfied with the resolution of their queries on WBCIS. There are threemain possibilities behind such a high number of ‘not satisfied’ respondents. The first one

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may have taken place if the response offered to the queries of these WBCIS beneficiaries
may not have been of the desired quality. The second possibility pertains to the complete orpartial lack of mechanisms for the WBCIS beneficiaries to voice their queries and to receive
an appropriate response from concerned stakeholders. The third but the most unlikely
possibility is of a deliberate attempt to mislead the WBCIS beneficiaries, perhaps due tosome unknown motive. The third possibility may impinge on the transparency aspect of
WBCIS whereas the first two point out the existing loopholes in the service delivery aspects
of WBCIS, which get sidelined due to the treatment of WBCIS currently as a public good,
rather than a market offering.
Chart 8: Response on Resolution of Queries regarding WBCIS

6.3Settlement of Claims
Settlement of claims can refer to both the timeliness and representativeness of claims.
Timeliness of claim settlement is an inherent strength of WBCIS as weather data is the only
external input required for computation of claims under WBCIS. Representativeness ofclaims under WBCIS refers to its ability to pay claims when the insured farmer and the otherfarmers falling under the same weather station suffer a tangible crop loss from adverse
weather. Representativeness of claims is a function of the basis risk, weather insurance
design and covariance of loss experience.
Indications from Farmer Beneficiaries on Time Delay in Intimation or Receipt of Claims
The satisfaction of farmer beneficiaries of WBCIS regarding the time delay in intimation orreceipt of their claims is illustrated in the following chart. Slightly more than one-fourth (26%)
of the respondents from the entire sample are not satisfied with the time delay in intimation
or receipt of their payouts under WBCIS. Though the level of satisfaction could have been
higher on this aspect which is the inherent strength of WBCIS, this aspect does not figure
among those 8 aspects of WBCIS (out of a total of 16 aspects) having the highest
percentage of ‘not satisfied’ responses, on the basis on Reference Chart 1. Therefore theconcern among farmer beneficiaries regarding the timeliness of claim intimation/settlement

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under WBCIS may be regarded as moderate and should be addressed in the medium tolong term.
Chart 9: Response on Time Delay in Intimation or Receipt of WBCIS Claims

Analysis of Delays in Claim Settlement
The following table indicates the distribution of farmer-beneficiaries of WBCIS surveyed, who
are not satisfied with the time taken for settlement of claims under WBCIS. The proportion of
aggrieved respondents is highest in Bihar and lowest in Karnataka.

Table 23: Distribution of Respondents ‘Not Satisfied’ with Time Taken for Claim Settlement

Respondents‘Not Satisfied’124

Proportion of
Total Respondents
49.6%

Rajasthan95

38%

Chhattisgarh

99

39.6%

Karnataka

88

35.2%

State
Bihar

Based on the discussions with progressive farmers (10 from each state) and unstructured
inputs from the respondents covered during survey, farmers attribute the delay to insurers
and the respective state governments. Some of these informants have tried to indirectly linkthe delays in claim settlement with the intent of the insurer’s representatives to extract
economic rent for speeding up the transfer of claims to farmers.
Representing the perspective of insurers, state-level officials and key resource persons frominsurance companies have attributed the delay in claim settlement to the following causes.

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Evaluation of Pilot Weather Based Crop Insurance Schemea) Delay in Receipt of Data from Weather Data Providers: This has emerged as the mostcommon and potent reason for delay in claim settlement. Before the advent of weather
insurance in India, IMD was the sole entity in India dealing with recording, processing andsupply of high-quality weather data. The administrative processes and the operational
workflow of IMD could not align well with the need for regular, timely and seamless data
sharing with insurers offering weather insurance. Such a gap spawned private, third-party
weather data providers who could address the requirements of insurers in a more
conducive manner. It would not be an overstatement that most of these private, thirdparty weather data providers owe their survival and growth to insurers offering weather
insurance in India. Insurers could effectively avail the client-oriented functioning of theprivate, third-party weather data providers to scale up weather insurance in areas where
there was absence or inadequate coverage of IMD weather stations. In line with the
evolving requirements of insurers who are playing a key role in the weather data services
domain of India, IMD has been realigning its workflow to meet these requirements.
However, different regions are at different states of development with respect to theweather data infrastructure which can explain the differing levels of delay in receipt ofweather data. Rajasthan, Karnataka and Chhattisgarh have seen the introduction of
weather insurance earlier than Bihar coupled with the fact that the state of infrastructurein Bihar had to catch up with that in other states of India, including Rajasthan andKarnataka. The increase in penetration and outreach of weather station network can be
expected to bring down the delays in claim settlement because of delay in weather data.
b) Delay in Receipt of State Government Share of WBCIS Subsidy towards Premiums: This
has also been a major reason for delays in claim settlements. Unless the insurers receive
the entire premium, they cannot settle their claim liabilities with farmers. Since theweather insurance portfolio of these insurers is reinsured, the insurers are reconfiguring
their processes for improving adherence to norms for reinsurance. In the absence of due
cooperation from State Governments in the form of timely release of their share of WBCIS
subsidy, the insurers have to hold back the settlement of farmer claims.
6.4Benefits of WBCISWBCIS has transformed the domain of agricultural risk management in India. Before theadvent of WBCIS, weather insurance was a promising risk management tool that had
created enough buzz to remain a talking point in India for many years to come. However the
long-term customer appeal of weather insurance was dicey as farmers could not see asustainable value proposition in regularly buying this useful but costly risk management
instrument. The price-value mismatch had started to pull down the demand of weather
insurance, and entities that had been extremely bullish on the positives and growth potentialof weather insurance started becoming more realistic, diverting their resources for their more
optimal utilization. The search for scale and growth path for weather insurance was
becoming more elusive with every passing crop season.

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Key Indicators of Weather Insurance Programme of BASIX
The chart below indicates the total premiums collected and the total number of weather
insurance policies enrolled through BASIX – the organization which pioneered weather
insurance in India. After hitting the peak number of 11,716 weather insurance policies in2006, the farmer enrollment has been topsy-turvy. The weather insurance portfolio appears
to have been beset with difficulties and stagnation as was evident during field interactions.
Since 2007-08, there has been no change in the generic weather insurance productmarketed by BASIX. This situation has been aggravated due to bad marketing experiences,mistiming of sales activities and insufficiency of products to cover the crop risks.
Chart 10: Key Indicators of Weather Insurance Portfolio for BASIX

Growth of Weather Insurance Portfolio of AIC8 and ICICI Lombard (in Area Insured)
It was during the budget of FY 2007-08 that Hon’ble Union Finance Minister laid thefoundations for WBCIS by announcing an annual subsidy of INR 100 Crore. The interest of
farmers in weather insurance got reinvigorated as a result of the affordable pricing of policies
offered under WBCIS. Ever since then, weather insurance has been going from strength tostrength in India. For the Kharif 2007 season, it was AIC which got greater impetus from
WBCIS. However from Rabi 2007-08 season and thereafter, balance has been restored
between AIC – the public sector insurance company, and other private sector companies
operating in the weather insurance domain. The following graph depicts the spurt incumulative insured areas of AIC from 2007-08 and ICICI Lombard from 2008-09 onwards.

8

Portfolio for AIC in terms of Total Area Insured from 2007-08 onwards has been equated to Total Area
Insured by AIC under WBCIS. The area insured under Non-WBCIS portfolio of AIC has been ignored becauseof lack of reliable data. For 2009-10, Total Area Insured by AIC under WBCIS during Kharif 2009 season has
been taken as the Total Area Insured by AIC for the entire year.

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Chart 11: Area Insured by Top 2 Insurers under Weather Insurance (in Acres)

Indications from Farmer Beneficiaries on Effectiveness of WBCIS as a Protection Tool
against Crop Losses & Climate Change Effects
The satisfaction of farmer beneficiaries of WBCIS on the effectiveness of WBCIS as aprotection tool against crop losses and climate change effects is indicated below.
Slightly more than one-sixth (17%) of the respondents from the entire sample are notsatisfied with the effectiveness of WBCIS as a protection tool against crop losses andclimate change effects. This aspect of WBCIS figures at the second place among those 5
aspects of WBCIS (out of a total of 16 aspects) that got the lowest percentage of ‘notsatisfied’ responses (Reference Chart 1).
Chart 12: Response on Usefulness of WBCIS against Crop Losses

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Indications from Farmer Beneficiaries on Usefulness of WBCIS as an Alternative to
NAIS
The satisfaction of farmer beneficiaries on the usefulness of WBCIS as an alternative to
NAIS is illustrated in the following chart. Slightly less than one-third (32%) of the
respondents from the entire sample are not satisfied with the usefulness of WBCIS as analternative to NAIS.
Indications from Other Key Stakeholders on Usefulness of WBCIS as an Alternative to
NAIS
Most key stakeholders with a fair understanding of both WBCIS and NAIS haveacknowledged the fact that both have some unique advantages and unique limitations. Since
neither of these two types of crop insurance can singularly address the diverse production
risk management of Indian farmers, it would be better to allow both of them as independent,
standalone types. A more detailed discussion on whether and how NAIS and WBCIS cancoexist and flourish is presented in the later sections of this chapter.
Chart 13: Response on Usefulness of WBCIS as an Alternative to NAIS

6.4.6 Claim Benefits of WBCIS
The true test of an insurance contract is its ability to pay claims when the insured has
suffered losses under the insured perils. As farm-level or farmer-level production losses inIndia are unavailable in documented form, proxy indicators like percentage of total insured
farmers benefited or claims ratio under WBCIS can serve as useful yardsticks for assessing
the ability of WBCIS to benefit the insured farmers. The following charts depict these proxy
indicators to give an estimate of the claim benefits provided by the three insurancecompanies participating in WBCIS. Over the first five seasons of its WBCIS experience, AIC
has been able to provide claim benefit to nearly 62% (cumulative across all seasons) of the
farmers insured by it. The overall claims ratio of AIC is nearly 77% which indicates that out of
every 100 rupees of premium received by it, it has paid out an average of 77 rupees across

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the six seasons of its WBCIS coverage. The corresponding percentage of farmers benefited
for ICICI Lombard is 41% while its overall claim ratio during five seasons of its WBCIS
participation has been 60%.
Of the six seasons of WBCIS coverage, including Rabi 2009-10, the one season where theability of weather insurance to pay claims faced its toughest test was the Kharif 2009season. This season was claimed to be the worst in the last 27 years and had widespread
impact across India. Western India, particularly Rajasthan, suffered badly from drought andhad to look upon relief from State and Central governments. Rajasthan, coincidentally, hasbeen one of the foremost states in adoption of WBCIS. The claim ratios of the leading
insurers, AIC and ICICI Lombard for Rajasthan during the Kharif 2009 season were 98% and
34% respectively. Except the conspicuous Kharif season, ICICI Lombard has fared well interms of claims payment. Its claim ratio for the latest Rabi 2009-10 is 66% compared to 82%(approx) for AIC.
Chart 14: Percentage of Farmers Benefitted through Claims under WBCIS

Chart 15: Claims Ratio under WBCIS

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6.5Weather Station Infrastructure for WBCISThe location of weather station has the greatest bearing on the basis risk in a weather
insurance contract once the key parameters of the contract have been set. The location of
weather insurance has the potential of turning weather insurance - basically a loss
compensation instrument, into a lottery. On the question of what should be the ideal radius
for coverage of rainfall, the responses have been wide-ranging, from 25 km to 5 km. These
numbers can be regarded as guesstimates, moderated by the pulls of demand andconstrained by the limitations of supply. However there seems to be a growing unison
among key stakeholders that coverage under rainfall insurance should not be offered to afarm located at a radial distance of more than 10 km from a weather station. Starting fromthis heuristic value, there should be efforts to systematically bring this down to 5 km within acouple of years through better planning and guided enforcement. Since both Kharif and Rabi
seasons would essentially use the same infrastructure for weather data, the heuristic radial
distance for rainfall insurance would, in turn, become the guiding value for weather
insurance based on temperature indices.
WBCIS has been successful in galvanizing insurers to look beyond the existing network of
weather stations and work out pragmatic ways to meet the weather insurance demand, from
wherever it had been emanating. The receptivity and the problem-solving orientation of
insurers has been successful in spawning a whole new business class of third-party, private
weather data providers.
The remarkable aspect of the flexibility displayed by insurers has been that the key attributes
of weather data - reliability and accuracy, were not compromised at any point. Though there
may have been some isolated errors, the neutrality and authenticity of third-party, private
weather stations have never been in doubt. At the same time, the responsiveness,timeliness and representativeness of both weather data and the service providers have
improved substantially enabling insurers, in many cases, to periodically track claim status
and to compute interim claims much before the date of completion of risk coverage under
WBCIS. The quality of the weather data for claim settlement is no more an issue of concern
for the insurers and the insured, even when third-party, private weather data providers comeinto the picture.
The growth momentum in demand for weather insurance, triggered by WBCIS, has actuatedthe quest for achieving international standards in weather data services. IMD, thefountainhead of technical expertise on meteorology, has also responded keenly to thedemand for new weather stations which work on state-of-the-art technology. During theperiod 2008-2010, IMD planned to set up more than 500 new automatic weather stations(including automatic rain gauges). ISRO, the apex institution in India for space research, hascollaborated with IMD for installation of another 1000 AWS across India.
In the midst of competition for installation of new weather stations to serve weather
insurance in India, the growth plans of all the weather data providers have to be harmonized

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to tap synergies, reduce redundancies and minimize gaps in coverage. Data supply forweather insurance is likely to be the main revenue stream for most of these weather data
providers before other revenue streams like sale of historical weather data forindustrial/business research and other value-added services become potent. Unlike telecom
sector which is seeing a rapid proliferation of network and service providers, it is difficult toenvisage a scenario when weather data or services based on weather data could be soldprofitably to an average citizen of India which could help in recovery of investments madetowards creating a wide, dense network of weather stations.
6.6Relationship between NAIS and WBCIS
NAIS is a crop insurance which could not translate into conceptual appeal and structured
design into fair, widespread, and sustainable value for its designated beneficiaries. As thelargest crop insurance programme in the world, it has its share of unique advantages likecomprehensive coverage, physical assessment and low physical infrastructure
requirements.
Weather insurance is another type of crop insurance which was borne out of the need for
objective, transparent, prompt and administratively-simple claim settlement. Right from its
successful pilot, it was looked upon as a natural successor or alternative to traditional formsof crop insurance. With the passage of time, the innate limitations of weather insurance havesurfaced and have raised serious questions about its ability to replace area yield insurance.
Even the naysayers of NAIS have started to realize that weather insurance can becomplementary to area-yield insurance, rather than acting as its substitute.
The hopes of the stakeholders in the crop insurance space have now shifted to remotesensing technologies which is witnessing rapid advancement. Till the time remote-sensing
technology becomes so reliable and cost-effective that it can be utilized for loss assessmentof existing insurance units, the crop insurance sector in India will go through a transitional
phase wherein NAIS and WBCIS can play the role of either complements or alternatives, but
not substitutes. The application of remote-sensing for crop yield/loss estimation has shown
the promise of rectifying the big malaise of NAIS, which is its loss assessment procedure:
sub-optimal, unwieldy and error/risk-prone. Therefore the growth curve of remote-sensing
applications will determine the future path of crop insurance in India.
On the question of offering NAIS and WBCIS as alternatives for the farmer-client to choose,
there was no clear view. Though some experts who trust the discrimination ability of farmers,
were in favour of giving farmers the option to choose their crop insurance type, there werean equal number of practitioners who felt that such an option will inevitably engenderconfusion, dissatisfaction and mistrust among farmers. Since there are bound to bemismatch in payouts for the same location under the two dissimilar crop insurance schemes,
farmers may unintentionally or deliberately exploit this mismatch to demand parity with themore beneficial outcome. In case of unequal payouts from NAIS and WBCIS, either the
farmer-clients will press for equal payouts on the pretext of insufficient awareness andAgricultural Finance Corporation

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understanding or will turn away from crop insurance with dissatisfaction citing
discrepancies/contradictions. The political economy of crucial support from farmers may
cause a point of contention or rift between the State and the Centre if farmer dissatisfaction
gains sufficient magnitude. Even when farmers will find one type of crop insurance better
over the other, it will solely be on the narrow-minded basis of benefits received by themduring their period of experience, rather than on the inherent strengths of the given
insurance type. Therefore for a technically intricate concept like crop insurance, the aim ofachieving a minimum threshold of awareness and understanding among farmers should beheld paramount before empowering farmers with the option of choosing between different
types of a social good like crop insurance.
After examining the potential pitfalls in offering farmers the option of choosing between NAIS
and WBCIS, the possibility of integrating WBCIS with NAIS appears to make better sensewith the inputs from various stakeholder groups. All key stakeholders, other than farmers,
have acknowledged the unique advantages of WBCIS and NAIS and have supported thecontinuation of both types till a more optimal type of crop insurance is found. The viewpointsof the various key stakeholders, particularly those with a direct or indirect stake in theoutcomes from crop insurance, have been in favour of the crop insurance scheme which has
demonstrated better payout ability (more/bigger/widespread payouts) during theirexperience. Despite this natural preference, most of these stakeholders have also beenrealistic enough to admit that any one type of crop insurance, either WBCIS or NAIS, isincapable of meeting the expectations of the farmers and the larger community of
stakeholders. Therefore, the possibility of integrating WBCIS with NAIS is more plausible inthe current scenario. The workable approaches for achieving integration between WBCIS
and NAIS are discussed in the next chapter.
6.7Analysis and Cross-Comparison of WBCIS Products Offered by Insurers
The involvement of designated agencies of the State Government in the administration of
WBCIS has led to the standardization of WBCIS products, especially after one or two
seasons of WBCIS experience in a given state. Though standardization of WBCIS products
being offered by various insurers in a particular state is desirable for minimizing information
asymmetries and simplifying product communication, it creates disincentives for insurers toundertake improvements in product development for reducing basis risk. Basis risk inweather insurance is not only inherent in the location of reference weather station but it isalso a function of the design of the WBCIS product. The specialized nature of productdevelopment, the esoteric terminology used in a term sheet, and the concoction of agrometeorology, statistics and economics within the underlying parameters have theundesirable effect of turning weather insurance into an incomprehensible device.
Two key factors underscore the need for proper evaluation of weather insurance products for
rainfall, and consequently, the Kharif season. First, the Indian agricultural production system
has been traditionally believed to be more sensitive to rainfall, particularly the rains fromSouth-West monsoons. Owing to the predominantly rain-fed nature of agriculture in India,

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mitigation of rainfall-related risks have merited higher priority in the earlier phases of weather
insurance in India. Secondly, rainfall is a phenomenon which is observable to the humaneye. Any adverse event arising from rainfall can be easily discerned and attributed to thespecific adverse event compared to the more subtle and obscure impacts of temperature.
The relationships between rainfall events and their corresponding impacts on crop output are
better validated and, more importantly, commonly understood, which makes the tasks ofappraising rainfall-based weather insurance covers easier.
The majority of WBCIS products employed for rainfall insurance during Kharif season
employ predominantly covers of the following 3-4 types:
(i)
(ii)
(iii)

Phase-wise deficient rainfall cover for insuring volume of rainfall
Consecutive dry days (CDD) cover for insuring dry spells or rainfall distribution
Excess/unseasonal rainfall cover for insuring against excessive/unseasonal rainfall

The fundamental designs being employed for capturing production risks arising fromadverse weather events can be regarded incongruous with the peril/event being insured
through them. For the sake of illustration, let us examine a commonly-used rainfall
insurance cover like the Consecutive Dry Days (CDD) cover which aims to insure against
drought spells. This cover will indemnify the farmer-subscriber for dry spells if there are ‘X’number of consecutive days with daily rainfall on any day lower than 2.5 / 3 / 5 mm(threshold/benchmark values which are used commonly). If there is a rainfall incrementally
greater than 2.5 / 3 / 5 mm just 1 or 2 days before ‘X’ number of consecutive dry days areover, can this meager rainfall be expected to have significance for the crop being exposed
to a long drought spell? Furthermore for such small quantum of rainfall as 2.5 / 3 / 5 mm,
the probability of lower rainfall received by most farms within the insured area will also bequite high. Covers as this one, reduce the insurance of the farmer to a largely discrete,binary (payout/no payout) outcome which is not representative of the nature of loss
experience (continuous variable) in the farmers’ fields. Consecutive dry days cover is anextremely useful cover but its definition and operationalisation under weather insurance
have largely made it akin to a rainfall lottery.
On similar lines, consider the excess rainfall covers being employed in weather insurance
contracts for Kharif season, as part of WBCIS. It is a well-understood concept that heavy
and continuous rainfall within a short period of 1/2/3 days can cause damage to most
Kharif crops under excessive rainfall peril. Continuous and heavy rainfall for more than 3
consecutive days is a relatively rare phenomenon. The definition of excess rainfall in some
Kharif weather insurance covers is so anomalous that they treat a cumulative rainfall of
more than 150 mm during a 44 day period as excess rainfall. Even intuitively, rainfall of
less than 150 mm within a period of even 3-4 days cannot be regarded as excess rainfall.
Such glaring contradictions in definition of weather perils abound in the WBCIS products
which require a much critical evaluation before being offered under WBCIS.

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Chapter 7
KEY SUGGESTIONS AND AGENDA FOR ACTION
The role of WBCIS in giving a vital impetus to weather insurance in India cannot be
overemphasized. During the primary research, respondent entities from various sections of
key stakeholders have provided their suggestions for improving outcomes of crop insurancein India.
7.1Suggestions from Key Stakeholders
7.1.1 Suggestions from Farmer Beneficiaries of WBCISThe key suggestions provided by the farmer beneficiaries of WBCIS have been summarizedwith the help of the following spider chart. During the questionnaire survey, the beneficiary
farmers were asked to indicate their three most important suggestions for improvement ofcrop insurance. The ranking of the suggestions would help in prioritizing the efforts for
dovetailing crop insurance with the expectations of the farmers.
REFERENCE CHART 2
The following spider chart indicates the corresponding percentage of farmer beneficiaries
who have rooted for a particularly suggestion and placed it among their top 3 suggestions for
improvement of crop insurance in India. The respondents have not been segregated on thebasis of any characteristic variable. The percentages on the spider chart represent thepercentage of respondents from the entire sample of beneficiary farmers of WBCIS fromparticipating states. The aggregate percentage of respondents who have rooted for eachsuggestion, irrespective of its position within the top 3 ranks, can also be taken as arepresentative measure of its significance within the wide list of suggestions put forth by
farmer beneficiaries of WBCIS.
Though respondents were asked to provide their suggestions for improvement of both NAIS
and WBCIS, there is a preponderance of suggestions on various aspects of WBCIS due tothe higher emphasis on WBCIS in the earlier sections of the questionnaire. This study also
focuses more on understanding the perceptions and experiences of farmers regarding
WBCIS. However its relationship with NAIS and the general views of farmers regarding NAIS
have also been studied.

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From the spider chart above, the 3 suggestions that gained the highest share of responses
for the first rank are:1.

Better Location of Weather Station (32%)

2.

Better Design of WBCIS Products (26%)

3.

Greater Awareness & Understanding of Crop Insurance Working (11%)

Similarly, the 3 suggestions that gained the highest share of responses for the second rank
are:1.

Greater Awareness & Understanding of Crop Insurance Working (24%)

2.

Coverage of More Weather Parameters or More Perils (17%)

3.

Better Design of WBCIS Products (11%)

The 3 suggestions that received the highest share of responses for the third rank are:1.

Greater Awareness & Understanding of Crop Insurance Working (20%)

2.

Premium Refund for Successive No Claims (16%)

3.

Coverage of Pest & Disease Risks in Weather-based Crop Insurance (15%)

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The suggestions that received the highest aggregate (cumulative out of a maximum of300%) percentage of responses among the top 3 ranks, irrespective of their position within
the top 3 ranks, are as following:
1.

Greater Awareness & Understanding of Crop Insurance Working (55%)

2.

Better Location of Weather Station (46%)

3.

Better Design of WBCIS Products (45%)

The above suggestions define the agenda for further work towards bringing crop insurance,
particularly WBCIS, in alignment with the expectations of the farmers. The suggestions offarmer beneficiaries, that encompass a spectrum of issues associated with WBCIS, need tobe complemented by suggestions from other key stakeholders. The views of subject matter
experts, including both academicians and practitioners, endowed with a sagacious, wellrounded understanding of crop insurance can offer insights to address issues that result in
sub-optimal outcomes for the crop insurance sector in India.
Main Suggestions for Improving Crop Insurance in India (Integrating Inputs from
Farmers and Other Key Stakeholders)
Structural Reconfiguration of Indian Crop Insurance Model
Gaps and weaknesses in the Indian agricultural extension system coupled with the low levelof educational attainment and awareness among farmers negate the benefits of acompetitive market in terms of the variety of weather insurance products offered by different
insurers. In such a setting, the aim should be to offer a competitive market in terms of client(farmer) services which have been decried by almost all stakeholders during primaryresearch.
This calls for a crop insurance model in which a single well-evaluated and fine-tuned
standardized crop insurance policy is sold across an entire location with emphasis on
differentiation based on client services and product communication by various marketing
agencies. The three leading insurers participating in WBCIS have supported a crop
insurance model in which the main emphasis is on distribution and service delivery, rather
than the product per se. The Spanish model and the USA model are the two models of crop
insurance that have been indicated by the insurers as the most relevant to the Indian
context.

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Crop Insurance Model of Spain
Spain has a unique model of crop insurance in terms of both the program and the organizational setup. The basic feature of the system is that all insurable agricultural risks are covered by the private
sector and all types of policies are subsidized by the state.
Participation in the system is voluntary. It is a system in which ‘AGROSEGURO’ operates, both in its
own right and on behalf of the insurers, who make up the co-insurance pool. The system is based on
an intricate partnership between the private and the public sector. The key players of the systembesides farmers, are ENESA (Entidad Estatal de Seguros Agrarios), attached to the Ministry ofAgriculture; AGROSEGURO (Agrupación Española de Entidades Aseguradoras de los Seguros
Agrarios Combinados), a pool of forty private insurance companies which participate in a system of
co-insurance; CCS (Consorcio de Compensación de Seguros), a public enterprise with its own
resources, operating as a re-insurer.
A key feature of the Spanish system is the participatory approach. All stakeholders are represented in
ENESA, which enables taking strategic decisions and fixing the framework for the System (annual
plans) in line with their needs. For any given year, ENESA takes the lead in publishing the annual
plan. On the basis of the framework in the plan, AGROSEGURO fixes the detailed conditions for all
insurance products, in particular the differentiated premium rates which vary according to risk
exposure and also include administrative and re-insurance costs. Subsidies from the State andAutonomous Regions are paid out by ENESA and channelled through AGROSEGURO to theinsurance companies.

Crop Insurance in US

The Federal Crop Insurance Corporation (FCIC) was created in 1938, as a wholly owned Government
corporation. It is currently administered by the Risk Management Agency (RMA). The RMA was setup in 1996, to administer the agricultural insurance programmes and other non insurance-related risk
management and education programmes that help support US agriculture. The RMA regulates andpromotes insurance programme coverage, sets standard terms – including premium rates – ofinsurance contracts, ensures contract compliance, and provides premium and operating subsidies.
Crop insurance policies are delivered – sold, serviced, and underwritten by private insurancecompanies, along with FCIC. Companies that qualify to deliver crop insurance must annually submit
plans of operation for approval by FCIC. The plan provides FCIC with information on the ability of acompany to pay potential underwriting losses and on the allocation of its crop insurance business tothe various risks sharing categories, for the purpose of re-insurance. In addition to re-insurance, thecompanies are paid a subsidy by FCIC for administrative, operating, and loss adjustment costs. The
levels of administrative and operating subsidy and the terms of re-insurance are specified in theStandard Reinsurance Agreement (SRA), which applies to all companies delivering FCIC-reinsured
policies.
Private companies share the risk with FCIC by designating their crop insurance policies to risk
sharing categories, called reinsurance funds. Companies retain or cede to FCIC portions of premia andassociated liability. FCIC assumes all the underwriting risk on the ceded business and various sharesof the underwriting risk on the retained business, determined by the particular category and level oflosses. Companies can reduce underwriting risk on retained business, through private reinsurance
markets.

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For the Indian context, the role of AGROSEGURO or RMA can be played by a consortium of
insurers participating in WBCIS. The representation of various insurers in the apex-level
administrative or technical agency can be based on a mix of indicators that may include
business size, experience in crop insurance, technical expertise, process excellence,
investment (time, effort and funds) etc. This apex-level agency can be given the
responsibility of setting the detailed conditions for all insurance products, in particular the
product design and differentiated premium rates which vary according to risk exposure.
Subsidies from Government can be channelized through this apex-level agency to theparticipating insurers. Reinsurance arrangements for the entire portfolio can be negotiatedcentrally to attract better business terms and to reduce transaction costs.
At the field-level, crop insurance policies may be distributed and serviced by insurers andtheir marketing agencies. Insurers who intend to participate only in distribution and service
delivery of WBCIS may be allowed, entitling them only to marketing commissions oroperating subsidies. The insurers participating at the apex-level can contribute as coinsurers or share-holders in the entire crop insurance portfolio. The exact structuralconfiguration of the crop insurance model appropriate for India may be developed throughdetailed consultations with insurers, subject matter experts and technical agencies.
Universal Coverage of Farmers and Development of Macro-level Indices (Taluka-level
Weather Indices) for Catastrophic Insurance
During primary research, universal coverage of farmers under crop insurance has beenmooted by many key stakeholders, including insurers. Taking note that weather insurance isat best, a partial cover against covariate income shocks. No agricultural insurance scheme
can indemnify full losses experienced by the farmer. An effort, however, should be made to
optimize on the coverage of costs of production or getting close to the market value of the
produce lost. To ensure minimum coverage for each farmer, catastrophic indices for weather
need to be proposed and correlations between the different levels of risk should be validated
keeping in view the relatively higher cost of formal insurance vis-à-vis traditional risk
management strategies.
Furthermore, non-loanee farmers account for more than 50% of the total farmer base in
India in the context of formal sources of credit. Such a huge segment of farmers comprising
the non-loanee farmers, who are already devoid of cheaper institutional credit, virtually pays
double penalty as they are largely left out of a majority of governmental programs including
the crop insurance programme. At present, there are provisions to provide relief to such
farmers in case of catastrophic weather events or crop disasters, but the quantum of such
relief is largely ad-hoc and limited. In order to protect the non-borrowing farmers fromextreme financial distress and provide basic economic security, the Government can
introduce ‘Catastrophe Protection’ or ‘Non Insured Crop Loss Assistance’ for farmers,
drawing inputs from a similar program in the USA.

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Catastrophic Risk Protection (CAT) and
Non Insured Crop Disaster Assistance Program (NAP) in USA
Catastrophe Risk Protection in USA is the lowest level of Multiple Peril Crop Insurance (MPCI)
coverage. Premiums for the CAT portion of all crop insurance policies are fully subsidized by theFederal Government , although most farmers will pay an administrative fee for document
processing. Farmers with limited resources may be eligible for a waiver of the administrative fee
for CAT coverage. Any crop insurance agent can assist producers in determining if they are eligible
for a fee waiver.
CAT is a 50/55 coverage, meaning the losses exceeding 50% are payable @ 55%. In other words,
in the event of 100% of loss, CAT cover pays a maximum of 27.5% loss to the farmer (55% of 50%
loss).
NAP covers crops not insurable under typical crop insurance programs. These can be any crops,
including those for feed, in any county where at least catastrophic protection is not available. The
program protects against yield losses and prevented plantings due to catastrophic events such as
drought, excessive rain, floods, earthquakes, and other adverse natural occurrences. Conditions
related to these events such as fires or insect problems are also covered by NAP. Producers,
landowners, and tenants with shares are eligible for the program. Protection is offered at the basic
unit level.
NAP coverage pays an indemnity if the expected crop yield drops below 50 percent or where theproducer is prevented from planting more than 35 percent of the insured acreage. The indemnity
payment is calculated by multiplying the approved yield times the amount of production loss
covered.

Such protection can also become an effective conduit for channelizing calamity and disaster
relief funds from central and state governments. By linking relief funds to Catastrophe
Protection or Crop Disaster Assistance, the benefit of such relief can be passed on to thetargeted groups with greater efficiencies and transparency.
By developing taluka level weather indices for catastrophic insurance, the move towards
more robust systems to mitigate climate change impacts is also ascertained. Catastrophic
risks being low probability and high severity events have in principle a lower actuarially fairpremium compared to more frequent and moderately severe crop loss events. This toppingup of plain vanilla crop insurance products with low premium catastrophic covers would
ensure an excellent risk mitigation alternative to farmers at a higher level of granularity (e.g.
at taluka level). As the weather database improves with time, and cheaper channels todeliver WBCIS or weather insurance evolve, the sophistication of climatological modeling
could be harnessed to develop even village based covers, which could be envisioned as theultimate challenge for the frontiers of weather based crop insurance over the medium to longterm horizon. Problems of microclimates and basis risk could also be tackled under theselayered-risk transfer provisions for a large section of farmers.

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Relationship between NAIS and WBCIS and Need for Blended/Composite Products
After examining the potential pitfalls in preferring only type of crop insurance from NAIS andWBCIS, the possibility of integrating WBCIS with NAIS makes better sense based on inputs
from various stakeholder groups. All key stakeholders, other than farmers, haveacknowledged the unique advantages of WBCIS and NAIS and have supported thecontinuation of both types till a more optimal type of crop insurance is found. The viewpointsof the various key stakeholders, particularly those with a direct or indirect stake in theoutcomes from crop insurance, have been in favour of the crop insurance scheme which has
demonstrated better payout ability (more/bigger/widespread payouts) during theirexperience. Despite this natural preference, most of these stakeholders have also beenrealistic enough to admit that any one type of crop insurance, either WBCIS or NAIS, isincapable of meeting the expectations of the farmers and the larger community of
stakeholders. Therefore, the possibility of integrating WBCIS with NAIS is more plausible inthe current scenario.
Till the time remote-sensing based crop loss estimation evolves to reasonable perfection, aninterim solution would be to blend the features of the area yield index and weather index.
Such blended products, may appeal more to farmers and other stakeholders, as these would
incorporate the respective strengths of both NAIS and WBCIS while limiting the undue
dependence on any one of them. It may be expedient to promote such blended products,
with ideally an equal contribution to the total sum assured. Such blended products may becontinued with almost an equal emphasis on area yield index and weather index till an
integration of remote-sensing index becomes possible. The area yield index may then besystematically phased out with improvement in reliability and granularity of remote-sensing
index.
The hopes of the stakeholders in the crop insurance space have now shifted to remotesensing technologies which is witnessing rapid advancement. Till the time remote-sensing
technology becomes so reliable and cost-effective that it can be utilized for loss assessmentof existing insurance units, the crop insurance sector in India will go through a transitional
phase wherein NAIS and WBCIS can play the role of either complements or alternatives, but
not substitutes. The application of remote-sensing for crop yield/loss estimation has shown
the promise of rectifying the big malaise of NAIS, which is its loss assessment procedure:
sub-optimal, unwieldy and error/risk-prone. Therefore the growth curve of remote-sensing
applications will determine the future path of crop insurance in India.
Coverage of Perils under WBCIS
Coverage of further perils under WBCIS, with the predominant goal of meeting the
expectations of farmers and other key stakeholders, must be resisted to the best extent
possible. Multiple validation exercises in real-life conditions should be a prerequisite forinclusion of any new peril in a WBCIS policy. Experimental coverage of a peril should be

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discriminated unequivocally from coverage of standard perils through proper representation
in WBCIS policy. Rationalization of perils must be undertaken ab initio to gauge theeffectiveness of the coverage of a peril under a given policy. Only perils which can be
insured with reasonable representativeness and verifiability must be included. For example,
the inclusion of pest and disease incidence under WBCIS must be re-examined thoroughly,
as even the senior specialists in eminent research institutions, have expressed doubts about
the ability to cover such a peril through a weather index. Ornamental or highly suspect perils
should be weeded out from WBCIS policies through a systematic technical evaluation by acommittee of suitable experts.
Hydro-Meteorological and Crop Loss Database Development
Data are critical for weather insurance to generate credible results in terms of payouts. The
higher the quality of the data, the stronger would be the forecasting capability of the weather
insurance models.
Crop loss data are the basis for the development of vulnerability functions to estimate overallrisk leading to indemnification. It is recommended that in the future, crop loss data begathered in a more systematic manner, that losses be recorded by peril and at the highest
level of resolution possible. Having high resolution loss data would first improve therobustness of the vulnerability functions as the correlation between weather hazard and crop
loss would be more spatially representative. It would also increase the understanding of thespatial distribution of risk and therefore allow for locally specific risk mitigation measures.
Crop loss information (at individual or at an aggregate level) may be fed in by the afflictedfarmer customers through the toll-free phone service which can be stored in the centralized
loss database. The validation of such losses may be undertaken both through physicalverifications and juxtaposition with the corresponding weather or yield data. Development ofa centralized loss database for collection of agricultural loss experience of farmers andhazard impact should be initiated. This information is a critical input in the productdevelopment process for weather insurance and has considerable bearing on how the
product payouts correlate with the actual loss experience. Maintenance and update ofcentralized loss database can be subsequently managed by a suitable professional agency
which would be responsible for collecting and processing information from the farmers andother agencies.
Hydro-meteorological data are the basis for hazard quantification. The more detailed andcomplete the data, the better the results. In the present for weather/rainfall data is limited to
IMD stations and in some cases state govt. data on rainfall parameters only. It is
recommended that additional stations be added to obtain a better understanding to thespatial variation of the hazard.

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Although the sophistication of the risk assessment methods and tools is a function of thedata available, the information derived from these tools quantifies with variable levels ofcertainty, the following hydro-meteorological parameters:•

Location and extent – is the area affected by one or more hydro-meteorologicalhazards such as drought, flood, cold waves, heat waves, and cyclones, if yes, thenwhat types of hazard, and where?

•

Frequency and probability of occurrence - how often are hazard events likely to occur(in both the short and the long term)?

•

Intensity/severity - how severe are the events likely to be (e.g., flood levels; speed ofwinds and volume/rate of rainfall during hurricanes; magnitude and intensity of anindividual hydro-meteorological peril•

Duration - how long will the hazard event last (from a few days or hours in the case offlood and hurricane to months or even years in the case of drought)?

•

Probability/predictability - how reliably can we predict when and where events will
happen?

Information about the onset speed of a weather hazard event is principally relevant to
preparedness and early warning systems but may also have a direct bearing on planning
decisions, if sufficient capabilities and capacities are not in place. Therefore, while a set of
methods and tools are needed, strengthening capacities for meteorological, hydrological andclimate related hazard monitoring, databases and methodologies for crop-weather
relationship analysis in support of risk identification, reduction and risk transfer activities is a
must.
Implementation of Integrated Data System in India
Improving weather data system for insurers and research community working on agricultural
production risk management is the need of the hour. This calls for greater responsivenessfrom public agencies like IMD to leverage their contribution towards an integrated weather
data system for India. In order to materialize the goal of an integrated data system,
concerted efforts are required to tap the synergy in weather station installation by various
public/private agencies. The current growth of weather station network in India is largely
haphazard and devoid of a coordinated approach and integrated planning. Since most of theongoing growth is driven by expansion in outreach and penetration of WBCIS, it would be in
the interest of concerned agencies to take up this issue and work out ways to address it
through the involvement and keen participation of key stakeholders.
In order to attain the objective of an integrated data system for India, Public- Private
Partnerships (PPP) and integrated planning at the national level should be promoted. These

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centralized efforts have to be followed by decentralized implementation in identified locations
across the country.
Lack of adequate and reliable weather and crop loss data is considered to be a major
constraint in developing an accurate understanding of the current and future crop production
loss variability. Often the data collected are stored in various formats ranging from log bookto spreadsheets to telemetry systems. Development of integrated data management
systems will provide an effective basis not only for storing and analyzing data like rainfall andflow data but also for disseminating such information. As experienced today, not all records,
be they meteorological observations or crop loss data, are available in electronic format.
Such information collected and stored in one central place would form a core element ofknowledge and information for planning purposes. The need for such an integrated data
management system to capture historical records for complete and safe long term storage isreinforced by the increase use of crop growth and hydro-meteorological models to quantify
risk and develop climate risk management policies in years to follow.
The requirements for a high-density weather station network are not uniform across thecountry. These are influenced considerably by the exposure of crop yields to weather-borne
risks, presence of microclimates, spatial distribution of landholdings and demand for crop
insurance. Based on above factors, the 600+ districts of India can be categorized in terms ofhigh-priority, medium-priority and low-priority for implementation of a high-density weather
station network. The need for high-density weather station network is more pertinent tolocations frequently exposed to deficient/low rainfall/drought conditions. For locations prone
to excess/unseasonal rainfall, the density requirement of weather stations is significantly
reduced. Taking the average size of a district in India as 5,000 square km and the area
covered by a weather-station with 10 km radial coverage as 315 square km, the total number
of weather-stations required in a high-priority district would be nearly 16. If we assume that
half (50%) of the nearly 625 districts within India fall under the category of high-priority
districts, for implementing high-density weather station network, the total requirement ofweather stations for these high-priority districts comes to almost 5000 (313*16) weather
stations. Considering the recent growth in automatic weather stations, it would bereasonable to expect that nearly 1000 weather stations would be currently operational inthese 313 districts. Therefore the immediate demand of weather stations is around 4000
weather stations. This translates into an investment requirement of nearly INR 40 Crore,
presuming the cost of a reliable, basic, automatic weather station to be INR 1 lakh.
Based on our interaction with the subject matter experts and important stakeholders in theweather-based crop insurance sector, it is imperative to conduct a series of joint workshops
which are inter-disciplinary in nature with a mandate of estimating investment requirements
and developing a systematic approach for achieving optimal station density (15-20 weather
stations per district of nearly 5 thousand sq km area).

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Benchmarking of Weather Insurance Products
By their very nature, weather insurance products are difficult to comprehend for a typicalIndian farmer who is equipped with limited capacities and experience. The multitude ofweather insurance products offered by various weather insurance providers necessitates theneed for benchmarking the various products to enable the farmer to make an informed
choice. Through benchmarking it may be ascertained whether the products offered by thedifferent insurance companies carry at least comparable benefits (Protection vis-à-vis
Premium). The complex weather insurance products may be disintegrated into theconstituent covers for different perils.
Approach for Benchmarking of Weather Insurance Contracts
Start Day of Contract: Most field crops, which are of strategic importance to the foodsecurity of our economy, need to be sown by farmers in their respective growing season.
Sowing dates have a proven and significant influence on the occurrence of subsequent crop
stages and their durations. For the Kharif season, the sowing date is a function of sowing
rainfall which varies from location to location. There are two ways in which the dynamic
sowing date (varying from location to location and year to year) can be built into the weather
insurance contract. One way is to incorporate the specific triggers which reflect the ideal
conditions for sowing, into a weather insurance contract. Suppose for groundnut in Gujarat,
the ideal condition for sowing is more than 60 mm rainfall in 2-3 consecutive days. The dateof sowing event can be identified from the weather data which may later be validated fromlocal officials from agriculture department. The dates of subsequent crop stages can bemade dependent on this sowing cover, included in the weather insurance contract. Anotherway is to offer weather insurance contracts in a location after the completion of the sowing
event in that location. A basket of weather insurance contracts can be offered to farmercustomers, in which the dates of constituent covers have start dates and durations,corresponding to the specific sowing date of that farmer-customer. Implementation of thelatter approach would require the development of software applications for automation of
weather insurance product design/structuring.
Commencement Dates & Durations of Key Stages: Based on the sowing dates andspecific agronomic information, the commencement dates and durations of key stages haveto be identified. The concept of dynamic dates and durations of different covers, based on
the difference in sowing dates, has not been incorporated in any of the weather insurance
products being currently sold under WBCIS.
Weather Perils and Parameters of Significance for Key Stages: After finalization ofcommencement dates and durations of key crop stages, it is necessary to enlist the set of
perils of significance which can adversely affect the productivity under a given crop stage.
The perils can be segregated into endogenous (internal/controllable) or exogenous(external/uncontrollable) for inclusion under crop insurance policy. For exogenous risks, thecausative weather conditions have to be specified in terms of the key weather parameters.

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Since all the weather parameters do not have adequate historical database, it will be
important to isolate only those perils whose dependence on weather parameters (with longtime series of historical data) is well-established and practically validated. It is to be notedthat the strength of relationship of the particular weather parameter with a given peril has topass stringent tests of scientific rigour and has to be backed by institutions with highest
levels of expertise in the given crop or domain.
Period of Time for Consideration of each Relevant Weather Parameter: After
identification of specific weather perils for inclusion in weather insurance contract, the periodof time for consideration of the relevant parameters has to be meticulously chosen. In caseof deficient rainfall, the period of consideration may be upwards of 7 days. In most WBCIS
contracts, the period of consideration of rainfall under deficient rainfall volume cover is one
month or more. With improvement in product design and customer understanding, this levelmay be brought down to weekly level. For more demanding customers, the switch towards
20/14/10 days of period of consideration for rainfall may be initiated to provide impetus to
improvement of WBCIS products. Likewise, in case of excess rainfall, the maximum
allowable period of consideration may be 4/5 days with daily, two-day or three-day
cumulative rainfall set as triggers/strikes for payout. The period of consideration fortemperature parameter, more pertinent for Rabi crops, has greater uncertainty andpossibility of error. This is borne out of the relatively less availability of rigorous scientificevidence for weather-related losses in Rabi crops. The information currently available inIndia for Rabi crops may be sufficient for most research studies but may not suffice thestringent requirement for settling a weather insurance contract. Therefore, greater attention
has to be accorded to improvement of weather insurance for Kharif crops before moving onto Rabi crops.
Relative Significance/Weight for Each Period of Consideration or Peril: The occurrenceof weather peril may cause different economic impact (loss) in different periods ofconsideration for a peril. For example, deficient rainfall in the 3rd week of September cause
different degree of loss compared to deficient rainfall in 4th week of September. In order todifferentiate between or provide relative significance to various periods under a given cover,weights or multiplying factors are given to such periods.
Design of Weather Index: A weather index is essentially a function which expresses the
economic variables (revenue/income/yield) in terms of weather parameters. Values of aweather index for crop insurance represent different levels of crop productivity and thus
indicate the economic manifestation of weather parameters (in terms of yields). Examples ofsome weather indices are as follows:
Heating Degree Days: One of the first indices developed and still among the most popular
ones, is the Heating Degree Day (HDD) index. The idea of structuring a HDD index cameabout in order to correlate revenue fluctuations and temperature. Analysis of therelationships between temperature and demand for heating in the US showed that theAgricultural Finance Corporation

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threshold of 65 degree F was the turning point for increase in energy demand for heating.
Based on such a threshold, the number of heating degrees per day is given by

Where T is the average of the high and low temperatures of the day
Selyaninov Hydrothermal Ratio Index9: SHR is as an index used to monitor the impact of air
drought on winter wheat crop yields in Ukraine

Caps/Floors/Triggers/Strikes/Threshold Values for Specific Weather Parameter: These
values can be deemed to be the most critical parameters for a weather insurance contract.
The interplay of these values with the indemnity payment rates (or notional) control a tradeoff between the protection level inherent in a weather insurance contract and thecorresponding price (manifest a premium) for the given protection level. It is during theprocess of setting the caps/floors/triggers/strikes and the indemnity payment rates (ornotional) that the greatest dilution takes place in a weather insurance contract, in terms of
the protection offered by it.
Indemnity Payment Rates/ Payout Structure: Indemnity payment rates determine thequantum of indemnity to be paid out at different values of index under a weather insurance
contract. Indemnity payout structures for weather insurance can be of the following types.
Payout Options
Payout Structures

Binary Payouts

Layered Payouts

Proportional Payout

The graph of the indemnity payout structure is a vital tool for understanding how the sum
assured for a weather insurance contract is distributed across the entire spectrum of the9

Source: ‘Managing Agricultural Production Risk’ published in 2005 by The World Bank

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weather index. The curvature or flatness of a payout structure gives a quick indication of the
lumped or well-distributed nature of indemnity for a weather insurance contract.
Historical Payout Distribution: It is the year-wise distribution of claims as simulated for all
historical years, considered for the structuring of a weather insurance and computation ofpure risk premium through burning cost method. The graph of historical payout distribution
when viewed in conjunction with the term sheet of a weather insurance contract provides an
instant snapshot of the payout frequency, level of loading and protection level. It is one of themost comprehensive tools for evaluation of a weather insurance contract. A sample
historical payout distribution graph for maximum temperature peril for wheat crop in
Bharatpur is given below for illustration. The term sheet for the same weather insurance
precedes the graph.
Term sheet for Tmax Weather Insurance Cover for Wheat in Bharatpur
Cover Phase,From

1-Jan-09To

31-Mar-09

Strike (HDD)0

Exit (HDD)10Standard Loss Rate between strike andexit i.e. Notional (Rs / HDD)

600

Policy Limit (Rs)

6000

Historical Payout Distribution Graph for Preceding Term sheet

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Maximum Sum Assured / Policy Limit: The term ‘total sum assured/policy limit’ in weather
insurance contract may be deemed anomalous in the sense that even when a farmer has
lost the entire crop during a particular stage, the compensation accruing to that farmer under
the policy may not be the maximum sum assured/policy limit for the given weather insurance
contract. The consequent indemnity may only be a fraction of the maximum sum assured
under the weather insurance policy bought by the farmer. The maximum probable loss
denotes the highest cumulative payout simulated historically from a weather insurance
contract. The quantitative difference between maximum sum assured and maximum
probable loss for a weather insurance contract represents a theoretical gap between the
maximum payout committed by that contract and the actual payout that could be expectedfrom that contract even in considerably adverse years.
Juxtaposing the above Table and Chart from the preceding point 9, we can easily see thatthe maximum probable loss for the given weather insurance contract is Rs 1300 (approx)
whereas the maximum sum assured or policy limit is Rs 6000. In the more than 30 years ofhistorical simulation of the given weather insurance contract, the highest payout receivableto a farmer would have been nearly Rs 1300 whereas the policy limit is Rs 6000. Suchcontradictions undermine the protective value of weather insurance and need to beminimized to the best extent.
Need for Technical Advisory Body on Crop Insurance
As crop insurance built on a scientific platform is being increasingly used as important
welfare mechanism with substantial financial support from the Government , it makes sense
to create a ‘Technical Advisory Body’ (TAB) by the Government within the Ministry ofAgriculture and Cooperation (GoI) to review the progress of crop insurance schemes oncontinuous basis and to provide policy directions.
The fundamental mandate of the ‘Technical Advisory Body’ would be to approve roll-out of
only those weather insurance products under WBCIS which can ensure balance between
expectations of the demand side and deliverability of the supply side. The TAB may also
take the role of agriculture insurance development agency with technical functions and canwork closely with IRDA. The suggestions of insurers and premier research institutions canbe invited for identifying such subject matters experts from India who can objectively assess
weather insurance products and provide inputs for improving them. Since weather-based
crop insurance is a relatively new financial instrument even globally, the possibility of
involving international experts (like actuaries, crop-weather simulation experts etc) in such abody may be considered.
To complement the process for improving weather insurance products, medium-term
research projects may be commissioned by the Government. As part of these projects, highquality weather data from IMD may be analyzed through inter-disciplinary research exercises
involving research institutions, agricultural universities, industry think-tanks which can takeup region and crop specific correlation and calibration exercises. The public good nature ofAgricultural Finance Corporation

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these research outcomes could go a long way in making partial insurance products likeindex-based weather insurance more popular and gradually affordable.
Improving Financial Literacy and Technical Understanding of Farmers throughContext-Specific, Proven Measures
There are substantial challenges in marketing weather insurance and complex agricultural
insurance schemes to farmers and rural communities with low literacy and limited financial
sophistication. Most marketing agents in the field and even personnel from the insurancecompany have their own doubts when it comes to the difference between NAIS and WBCIS.
As evident from our primary research findings, huge gaps are seen with regard to proper
understanding of insurance in general and weather insurance in particular.
Considering the substantial financial outlay on providing crop insurance to farmers, theexpenditure on its dissemination and promotion through mass media should, at best, beconsidered marginal vis-à-vis the annual revenue expenditure on operations and financial
support. The awareness-building campaign for crop insurance may be modeled on the linesof the remarkably effective communication and promotional campaign employed for
Mahatma Gandhi Rural Employment Guarantee Scheme (alternatively NREGS). The
network of institutions falling in the framework of Local Self Governance should also be more
effectively utilized for promotion of crop insurance. In order to address the queries andgrievances of potential and existing farmer-clients in a personalized manner, the mass-levelawareness building efforts could be complemented by toll-free helplines. It may be useful toavail the existing set-up of toll-free facility made available by Agricultural Universities andother extension agencies. Interactive media sensitive to local conditions like street-plays or
insurance games should be employed in areas of more focused sales activity in order tosimplify the insurance mechanism and to make the potential adopters more comfortable withcomplex insurance products.
Improvement in Service Delivery Aspects of Crop Insurance
One aspect of WBCIS and weather insurance schemes in India that frequently came out
from our focus group discussions (FGDs), field surveys and interaction with intermediaries
and experts is the need for improvement in service delivery. Both pre and post policy sales
period service holds the key to customer satisfaction as in any other product. If the farmers
believe that they are valued by the insurers as well as by the intermediaries, their trust in theweather insurance being marketed also goes up. Studies with farmers of SEWA in Gujarat
and BASIX in Andhra Pradesh (Cole et al 2009, Gine et al 2008) provide quantitative
evidence on the role of trust and service delivery aspects in making weather insurance workfor the Indian farmers.
Value added services like periodic dissemination of weather index data and claims situation
could go a long way in improving the popularity of the products marketed. Use of SMS based

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weather data dissemination to progressive farmers or influential farming groups in the social
network of the farming community or complimentary services like pre sowing weather
forecasts could be provided at extremely low costs while the corresponding returns fromthese minimal investments by the suppliers could more than proportionately increase thereturns over the long run. Input dealers and field extension agents could be incentivized tohand hold the clients in the early days of being introduced to weather insurance policies
which would create a conducive environment for a fast and educated growth of an importantrisk management strategy like weather based crop insurance. Discount schemes onpremium discounts for next season or lottery draws with modest gifts could be used as amarketing strategy to influence farmers to purchase multiple policies of rainfall insurance(based on their risk appetite and ability to pay) as most of the voluntary purchased are
confined to single unit purchases.
Physical Individual/Area Assessment of Non-Indexable/Localized (Hail/Frost/Wind)
Losses (under both WBCIS and NAIS)
NAIS presently provides for individual assessment of losses in case of localized risks, viz.
hailstorm, landslide and flooding, on an experimental basis. Farmers feel the experiment is
not adequate, and it should be implemented on a full scale, covering all areas. Earlier
Government reviews have supported the view that the localized calamities should beassessed on an ‘individual’ basis in all the areas. But it should be reiterated that historical
data and past claims play a role in determining the premiums and damage assessment
continues to be the biggest challenge for crop insurers. Crops at different stages are affecteddifferently by hail/frost/wind making knowledge of losses arising out of these essential for
insurers. The practice of physical individual/area assessment of losses from nonindexable/localized perils (Hail/Frost/Wind) must be extended to the entire coverage of NAIS
and WBCIS.
A tentative suggestion for testing avenues to attain this integration could be by way of a‘Complete Insurance Model Village’ pilot as mooted by AICIL and to be realized inassociation with a consortium comprising organizations like NABARD, development
agencies of Gujarat, third-party weather data providers and research institutions. A cluster ofcontiguous villages could be taken up and laden with the adequate weather infrastructure aswell as physical yield assessment trials and then compared to some control clusters to see
the effectiveness of this integration and how a possible sharing of the cost of covering
different layers of risks as discussed earlier could be developed. Farmers’ feedback should
be timely and appropriately factored in while developing such an integration framework andthe findings from the experiments should be stress-tested over a few seasons before coming
out with prescriptive ways to scale up the model.

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Making Crop Insurance Mandatory for Contract Farming Initiatives
With an increasing focus and participation of private sector entities across the entire
agricultural value chain, the role of quality-oriented agricultural production is also gaining
impetus through an increasing number of contract farming initiatives.
It is a known fact that the economic exposure to uncontrollable risks is significantly higher fora farmer participating in contract farming initiative than his peers on account of the higher
investments by the former for crop inputs, technology and quality control. In order tosafeguard the interest of farmers participating in contract farming and to promote
trials/adoption by other farmers through demonstration effect, crop insurance needs to begiven the status of a mandatory input under contract farming initiatives. The responsibility ofarranging crop insurance from suitable insurers can lie with the contract farming sponsor or
aggregator. Taking into account the diverse nature of crops covered under contract farming,
it is essential to give thrust to new product development by insurers to cater to demand bycontract farming sponsors.
Promoting Collective-based Models of Crop Insurance
Self-help groups (SHGs) for rural women are one of those few programmes that haveendured and given good results. Their achievements have been impressive in mobilising thepotential for savings and thrift even among the poorest of the poor. They have helped indelivering bank credit to the poor and in inculcating the habit of timely repayment of bankloans.
With the outreach and penetration of self-help groups (SHGs) and other interest-based
collectives spreading deep into the rural hinterlands of India, there are enormous
opportunities to leverage these SHGs and other interest-based collectives for increasing thepatronage and reach of crop insurance in India. The utilization of SHGs and other interestbased collectives as a vehicle for sales, distribution and post-sales service delivery is going
to be a win-win proposition as it can reduce the typical insurance problems of moral hazard,
high administrative and transaction costs, lack of customer feedback and poor post sales
service delivery. All such advantages associated with merit focused pilot projects to assess
the effectiveness of SHGs and other interest-based collectives in crop insurance promotion,
administration and service delivery. The selection of the SHGs and other interest-based
collectives or the location for such pilot projects should take due consideration of the wide
differences in the institutional strengths and operational efficiencies across SHGs and otherinterest-based collectives.
Two potential models on the lines of which crop insurance programmes based on SHG andother interest-based collectives may be tried out in India are summarized in the following
boxes:

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SHG-based Model of Crop Insurance by Prof V.M. Rao

Rao V.M. suggests an SHG-type model of insurance for farmers in order to set up a simple andfeasible arrangement for insuring their enterprises against risk and uncertainty caused by weather.
While an SHG-type model would not be adequate by itself to protect the farmer from the hazards of
his occupation, it could be of help as a base and an initial framework for developing an insurance
system to meet the requirements of modernized agriculture. He suggests that the guaranteed yieldwould be fixed at village level on the basis of simple average of five years. Farmers participating in
the programme can choose the number of units of insurance based on how much he / she is willingto pay as premium. Claims would be paid on ‘area approach’ at village level and the aggregate
payout is decided on the basis of actual sample yield estimated during the current season and theaggregate value of sum insured. The claim is distributed in the proportion of the number of units of
insurance bought. If the accumulated premium fund available in a year is less than the total amountof compensation to be paid, the lower amount will be used to calculate the amount of compensation
to be paid per unit of premium per acre. In the event of a lack of agreement among the farmer
members about the status of the activity in a deficit year, an expert can be asked to estimate thedegree of the loss in that year.

Rao also suggests a variant of the above model, in which the Government / NGO/ funding agency
that sponsors the programme will contribute to the premium fund of the community. Each year thiscontribution can be a fixed percentage of the total amount of premium collected by the community
in that year and will be paid to the community at the end of the year. This can serve as an incentive
for the community to start operating the insurance programme and augment the funds available forthe payment of compensation during deficit years. The administrative costs, which in any casewould be meager, can be paid out from the premium fund.

Rao rightly believes that a valuable by-product of the SHG model of insurance will be the data baseon farmer-wise acreage, output and income of different crop and non-crop activities in thecommunity over a period of years. The community itself can learn to monitor these data carefully to
improve their capacity to cope with the risks inherent in their occupation. It could also open up newvistas for analyses of the farm economy and weather and market fluctuations by researchers andpolicy-makers. Further, insurance companies will find the database invaluable for developing new
insurance products for farmers. Thus, the SHG model for farmer insurance could trigger innovative
developments within the rural communities, including individual farm based insurance.

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Mutual Crop Insurance by People’s Mutuals of Dhan Foundation

Mutual Crop Income Insurance (MCII) for groundnut crop was piloted in Nattarampalli, a block in Vellore
district of Tamil Nadu, India through support of DHAN Foundation. After three years of experience of
deficit rainfall insurance, the technical constraints in designing a product that fully reflects the relationshipbetween crop performance and rainfall forced the participants to discontinue rainfall insurance. The search
for an alternative insurance mechanism brought them to Eureko Re, a Dutch reinsurance company which
offered its support to mutuality based crop income insurance.
Under MCII, farmers are indemnified based on actual losses, with loss assessment and price monitoring
done by older and wiser farmers. As participating farmers are already organized into various collectives, thenecessary social capital for piloting Mutual Crop Insurance (MCI) is already in existence. The covariant
nature of rainfed agriculture, which makes it difficult to insure, was addressed through pooling of risks ofdiverse collectives, each with different risk profile. Further this pilot has become feasible due to training
and reinsurance support by People Mutuals, technical support given by Mutual Insurance Association of
Netherlands (MIAN) and financial support and back up guarantee support given by Eureko Re.
To design the MCII product, the data on past experience of rainfed groundnut cultivation in Nattarampalli
was collected from a group of farmers. This included frequency, levels and causes of loss, variations acrossthe location and cost of cultivation. The preliminary product was discussed in detail with the Mutual
Insurance Committee (MIC) which customized it in terms of sum insured and premium per acre. It was
decided to go for the groundnut mutual income insurance policy with Rs. 2000 as sum insured and Rs. 500
as premium per acre. The design of the product was such that cost of cultivation was considered as thebench mark for compensation and not the expected income, to make the product affordable to farmers.
Moral hazard risk was addressed by introducing retention, requiring farmers to pay a pre-determined
percentage of their loss themselves. The insured farmers own the mutual pool and thus critically assess thefarmers accepted as members of the insurance pool. This environment of social control and familiarity ofcolleague farmers with production circumstances have resulted in avoiding farms that repeatedly face loss,
thereby addressing adverse selection. Last year, a total area of 74.1 acres belonging to 190 farmers wasinsured with the total premium and total sum insured being INR 37,065 and INR 148,260 respectively. Outof the 190 insured, 64 farmers received compensation amounting to INR 21, 250. Piloting MCII has
resulted in rich learning on designing and implementing mutual crop insurance and on various aspects ofgroundnut cultivation in Nattarampalli.

Legal and Regulatory Improvements
The continued imposition of service tax on weather insurance policies is startling consideringthat other forms of insurance targeted at similar socio-economic segments have been
exempted from service tax. Service tax on weather insurance policies sold to farmers must
be waived reinforcing its utility as a social good.

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The definition of insurable interest must be widened by AIC to include agricultural labourers
in its service net. The income of these workers depends considerably on rainfall. Initiallysuch coverage of agricultural labourers under WBCIS may be channelized through reliableintermediaries and can be scaled up after successful trials
Due to the structure of WBCIS, it becomes difficult for insurers to meet the stipulated
regulatory provision under Section 64 VB of the Insurance Act, 1938. Either the structure of
WBCIS or the concerned regulatory provision has to be modified suitably to ensure propercompliance by insurers.
The ceilings on actuarial premiums and premium subsidy have to be hiked up for providing
satisfactory coverage to cultivators in areas prone to high and frequent risk exposure. In
order to incentivize the insurance intermediaries for serving their farmer-clients better, theircommission may be computed on a value much higher than the subsidized premium being
paid by a farmer insured under WBCIS. According to some key intermediaries, thecommissions for a WBCIS policy are relatively small to demand substantial efforts inmarketing and service delivery.
The maximum probable loss denotes the highest cumulative payout (sum of payouts of all
constituent covers) among all the cumulative payouts simulated historically from a weather
insurance contract. The quantitative difference between maximum sum assured andmaximum probable loss for a weather insurance contract represents a theoretical gap
between the maximum payout committed by that contract and the actual payout that could
be expected from that contract even in considerably adverse years. In order to improve therepresentativeness and verifiability of WBCIS products, the quantum of maximum sum
assured should be made equal to the maximum probable loss under that policy. In order tofacilitate farmer-clients and other key stakeholders to choose their WBCIS policy more
effectively and enhance transparency, insurers should provide historical payout distributiontable/chart for the given weather insurance contract for all years considered for structuringthat contract.
Other Suggestions
Some other aspects of making WBCIS work for Indian farming communities is the need tounderstand the stabilizing effects of WBCIS with our without NAIS. It may be noted thatwhen single crops are insured, the revenue streams might be stabilized for that specific crop,
but in reality the farmers go for a mix of crop by way of mixed farming or inter-cropping, thus
creating a situation where the covariance of the revenues between the different crops grown
matter in ensuring the overall stability of the cropping systems as a whole. Since both priceand quantity volatility induces revenue fluctuations, a robust weather based crop insurance
system should be sensitive to the price and quantity movements of other crops which are notinsured. It is possible that the overall stability may fall in spite of having insurance for single
crop, when the correlations from the revenues from other crops are significant and move inthe opposite direction.

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Evaluation of Pilot Weather Based Crop Insurance Scheme‘Welfare effects’ of WBCIS payouts should be studied to estimate the impact of thedifferences in income and asset positions of those who got payouts as against those who did
not at times of droughts or village level catastrophes. The question whether the payouts aresubstantial enough to shield household consumption from weather shocks needs thoroughevaluation. Also, there might be substantial distortions in the labour markets because of askewed income enhancement situation and certain ‘price effects’ might emerge, when one
section of the agrarian community gets cash rich because of payouts while others like thepreponderant agricultural labourers get nothing.

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Annexure 1: Primary Research Data Outputs
1.1

Composition of Sample in terms of Key Variables

Looking at the composition of the sample of the farm households by social groups, aroundthree-fifth (61%) belong to the backward sections of which around three-fourth belong to the
OBC category. The rest of the sampled households belong to the general category.
In terms of credit usage from banks, 84% of the respondents are loanee farmers availing
agricultural credit from banks. In terms of the land holding size, 55% of the respondents
within the insurance beneficiaries sample are large farmers, medium farmers constitute alittle over 30% while small and marginal farmers comprise 14%.
As far as the literacy level of the sampled households is concerned, only 10% of thehouseholds are illiterate. The literacy profile is also quite impressive with 36% of themhaving completed the primary/middle school level, 43% having completed intermediate/highschool level and the rest 11% being graduates. It indicates the sampled farmers having
modest education levels to be able to respond to all the questions regarding theperformance of WBCIS and NAIS.

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1.2

Satisfaction Analysis for Quantum of Sum Assured

Analyzing the satisfaction level among the farmers regarding the quantum of sum insured,
25% of the sample report satisfaction, with another 32% reporting partially satisfied.
However, a large percentage of them (43%) are not satisfied with the quantity of sum
insured that reflects the inadequacy of the scheme in covering the crop losses experienced
or expected by the farming community. Decomposing the sample into different social groups,
the lowest satisfaction rate is expressed among the general category (11%) followed by the
SC category (17%). Interestingly, ST (45%) and the OBC (37%), on the other contrary havea quite high satisfaction level. This may reflect on the fact that the type of crops and theamount invested in cultivation as well as the risks associated are greater for the general andSC categories. The cost of cultivation data has not been collected as a part of our survey,
but based on field work in the sample regions and interactions with agricultural scientists andfarmers during focus group discussions (FGDs), it can be argued that those who reportsatisfaction with the sum insured as being adequate enough to cover the cost of cultivation
given the expected value of production, have lower cost of cultivation on an average. It may
also be possible that there may be other state specific schemes for the ST and OBC
categories that do not cover the SC and general categories, making the high satisfaction
reporting groups to be happy with status quo, as far as sum insured is concerned.
Going by size class of land holding, small and marginal farmers are more satisfied (31%)
than the medium (26%) and the large farmers (24%). This trend might arise due to the very
nature of small and marginal holding agrarian life. These farmers eke out a living under more
deplorable conditions than others given their higher vulnerability to agricultural production
risks and even a small amount of support might reflect in higher satisfaction, compared tothe middle and large farmers who are better-off and have developed higher aspiration levels.
In terms of indebtedness categories, the level of satisfaction is higher in case on non-loanee
farmers than in case of loanee farmers. By education groups, the illiterates have a lower
level of satisfaction. This is a reflection of the established relationship between educational
levels and appreciation of the need for insurance. Another interesting observation is that thefarmers having education level up to the tenth standard have higher satisfaction level thanboth the illiterates and the better educated individuals.

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1.3

Satisfaction Analysis for Types of Risks Covered

In the analysis of the satisfaction about the types of risks covered by WBCIS, almost thesame pattern is observed as in case of opinion about the amount of sum insured. The
notable exception is the response pattern by indebtedness category where both the loanee
farmers as well as the non-loanee farmers have the same level of full satisfaction whereas
the loanee farmers have very high level of dissatisfaction compared to the non-loanee
farmers. This can be possibly explained by the inadequacies of the coverage of risk types
faced by the loanee farmers who have higher exposure to the mandatory crop insurance
scheme (NAIS), which in principle is more comprehensive than the pilot WBCIS, comparedto the non-loanee farmers. The structural differences in cropping patterns, access toprotective irrigation and debt-servicing obligation could also play an important role in driving
these differences in self-reported satisfaction.

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1.4

Satisfaction Analysis for Period of Risk Coverage

The general pattern as observed in the previous two sub-sections is also observed in thesatisfaction among the farmers with regard to the period of risk coverage with some
additional findings. In case of social group-wise analysis of satisfaction, it is very clear that

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compared to the ST and OBC category who have very high satisfaction rates, SC andgeneral category have very low satisfaction rates that may be ascribed to their higher
dependence on agriculture both due to the growing of longer duration crops with possible
overlapping of the crops’ growing season as well as low opportunities to hedge income
shocks by off-farm avenues in general. In case of loanee farmers, the higher level ofdissatisfaction may arise from the inability of the scheme to cover the credit risk to its full
extent.

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1.5

Satisfaction Analysis for Explanation on Policy Working

Explanation of policy working is a very important indicator that might make or mar theoperationalization of any agricultural insurance scheme in the developing world and our
findings corroborate the same. Farmers’ response to the questions on satisfaction with this
aspect does bring out the poor state of affairs in terms of explanatory power of the suppliersof insurance. The general category farmers who have been associated with higher literacy
and education levels report an abysmally low satisfaction (1 per cent). The other socialgroups also report low satisfaction levels in this aspect. The trend persists even for the
analysis based on size class of land holdings and there is no difference between thesatisfaction levels of loanee and non-loanee cultivators as far as explanation on policy
working of WBCIS is concerned. The inability of the marketing agents/intermediaries/delivery
channel to simplify and communicate complex actuarial terms to the farmers, or the inabilityof these parties to understand the mechanism and fundamentals of agriculture insurance
contracts themselves can be held responsible for the invariably low awareness levels
observed in the field. These stylized facts substantiate the case for customized massive
insurance education and financial literacy campaigns during the marketing phase.

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1.6

Satisfaction Analysis for Resolution of Queries regarding WBCISIn the analysis of satisfaction for the resolution of queries among the farmers, it is clear thatthe satisfaction is inversely related to the size of the holding and education level i.e. larger
farmers and more educated farmers report higher dissatisfaction levels. Also, the satisfaction

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level is quite low for the farmers falling under the SC and general category that clearlyreflects that there is some problem regarding the clarity on the pattern of cultivation practiced
by them or that there may be caste sensitive practices driven by vested interests that is
actually impeding the resolution. This hints at some politico-economy forces at play thatmight create problems in getting the resolution of queries regarding WBCIS a complicatedprocess in rural India.

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1.7

Satisfaction Analysis for Convenience in Enrollment under WBCIS

From the responses elicited from the farmers, it is clear that ST and OBC farmers, small andmarginal farmers as well as the loanee farmers (for whom it was mandatory), enrollment
under WBCIS is reported to be more convenient vis-à-vis other groups. The respondents
from the ST category report a very high satisfaction level at 72 per cent; small and marginal
farmers at 54 per cent and loanee farmers at 39 per cent, which is significantly higher thanreported by the other corresponding groups in the comparison. This is a positive
development in the sense that WBCIS could be claimed to have succeeded in terms of its
goals to provide adequate protection to the farming community, the most vulnerable of them
being the groups discussed above.

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1.8

Satisfaction Analysis for Weather as a Basis for Crop Insurance

There seems to be greater appreciation among the non-ST, non-OBC; medium and large
farmers and non-loanee and better educated farmers about the weather as the basis of crop
insurance. The reasons for low satisfaction levels among the other categories may arise
from the generally lower awareness and poor prior experiences with NAIS in contrast withthe satisfied groups. Other plausible reasons could be the heterogeneity in vulnerability to
production risks, default risk as well as the technological risk in case of small and marginal
farmers who have most likely not been exposed to the concepts of weather as a basis ofcrop insurance or weather insurance initiatives as their more educated, progressive or
wealthier counterparts.

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1.9

Satisfaction Analysis for Location of Weather Station

There is unequivocal consensus among the farmers across different classifications in theirdissatisfaction about the location of weather stations. The very high dissatisfaction levels or
extremely low satisfaction levels (0 per cent or 1 per cent) on an average cuts across all the
farmers groups we have analyzed so far. These are most likely borne out of their bad
experience in the accuracy of the weather predictions (forecasts) that is crucial for planting
and agricultural decisions. Unmitigated basis risk and the low correlation of measured
parameters with experienced losses or phenomena of microclimates are very likely to deflate
the satisfaction with the WBCIS in its current form.

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1.10

Satisfaction Analysis for Design of WBCIS Policy

The lack of knowledge surrounding the clarity about the actuarial rules and mechanisms
involved in the designing of weather insurance policies seem to be pervading across thefarm segments as the level of partially satisfied sampled farmers is more than half, whereas
the level of satisfaction is much lower than that of both partially satisfied as well as notsatisfied groups. However, there seems to be more dissatisfaction among the ST farmers
vis-à-vis other caste groups; loanee farmers vis-à-vis non-loanee farmers; and illiterates andhigh school educated vis-à-vis other education groups reflecting confusion among farmers
on the various important aspects of WBCIS discussed so far.

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1.11

Satisfaction Analysis for Responsiveness of Insurance Intermediary

On the question of responsiveness of the insurance intermediary, the overall satisfactionlevel is at a very low level of 5 per cent overall with the ST category reporting the highest
satisfaction level of 17 per cent. The satisfaction with responsiveness of insuranceintermediaries remains at very low levels (less than 8 per cent) even when we consider the

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differences across size class of land holding and remains so for the loanee and non-loanee
categories. Education appears to be worsening the satisfaction levels with the
responsiveness of the insurance intermediaries as expected, since the more awareness a
farmer has about the insurance mechanism, the more would be the complaints or queries
directed at the intermediaries and given the low level of general awareness amongintermediaries as proven earlier, the dissatisfaction levels are bound to be higher. These
perceptions about satisfaction with the insurance intermediation is a very important reminderof the practical problems that handicap faster diffusion of formal risk management in India
and the increasing need for better intermediary and consumer education initiatives for
WBCIS to deliver value to the end clients sustainably.

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1.12

Satisfaction Analysis for Mechanism regarding Grievance Redress

Satisfaction in terms of grievance redressal is at an extremely low level of 15 per cent on anaverage with the general category farmers reporting lowest satisfaction levels of 3 per centand the STs that of the highest level at 28 per cent. This corroborates the findings on very
low satisfaction levels in terms of responsiveness of the insurance intermediaries. It isnoteworthy that the SCs report a high dissatisfaction level of 68 per cent. The dissatisfactionlevels are seen increasing with landholding and education level and are at a level higher
than 40 per cent across all these sub-groups considered. These low satisfaction levels might
be explained by the low intensity of general insurance in general and low penetration ofagricultural insurance in particular across rural India. With the sector and the knowledge
base on what works and what does not work, huge gaps in grievance redressal mechanisms
exist as reflected in our findings.

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1.13

Satisfaction Analysis for Reliability of Weather Station & Data

A striking inference from the analysis of satisfaction with reliability of weather station andweather data regarding WBCIS is the high partial satisfaction levels for all the social groups
considered, with the overall percentage being at a high level of 59 per cent. The satisfactionlevel remains modest at around 20 per cent, without significant variations between andamong the categories considered, hinting at an inherent absence of information about thesignificance of weather data infrastructure in the study regions and the relevance ofspecialized information on agro-meteorological databases (and models) in dictating themechanism of WBCIS.

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1.14

Satisfaction Analysis for Time Taken for Intimation or Receipt of ClaimsTime taken for intimation or receipt of claims is a very sensitive element of agricultural
insurance scheme design and holds the key to the adoption of WBCIS by farming
communities in India. The satisfaction levels are at a modest level of 30 per cent overall,with the reported levels of satisfaction improving when we consider the communities by sizeclass of land holding. The small and marginal farmers seem to have had a satisfactory
experience with WBCIS claims receipt and turn around time which is reflected in theircomparatively higher satisfaction rating levels at 40 per cent. The loanee farmers’satisfaction level is almost double that of the non-loanee farmers and this hints at inherent
differences in perception or access to information for these structurally different categories of
farmers. As another important role of education in influencing farmers’ perceptions of theutility of weather based crop insurance, it is the graduate farmers who report significantly
higher dissatisfaction levels compared to their less educated counterparts. This is in line withthe accumulated evidence of education in positively influencing the perceptions around
WBCIS features, and substantiates the importance of disseminating product information andinsurance fundamentals among the less educated or illiterate farmers. This aspect has been
corroborated by Gaurav et al (2010) where they show the significantly higher returns toinsurance education and financial literacy intervention among lower than median literacy andfinancial literacy groups of farmers in Gujarat.

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1.15

Satisfaction Analysis for Usefulness of WBCIS as an Alternative to NAIS

Coming to the crux of the basis for the evaluation of WBCIS in India, when the farmers wereasked to rate their satisfaction levels in terms of the usefulness of WBCIS as an alternativeto NAIS, around 20 per cent farmers preferred WBCIS to NAIS. This level was the lowest
among the general category of farmers (7 per cent) while the SC, ST and OBC groups
reported high satisfaction levels of around 30 per cent. Interestingly, there is no significant

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variation among the satisfaction levels by size class of land holding and around 20 per centof the respondents in each category of land holding class are satisfied if WBCIS replaces
NAIS. As expected, the loanee farmers expressed lower preference for replacing NAIS with
WBCIS vis-à-vis the non loanee farmers as they have been habituated to NAIS given its
mandatory nature or WBCIS being mandatory for only a few seasons. On the other hand,the non-loanee farmers who have been primarily excluded from the NAIS as evident fromthe coverage data analyzed earlier in our study, WBCIS promises to be an interesting risk
management alternative, conditional on their knowledge of NAIS. With higher educational
attainment, the preference figures for substituting NAIS with WBCIS does not gather much
momentum as around 20 to 25 per cent report of being satisfied with this possibility, for thevarious educational groups considered. It can thus be inferred that the findings from theprimary data of our study does not provide substantial evidence for us to conclude that
WBCIS can replace NAIS without making some section of the farming community worse off(dissatisfied) in general, while making some other sub-group better off.

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1.16

Satisfaction Analysis for Effectiveness of WBCIS in Avoiding Political Risk

NAIS has been critiqued to be prone to political manipulation and an alternative like WBCIS
has been argued to emerge as a more stable scheme that is insulated against suchmanipulations or political risks. This hypothesis can be tested against the perceptions
reported by the surveyed farmers. Around 20 per cent of the farmers surveyed approve of
the superiority of WBCIS in avoiding political risks while land holding wise differences are notvery striking. It may, however, be observed that the difference to the response for thisquestion on satisfaction level is significantly different between the loanee and the loanee
farmers, with the loanee farmers expressing lower satisfaction of 19 per cent and the nonloanee farmers expressing an almost double satisfaction level at 34 per cent. This hints at a
possible air of insecurity among the loanee farmers who believe payouts under NAIS to bepolitically dictated and an alternative like WBCIS could perhaps be detrimental to future
payouts. More educated farmers seem to be having a greater preference of WBCIS over
NAIS in terms of isolation from political risks and these may be an outcome of the ability (orhigher awareness) of the better educated farmers to better correlate past political events or
vested interests in influencing payouts.

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1.17

Satisfaction Analysis for Effectiveness of WBCIS as Protection against
Crop Losses & Climate Change

Given the uncertainty around the effects of climate change on Indian agriculture, mostly theeffects on crop yields, the responses given by farmers are reflective of their ignorance of thesame and nature of assessments which are short-term in nature. This is evident from thevery low satisfaction levels reported by illiterate farmers (4 per cent) as against thesignificantly higher levels reported by their more educated counterparts. The satisfactionlevels around WBCIS as providing sound protection against crop losses and climate change
stands around 20 per cent, without drastic variations among the different social groups or
size class of landholding considered. Non-loanee farmers’ assessment of the beneficial
coverage provided by WBCIS is almost twice that of what the loanee farmers think (18 percent). This can largely be explained by the ‘bank loan’ nature of NAIS where insurance
comes attached with credit and the loanee farmers being habituated with a heavily
subsidized comprehensive crop insurance scheme where the yield losses are calibrated
against field based crop assessment losses unlike the WBCIS case.

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Annexure 2: Main Points from Interactions with Experts and Intermediariesa.

Affordability

There was a unanimous consensus among all the intermediaries interacted with, thatweather insurance products currently being marketed in India are not affordable to the small
and marginal farmers while the medium and big farmers might have the ability to pay though
the willingness to pay does not seem to be evident given low percentage of multiple unit
adoption hinting at non price factors depressing the demand for weather insurance among
this market segment. Premium rates in excess of 10 per cent do not justify as a risk
mitigation strategy for the vulnerable farmers if the frequency of payouts is not rationalized or
the probability of the policy paying out or not payout is not properly communicated to thefarming community. Based on its past experience with marketing rainfall insurance tothousands of farmers in Gujarat, Sajjata Sangh has succeeded in convincing NABARD to
subsidize the premium for the Kharif 2010 season and investing in automatic weather
stations. Farms of SEWA have also been provided low priced rainfall insurance for the last
four years given the sensitivity of the landless agricultural labourers and small-holder
farmers to premium rates (Cole et al 2009).
But the experts had differing views on the issue of the premium rates being high. Most
identified the host of loadings to the actuarially fair premium being the factor that inflates thepremium rate for the farmer. Basis risk and high cost of administering insurance to the ruralclients drive the premium rates into the low affordability region. According to some of theweather insurance product design and delivery experts, unsubsidized weather insurance
does not make sense for the small and marginal farmers in India today and the weather
insurance sector is rife with pricing problems. Actuarial rates would be at least 3 to 4 times
the average losses and the loading of administration and marketing charges makes theweather insurance products costly.
A few experts stressed the importance of subsidies in making agriculture insurance
affordable in a developing country like India and why there is no need to subsidize theprivate insurance. Cross-subsidization is always going to happen. The poor end up
subsidizing the rich or the small and marginal farmers subsidizing the bigger farmers is areality of crop insurance. The fundamental problem is that the claims rate is high inagriculture insurance as agricultural shocks are covariate in nature. An important point thatdemands attention is the scope for insurers to differentiate on the basis of product andservice attributes. On the importance of setting premium at affordable rates, the general view
was that premium with fair loading is desirable. The claims probability in agriculture
insurance is at a higher rate of 25 per cent or 30 per cent unlike other general insurance
product categories where it is even as low as 0.03 per cent for some product lines. This
idiosyncrasy should be factored into identifying loading factors that pull up the insurance
price. The Government of India should not subsidize the policy by tax payers’ money without
taking into account these considerations and take note of the fact that the system ofweighting is not transparent when the claims ratio is high as 80 per cent or 85 per cent on anaverage. Most of the agricultural insurance products have proportional reinsurance and theGovernment of India should be properly communicated about the actual pricing formula
adopted by the insurer.

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Reinsurers have been identified as important stakeholders who play an important role indetermining the pricing for the insurer who directly underwrite the risk while role of TPAs hashelped in data availability and claims management processes, which is in turn influences theloading factors and the actuarially fair premium or expected losses itself. The experts
indicated that the pricing done by the insurers in India was transparent and all the
stakeholders in the delivery system should be incentivized to deliver crop insurance
affordably. The importance of having an ‘actuarial regime’ for NAIS should be highlighted
and the weather based crop insurance schemes can then go alongside. Reduction in basis
risk would introduce conducive reduction in the premium rates. Lowering down of thesettlement region of NAIS and investing in weather infrastructure for better estimation of crop
losses in weather insurance are critical for making the crop insurance market participation
for farmers in India more cost effective.
b.

Information Availability and Ease of Enrolment

The interaction with subject matter experts and intermediaries who actively work with
farmers availing crop insurance and weather insurance, predominantly in the form of rainfall
insurance, reveals huge gap existing with regard to explaining the working of the policy toclients with low educational attainment and limited exposure to sophisticated financial
products and services.
The biggest challenges that the predominantly credit linked NAIS faces is the low level ofawareness among farmers and lack of knowledge of the claims management mechanism.
To complicate things, more sophisticated products like weather insurance have beenlaunched without focusing on the need to educate the farmers with low financial literacy andlimited financial experiences about sophisticated risk management products which arecomplex financial instruments. To evaluate the performance of the WBCIS vis-à-vis the
NAIS, it is very important to gauge how sensitive are our farmers to the principles of
insurance and subtle variations in the trigger and indemnification processes. BASIX’s
insurance experts reiterate the need for sprucing up availability of information about thepolicy mechanisms and the limitations of the insurance products during the marketing phase.
NAIS being mandatory does not face the problems of being marketed, while the banking
network could go a step further in raising the awareness levels.
AKRSP (I)’s experience is that farmers’ inability to access insurance is a problem.
Awareness about insurance scheme amongst the farmers is not that great. Illiteracy fromfarmers’ side and lack of willingness from the companies has compounded the problems of
the farmers. The same was iterated by Sajjata Sangh, which is a network of NGOs working
in the space of natural resource management in Gujarat and which has been promoting
Weather Insurance in the state over the last two Kharif seasons.
Experts from TNAU argue that the coverage of public crop insurance products is wider
given that it is mandatory for the loanee farmers, but with limited success in terms of actual
number of farmers covered and benefited, while the insurers have diversified with many
weather insurance products, the coverage of which, however, has not shown much
headway. Both yield and weather based insurance products have been delivered to thefarmers of Tamilnadu who face problems of enrolment given farmers’ poor understanding of
the enrolment process and the claims management procedures.

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According to Microensure, there is no system in place for systematic resolution of the
farmers queries, making the enrolment process very inconvenient and most farmers lose
interest in these insurance products if their queries are not timely addressed within a critical
window of the agricultural planning period when the farmers could consider paying for thepremium of a new or poorly understood product. Based on client interactions, it has been
established that the pricing is not communicated properly to the buyers. The marketing
teams are not even aware of the production communication challenges. The private insurers
do not seem to be keen on village level meetings and awareness generation among farmers.
c.

CoverageOn the coverage features of both NAIS and WBCIS, the experiences of the intermediaries
and views of the experts revolve around the three important aspects of Sum Insured, Typesof Risks Covered and Period of Coverage.
Some experts stressed on the fact that NAIS is needed for low probability extreme severity
loss events. There are glaring exclusions in NAIS Coverage. NAIS is synonymous with ‘Bank
Insurance’. Non-loanee cultivators (NLCs) constitute less than 3 per cent of the total farmers
covered under NAIS (cumulative) and cases of adverse selection with the inclusion of NLCs
has been observed in Maharashtra and Chhattisgarh. It is worth noting that till 2006, there
was not a single NLC in Andhra Pradesh as per the NAIS and only five farmers got covered
through the financial institutions in the year 2006 (for coriander). For these Rs.2700 was thetotal premium collected where claims to the tune of Rs.30, 000 were settled. The coverageof NLCs is the highest in Maharashtra as the compulsion on NAIS being mandatory for theloanee farmers has been relaxed.
The seasonality discipline is an important aspect of administering crop insurance in thedeveloping world. The NLCs face a tightened seasonality discipline with respect to theirloanee counterparts which is deliberately enforced upon them to bring in more discipline to
avoid misuse of crop insurance arising out of adverse selection. The shorter cut off dates (asmentioned in the guidelines) controls for bad risk farmers seeking insurance at a delayed
stage e.g. Latur and Osmanabad in 2006 and 2007 which were consecutive drought years
and crop failure was imminent, the farmers agitated and compelled the Government toextend the cut off dates to 31st August. The relaxations coincided with dry spells and assured
pay outs. A law and order situation emerged with a terrific rush around the banks to get thedeclaration forms and proposal forms filled up and submitted as the pay outs were a surething. Such adverse selection needs to be averted.
The Hon’ble Finance Minister’s announcement in the annual budget 2007 regarding
premium subsidy in fact came in after due consideration of the scope of providing fasterpayouts to farmers incurring financial losses on account of adverse weather conditions. The
premium rates of the two schemes have been kept at par to be an incentive compatible forthe farmers, and in the long run as long as the actuarially priced insurance covers are notaffordable to the small and marginal farmers in India, subsidies on the premium cannot bedone away with. The concessional premium regime has to give way to the actuarial pricing
regime gradually. Mostly annual commercial crops and horticultural crops, 50 per centsubsidy to start with, saw more commercial crop growers join the scheme. Over time there
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the farming community. The actuarial rate of 8 to 9 per cent is a binding constraint forfarmers.
Some experts believe that there are coverage issues in the crop insurance (NAIS) and theideal approach should be improving the coverage of protection given to the entire yieldrather than link them to crop loans only. The WBCIS pilots are very important to understandthe problems in reaching out to a larger section of the farming community who are mostly
unaware of the NAIS or have had bad experiences with it. It would also help us in a better
understanding of the take-up behaviour as well as problems with multiple insurance
purchases and repeat take-ups. Yield based insurance is very important in the Indian
scenario given the volatility in crop yields given its sensitivity to monsoon and other naturalcalamities. Farmers should be given the option to choose between WBCIS and NAIS.
Universal coverage should be pursued over the next few years and the farmers should haveat least a sound coverage under any of the two schemes to ensure modest liquidity at bad
times when they really need the pay outs. The experts opined that both WBCIS and NAIS
need to be continued and extended to the uncovered communities. Some concerns havebeen raised about the products being complicated by the private insurers while AIC has
improved in terms of its geographical coverage and commitment to cover more crops withevery passing season. Discontinuing of the products by insurers has been a problem and it
should be regulated against. The agriculture insurance sector should strive at introducing
simple products, the farmers, state Government officials and functionaries at the grass rootlevel should be able to objectively evaluate. Claim verification should be improved and ‘plain
vanilla’ products should be available. Riskier products should be innovated upon and thefarmers should be given an option to decide the additional level of risk cover given their risk
exposure and ability to pay.
A few experts opined that the coverage gap persists in terms of number of farmers being
reached out by crop insurance dictated by the focus on reaching out to the loanee cultivatorsand not the non-loanee cultivators in most cases. The weather insurance market is very thinin spite of the huge potential and untapped segments. Gaps in terms of coverage of Rabi
crops and other horticultural and high value commercial crops demand attention as well. It isonly by high payout frequency (without proportional increase in the premium because ofhigher payouts) is important to ensure higher take up. According to experts, two
stakeholders in the weather insurance business who hold a key to scaling up weather
insurance are reinsurers and third party administrators (TPA).
Discontinuation of products has been a major problem across all the areas where Weather
Insurance had been introduced sometime or the other. Lessons from the health micro
insurance delivery and product design can be incorporated into weather insurance. For
instance, the notion of co-payment could well be a standard feature of weather insurancewith the farmers bearing a certain critical percentage of the losses incurred. This would doaway with problems associated with information asymmetry.
The sum insured needs special attention in the near future as the level of indemnification
between input costs and output value should be attuned to local experiences. Farmers do
not actually get whatever is shown as the maximum sum insured in each phase as for eachphase there are caps on the payouts which are not transparently communicated to thefarmers.

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Basis for Settlement

Weather as basis for crop losses, location of weather station as an indicator of the weather
experienced at the farm and design of insurance policy to reflect key productions were
important areas that was discussed by all the intermediaries and experts.

The experts identified some important aspects of agricultural and farm insurance that needto considered seriously over the next five or ten years:•

The relationship between climate change and insurance effects are also not clear
and various scenarios for agricultural insurance in India need to be studied.

•

Horticulture and livestock insurance are important insurance segments whose
penetration are very low and should complement crop insurance or weather
insurance.

•

Financial literacy and insurance education is important for sustainability of properly
designed agricultural insurance schemes.

•

The density of AWS and IMD observatories needs to be increased. This holds the
key to better pricing of the risk products with maturity and introducing products
based on other parameters.

•

The correlation between crop yields and weather parameters need to be improved
to address the problem of basis risk and instill faith in weather based insurance
among the agricultural communities.

On the issue of weather infrastructure and need for immediate action to increase theinvestment in weather infrastructure in India, the most significant factors were identified by
the officials of the India Meteorological Department (IMD). IMD aired the concern that basis
risk is a major barrier to scaling up weather insurance in India. Around 32000 observatories
would be required to bring in reference within 5 km radius and improve therepresentativeness of weather data suitable for designing weather insurance products. They
raised questions pertaining to the management of the new weather stations or observatories
installed and how the AMC and verification would be standardized. They also cited earlierinteractions with the Ministry of Agriculture, Government of India on these lines. IMD isalready providing Agromet advisory services in 130 stations apart from their free online
forecast services for 5 days to the public.
The philosophy should be making use of the existing network of weather stations andobservatories to design comprehensive covers and writing weather derivatives with theavailable weather parameters. Location specific data based on IMD observatories and AWS
would take time. Other parameters need to be taken up to provide credence to weather
insurance models. Modeling should integrate agro-meteorological and agronomic
information and these call for more concerted inter-disciplinary efforts which do not seem tobe happening today. Crop data generation should be in tandem with weather datacollection. More collaborative research should be undertaken. The State Agricultural
Universities (SAUs), field units and insurers should chalk out the operational modalities ofsuch collaboration. Explaining the given scope of representation is important and so isconducting more simulations making use of data available from all the stations. In thisAgricultural Finance Corporation

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context, works by J.W. Hansen is deemed important where he has done simulations for
Africa. Pilot simulation exercises need to be taken up on similar lines in India. The IMD
officials stressed on the need for a Public-Private-Partnership (PPP) model to cover all thevillages in India that make use of frontier science and technology in solving the problems of
millions of farmers and the poorest of the poor.

e.

Settlement Process

The intermediaries cited stylized facts of the state of reliability of weather data in claims
settlement for weather insurance/WBCIS and the lags in the claims settlement process.
According to the experiences shared by BASIX, weather Infrastructure10 and reliability of
weather data are two limiting constraints in the Indian weather insurance markets today.
Though the promise of weather insurance that the BASIX story is synonymous with faces
serious doubts from skeptics in the farming community, academia and policy arena, it should
be well understood that without proper support from the channel partners and feedback fromthe ultimate consumers i.e. the farmers, even a basic financial product like savings bankaccount cannot experience high adoption rates, leave aside a complex product like rainfall
insurance.
Repeat take-ups have been suffering due to the claims not being declared timely andoperations team opposing to marketing of rainfall insurance products. This hints at how the
renewals cannot be sustained without continuous product improvements and constantsupport for the insurers. Number or volumes is what drive the private insurers to undertakenew crops and new areas. All these years BASIX has had generic products and in otherinsurance lines has had a master policy approach. With BASIX acquiring a ‘corporate
agency’ license, some regulatory challenges may crop up as IRDA regulation stipulates thecorporate agency to carry on business with one insurer only. In that scenario, Royal
Sundaram General Insurance Company has to start launching weather insurance productswith BASIX and the current insurer ICICI Lombard will have to stay out of the weather
insurance business with BASIX. These regulatory uncertainties are being sorted out.
Product development initiatives have stagnated and the area specific feedback from farmers
has died out. The problem of marketing rainfall insurance has emerged in spite of themonetary incentives in place. The insurance agents have been paid a commission of 15%
on the gross premium collected, but off late support from the channel partners are notforthcoming due to internal issues. After the 2003 pilots, the experience was conducive to
growth to innovations and consumer satisfaction for a few years. Since 2007-08, no change
in the product has happened and this situation has aggravated due to bad marketing
10

BASIX has relied on weather data from IMD, NCMSL and Ingen Technologies weather stations
and their officials believe, the best way to reduce basis risk is intensifying the network of rainfall
measurement and monitoring systems. This can double as an important weather and crop advisory
platform in the areas that are provided weather insurance as well.

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experiences, mistiming of sales activities and insufficiency of products to cover the croprisks. The most important problem of ‘basis risk’ remains to be addressed. The genericproducts approach to BASIX’s rainfall insurance business saw an exception in Chhattisgarh
and Rajasthan where crop specific insurance was provided. BASIX has never collaborated
with AICIL citing loyalty to its first insurer ICICI Lombard.
f.

Limits to Weather Insurance

It is an irony that the USP of weather insurance or index-based rainfall insurance was its
timely claim settlement (within 45 days of receiving weather data), but off late delays insettling claims have been reported. BASIX focuses on retail insurance business and giventhat rainfall insurance is another line of business and it is marketed along with otherproducts like life insurance, livestock insurance etc. There is also a limit on the percentageof the total portfolio that can be crop loans (20 per cent for BASIX’s portfolio) and out of
BASIX’s one million clients, only 15 per cent have been extended crop loans. Huge gaps inproviding rainfall insurance to its crop loans clients exist and servicing other non-client
pictures is not a priority for BASIX.
BASIX has been able to sell around 40,000 policies so far with claims to the tune of Rs.1
crore being processed so far. The Claims ratio has remained on the higher side of 50 to 60
per cent in the rainfall insurance business while it went up to as high a figure of 300 per centin Kharif 2009 season.
Political risks are rampant in the agriculture insurance market. In Anantapur district, every
farmer expects a payout as the crop insurance scheme has made it prone to receive
payouts once in every two years. These perceptions of rainfall insurance as “rainfall
lotteries” need to be tackled by strategic communication to the clients and better post sales
service to differentiate index-based weather insurance from the more comprehensive crop
insurance scheme. This knowledge can inform our choice in gradually choosing between
NAIS and WBCIS in the near future.
g.

Grievance Redressal

According to all the experts and stakeholders interacted with, it clearly comes out that inspite of a conducive regulatory environment for the promotion of micro insurance in general
and weather based crop insurance in particular, a standardized protocol for response of
insurance intermediaries on grievances and clearing of doubts regarding claims mechanisms
for grievance redressal is conspicuous by its absence. This calls for the need for a crop
insurance ombudsman and a mandatory communication of the possible ways of getting thegrievances redressed for the consumers during the marketing of weather insurance policies.
Having these red herrings and disclaimers in place would also necessitate the intermediaries
and insurers to give more thought to educating their clients about the products and services
they would end up paying for. This would create a win-win situation for all the parties to thecontracts.
h.

Overall EffectivenessTo gauge the overall effectiveness of WBCIS vis-à-vis NAIS, opinions on three criticalaspects were thoroughly evaluated. These aspects are :Agricultural Finance Corporation

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i.
ii.
iii.

Usefulness of WBCIS as an Alternative to NAIS
Effectiveness of WBCIS in avoiding political risks and manipulation of claims
Effectiveness of WBCIS as a protection tool against crop losses and climatechangeOn the important question of whether doing away with NAIS makes sense, the unanimous
response has been in the negative.
Most of the experts felt that no doubt weather insurance and WBCIS are very good products(with ample scope for product modification) the area yield crop insurance schemes are better
given their coverage of losses. In spite of the high administration costs and the high cost
incurred in loss assessment unlike the WBCIS, with lower correlation between rainfall andyield being established for the existing crops being covered, NAIS should definitely continue
in its present form.
On the decision to continue or discontinue WBCIS, the unanimous consensus was that
WBCIS should not be discontinued as it is a step in the right direction and as long as the
problems in marketing insurance in rural markets persist, any agricultural insurance productwould not be an exception. Given the low awareness among the NLCs and the inability of
the rural banking system to deliver the mandated insurance, the insurers should improve themarketing outlets or build upon existing marketing channels. Other parameters need to be
gradually developed, but having multiple perils covered under a single weather insurance
product would complicate the matter. Most insurers also resist changes in product design
drastically as the marketing agents need to be retrained regularly which might pull up
administrative costs, and hence the premium. The uncertainty loading by reinsures needs tobe curtailed to be capped.
The views of Swiss Re also reflect this idea of continuing with WBCIS and NAIS both. The
products are fine in their philosophy and the product structuring needs to be more sensitive
to the stakeholders across the value chain ensuring the simplicity and incentive compatibility.
By having a transparent claims process system in place, the true demand for the services
along with the efficiency of delivery mechanisms can be guaranteed.
The most important views on the overall effectiveness and the need for continuing boththese schemes with substantial modification come from the study conducted by TNAU. The
discussion with the farmers who were covered under different insurance products indicatedthat both yield as well as weather based insurance products were useful to them. However,these schemes need to be slightly refined so that they would be more effective. The
following suggestions by farmers are deemed important for the success of agricultural
insurance:

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Annexure 3: Case Studies
To capture the essence of the barriers to scaling up crop insurance and weather insurancein Indian conditions, we highlight the cases of two important non-governmental groups
actively working towards promoting agricultural risk management in India, namely Sajjata
Sangh and AKRSP (I).
CASE 1: Sajjata Sangh, Ahmedabad, Gujarat(i)Name of the Entity: Sajjata Sangh(ii)

Legal Structure: Network of NGOs(iii)

Address: C/O Development Support Centre, Nr. Govt. tube well, Bopal(iv)

Name of Respondent: Natu Macwana(v)

Designation of Respondent: Coordinator(vi)

Contact No: 09428826157(vii)

No. of Farmers Served: + 1500

(viii)

Experience in Insurance / Agriculture: Implementing Sustainable Community
based Approaches to Livelihood Enhancement (SCALE) Programme in Gujarat sincelast 8 years. Promoting Rainfall insurance in Gujarat since last three years

Please describe your background in the insurance/agriculture/finance/industry andyour current role?
In order to better understand the barriers to scaling-up weather insurance, Sajjata Sangh
had instituted a research study with the financial support from Aga Khan Foundation under
SCALE program in the year 2006 on providing a cushion to the farmers against crop losseson account of weather vagaries by providing Rainfall Insurance cover to the farmers. Basedon the study recommendations, 35 farmers were insured (covering 39 acres of land) against
the crop losses on account of uncertain weather perils like inadequate and untimely rainfall
in the operational area of its partner NGO SAVA (Saurashtra Voluntary Actions) in Jamnagar
in 2006. In the same year, the farmers also got a claim of Rs. 1010 under volume cover.
Taking forward this positive experience, SAVA mobilized 110 farmers to buy customized
product, thereby insuring 180 acres of land under groundnut in 2007.
With financial support from OXFAM INDIA, Sajjata Sangh expanded its outreach by making
Weather Insurance available in 425 villages of 8 districts and 25 blocks of the state. Eightpartners were involved in promoting weather insurance which at present is available forthree crops namely cotton, groundnut and maize for Kharif 2008 and 2009 season.
As a result of these efforts, 1377 farmers, of which 59% are small and marginal farmers and
9% are women farmers purchased rainfall insurance. Due to the strong follow up with
regional and head office of AIC, it declared the claim in just 15 days after the completion of
product stages. Out of the insured 1377 farmers, a total of 1277 farmers have received claim

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under deficit and excess rainfall cover. A total sum of Rs. 17,98,000 was received as payout
against the premium of Rs. 17, 92,101.
What are the three most influential trends facing the Indian Crop Insurance sector
today? What other trends may become influential over the next 5-10 years?
Since traditional risk management strategies of the farmers fail against covariate weather
shocks and the existing multi-peril crop insurance scheme (NAIS) has not performed
satisfactorily in terms of its coverage of the predominant non-loanee farmers, weather
insurance emerges as an important alternative.
During the annual budgets for F.Y. 2006-07 and 2007-08, the Finance Minister of India had
allocated subsidy to the tune of INR 1 billion and INR 500 million respectively for giving
thrust to this promising financial instrument for agricultural risk management. Theseannouncements resulted in the implementation of the pilot Weather Based Crop Insurance
Scheme (WBCIS) across select locations for specific crops since Kharif 2007. The idea wasto provide insurance protection to the farmers against adverse weather incidence, such as
deficit and excess rainfall, high or low temperature, humidity etc. which are deemed to
impact adversely the crop production. Of the total premium, while a part thereof shall bepayable by the insured cultivator, and the balance shall be borne by the Central Government
and State Government on 50:50 basis and paid up-front to the insurer as Premium Subsidy.
Under this scheme the risk incepts only upon the insurer’s receiving of the full premium as
per crop-group specific (wheat, cereals, millets, pulses, oilseeds, annual
commercial/horticultural crops) premium slabs.
Owing to the high subsidy to the tune of 50%-75% of the actuarial premium, Weather
Insurance in India has been tried by a large number of farmers in states across India withextremely impressive response in states like Bihar and Rajasthan. The positive response of
farmers with provision of subsidy and premiums comparable to NAIS proves theindispensability of bringing Weather Insurance on an equal platform with the existing crop
insurance scheme (area-yield crop insurance or NAIS) which is highly subsidized by theGovernment.
On account of its unique and significant benefits (like faster payouts, no need for costly crop
cutting experiments to assess yield loss, factoring in separate risk covers for different
phases of the crop phenology, unit based policies to enable the farmers to choose thenumber of policies as per perceived risk and ability to pay) vis-à-vis the traditional crop
insurance (area-yield crop insurance), Weather Insurance holds a worthy proposition for atypical Indian farmer who is either oblivious of its traditional counterpart or has given up on it(area-yield crop insurance) due to its glaring limitations. The sum-insured for the loanee
cultivators and non-loanee applicant cultivators as well as the payout mechanisms differ
drastically. For the loanee cultivators, the crop insurance being mandatory, the premium forthe notified crop is deducted from the farmers’ sanctioned credit. The large number of nonloanee cultivators who are excluded from the formal credit delivery system are usuallyignorant or have fallacious understanding of crop insurance. Practical challenges indelivering crop insurance to these needy sections of agricultural community remain to beaddressed.

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How do you assess the journey of Pilot Weather-based Crop Insurance Scheme(WBCIS) in India?
From its first pilot in India during 2003, weather-index based insurance has presented itself
as a potentially useful risk mitigation instrument for farmers undertaking rainfed agriculture.
The conceptual appeal of weather-index based insurance has elicited expectations ofvarying optimism, ranging from “Weather-index based insurance can also help alleviate
chronic poverty (USAID 2006)” to “major input suppliers could offer this type of insurance
product in order to increase reach and uptake (Hess and Syroka 2005).” It was hoped thatweather-index based insurance would be tied to input credit thus enabling microfinanceinstitutions (MFIs) in India to effectively lend to the farming community. Almost all the leading
MFIs of India, with the exception of BASIX, Dhan Foundation and a few others, have been
bypassing the Indian farmers because of the peculiar cash flows and the higher level ofexogenous risks (predominantly weather risks) in farming. The continued neglect of farmers
by such MFIs even with increased awareness and understanding of weather-index based
insurance indicates a deeper concern regarding the efficacy of this instrument.
On the other hand, there exists few doubts regarding the relevance of weather-index based
insurance for a subsistence farmer systematically exposed to the vagaries of weather. Skees(2007) acknowledges that the effects of weather risk are felt most acutely at the household
level, particularly by poor, vulnerable agricultural households, the majority of which are
subsistence farmers. Considering the significant number of pilots for weather-index based
insurance in India, clear-cut indications of the acceptability of weather-index based
insurance by Indian farmers as an effective measure to counter weather risks are still amiss.
The question put forth by Cole et al. (2009) “Why don‘t more households participate whenformal markets are available?” epitomizes the lack of clarity regarding willingness of farmers
to adopt formal risk management instruments like weather-index based insurance.
In general, what are the 3 most significant challenges faced by WBCIS in India today?
Nevertheless, the three biggest challenges in scaling-up affordable weather insurance are:a) designing a weather index with higher predictive capability to proxy crop losses taking
into account the inter-farm variability at an acceptable level of disaggregation; andb) the large ‘basis risk’ inherent in the rainfall index which is the most preferred andwidely-adopted weather index in India.

c) products available have been skewed towards the major Kharif commercial crops in
the region and affordable weather insurance for Rabi and other horticultural crops arelacking in the state.

These three challenges emphasize the need for innovations and experience-based initiatives
revolving around designing and delivering a complex financial contract like Weather
Insurance to a vast population of uninsured agricultural households (both cultivator andagricultural labourer households, or anyone whose livelihoods is intrinsically exposed to thevagaries of weather induced outcomes). Such initiatives have to integrate essential support
services and value-added components that can lend sustainability to these initiatives throughcontinued patronage by the client-farmers.

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In general, what are the 3 most significant opportunities of WBCIS in the next 3-5
years?
The promotion of Weather Insurance in a sustainable manner would help inculcate aworthwhile practice among the farmers of Gujarat for protecting themselves against crop
losses arising from an external, uncontrollable factor like weather and in the processcontribute to securing their livelihoods by stabilization of their incomes. The need forfinancial support also emerges from the products being in a stage of infancy in terms ofaccuracy of the correlation between measured weather and realized yield losses given thelack of longer disaggregate location specific adequate weather parameter measuring andmonitoring infrastructure. The premium also needs to be brought down gradually as theloadings in the insurance pricing reduce and administrative costs are rationalized by the
insurer. This is possible with a scale that imparts higher geographical diversification of riskon the insurer’s portfolio in the long run.
These products being very complex and given the limited financial experiences of the
farmers (most of whom do not even have simple savings products and life and non-life
insurance coverage is not universal) communicating the costs, benefits and design
mechanism of weather insurance to farmers, the true demand for weather insurance remains
deflated. We also hosted a first of its kind study of the determinants of rainfall insurance
adoption by farmers in three talukas of Gujarat (Khambha in Amreli, Jambusar in Bharuch
and Ghogha in Bhavnagar) in Kharif 2009 season. The study was supported by the ILO
Micro insurance Innovation Facility. This study has given us a fair idea of the factors that
influence farmers’ adoption of weather insurance and the most important finding of the study,
that promotional campaigns and training sessions for the farmers as done by Sajjata Sanghpartner NGOs in the study talukas has a significant impact on take-up. Given the success of
this study and the importance of similar initiatives elsewhere, ILO has started highlighting ourstudy to their grantee communities and experts worldwide.
How do you assess the reliability and accuracy of Private/Third Parties providing
weather data through automatic weather stations? How can these entities play adefining role in scaling up of WBCIS?
The single most important aspect that has emerged during the field research and pilots on
Weather Insurance relates to the location of weather station based on which the proposed
Weather Insurance contracts would be settled. The location of weather station for contract
settlement has to be representative of the loss exposure of all the underlying farmer clients
and, therefore, has to be finalized in consonance with the distribution of farmer clientscovered under a common Weather Insurance contract. It is useful to always remember that
there is a trade-off between the geographical coverage of a Weather Insurance pilot and thebasis risk inherent in it due to the costs of weather stations. Therefore, it would always be
useful to have ‘geographically-focused’ and ‘location-intensive’ pilots for optimizing thebenefits of weather stations. Due to the high flexibility of location, proven reliability andtimeliness of data supply by third-party weather stations, they are gaining increasing groundfor Weather Insurance pilots. The agreements with Third-Party Weather Data providers haveto be fairly meticulous for ensuring better returns from installation of farmer-location-specificweather stations. This is also imperative given the increased frequency of variations on
account of microclimates within an agro-ecological zone and the data from this dense
network of weather stations would also enable rationalization of risk mitigation and disaster

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management programmes of the respective departments and the State Government/Central
Government.
Earlier, the Government and the insurance company relied only on Indian Metrological
Department (IMD) stations for recording of rainfall data. In Gujarat, there are only 16 IMD
Centers established at various places which was increasing the geographical distance
between our field areas to respective reference weather stations to as high as about 100 km.
This unacceptable degree of basis risk defeats the very purpose of having weather
insurance products sensitive to local conditions. Also historically, there have been different
rainfall patterns in neighbouring talukas for instance - Mangrol and Mailiya in Junagadh
district. Hence, the above mentioned respective IMD stations for our defined areas were notentirely reflective of the rainfall pattern in our field areas. After realizing the above fact, AIC
agreed for other Government weather stations like agriculture universities, KVKs, GSDMA,
irrigation department etc. to take as a reference weather station for recording of rainfall data.
Still there are some pockets of villages in some blocks which are at a distance of more than
40 km from the above agreed reference weather stations. To the extent practicable, such
Reference Unit-Area shall be restricted to 10 km radius around the Reference Weather
Station in case of rainfall. A higher radius of around 40 km is feasible for writing non-rainfall
parametric insurance but for rainfall insurance products, a distance beyond 10 km makes the
product impotent to deal with the experience of yield losses in the concerned areas.

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CASE 2: Aga Khan Rural Support Programme (India) [AKRSP], Ahmedabad, Gujarat(i)Name of the Entity: Aga Khan Rural Support Programme (India)(ii)

Legal Structure: Section 25 Company(iii)

Address: Aga Khan Rural Support Programme (India), 9th Floor Corporate
House, Opp. Dinesh Hall, Off. Ashram Road, Ahmedabad-09, Gujarat(iv)

Name of Respondent: J P Tripathi(v)

Designation of Respondent: Area Manager (Gadu)(vi)

Contact No: 09825464902(vii)

Total Full-time Employee Strength of Entity: 235(viii)

No. of Farmers Served: 100000(ix)

No. of Offices Operational in India: 27 offices (Head Quarters in Ahmedabad)(x)

Experience in Insurance / Agriculture: 2 years(xi)

Details of Experience in Agriculture Insurance / Crop Insurance: Since 200809, we have been involved in preparation of the WBCIS product modules for ourfield area in Gujarat and MP on cotton, groundnut and soyabean crops with
rainfall as the parameter for the weather. In 2009-10, we have implemented the
WBCIS in our field areas of Gujarat for cotton and ground nut crops.

Please describe your background in the insurance/agriculture/finance/industry
and your current role?
Other details are as above but as far as the present role is concerned, we are involved indisseminating the information about the product to the farmers in our field areas and inassociation with AIC we have helped in designing the weather insurance product forcotton, groundnut crops.
What are the three most influential trends facing the Indian Crop Insurance sector
today? What other trends may become influential over the next 5-10 years?
Farmers’ inability to access insurance: Awareness about insurance scheme amongst
the farmers is not that great. Illiteracy from farmers’ side and lack of willingness from thecompanies has compounded the problems of the farmers.
Lack of transparency in claims disbursement (in case of NAIS): The small numberof farmers who are able to reach the insurance systems have stepped back as there isno transparency in settling of claims and if at all there are settlements, it takes years bythat time calamity has taken its toll.

High premium rates (in case of WBCIS): The premium rates of WBCIS is very high in
our area it costs between 9-16% and it is really high. Why cant these insurance schemes
are designed like the medi-claim schemes where the premium rates are less than 1%.

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What specific influence will these short & long term trends have on Crop
Insurance sector in India in the next 3-5 years? Who will benefit the most fromthese changes?
This sector will have to innovate itself in order to save itself from getting lost. Innovation
for mass awareness, easy services for small and marginal farmers and also on reducing
the premium rates. It will definitely need support from Government.
Who is at greatest risk from these changes?
If changes do not happen for supporting the crop insurance sector and farmers reachingout for crop insurance in such a case it is the farmers who are at great risk – climatechange patterns and no insurance is a big catastrophe in waiting.
How do you assess the journey of Pilot Weather-based Crop Insurance Scheme(WBCIS) in India?
It is like a refreshing cool wind in hot summers – having all the formula (designed very
meticulously for crop and region, transparent calculation methods, fast claim
disbursements) for success except the premium rates.
Where do you see WBCIS in the next 3-5 years?
It is bound to grow provided premium rates are reduced and focus given on its proper
extension amongst the farmers.
How do you assess the delivering ability of WBCIS compared to that of Area Yield
Insurance (NAIS)?
Although it covers lesser risk but it has the capacity to grow big. Comparing it with NAIS
is quite premature and inappropriate. NAIS is hugely subsidized on the other hand
WBCIS is not, even their presence in the market is also different, but as said earlier
WBCIS is there to stay provided it is taken care of properly.
In general, what are the 3 most significant challenges faced by WBCIS in India
today?
•

Lack of weather automated stations at block levels – we need at least two stations
per block/ taluka•

High premium rates•

Lack of a good extension mechanism

In general, what are the 3 most significant opportunities of WBCIS in the next 3-5
years?
•

Wide area pilots has created some swirl in various parts of the country, whichshould lead to easy expansion•

Scope for Innovation in designs•

Can be adopted by small farmers as well even if he is not accessing the bank loans

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What farmer needs related to Crop Insurance are not being addressed effectively
today?
A good comprehensive cover from – weather perils and market fluctuations
What are the main challenges to the repeat purchase of WBCIS?
No package schemes available – current design address the farmers’ need of threemonths. It has to be designed for multiple years and multi season (Kharif, Rabi and
Summers).
How can these challenges be overcome?
Making year round risk cover available for farmers on the various crop combinations theygrow and installation of automatic weather stations in every block. Of course withaffordable premium rates.
What could be done to leverage technology for improving the accuracy of WBCIS
and NAIS?
For any insurance scheme, it is important to establish the transparency in deciding
claims and keeping political influence out.
In terms of WBCIS, what drives the farmer’s purchase decision-making process?
Premium rates and weather station location.
From your perspective, what could an Insurance Company underwriting Crop
Insurance do to bring greater value to insurance intermediaries?
•

Good extension mechanism•

Low premium rates•

Comprehensive risk cover•Fast grievances redress mechanism

How do you assess the reliability and accuracy of Private/Third Parties providing
weather data through automatic weather stations? How can these entities play adefining role in scaling up of WBCIS?
We have found them quite reliable (we were linked with NCSML). They definitely have agood role to play as it will take years for Government to establish the automatic weather
stations and even if they do, its maintenance and accuracy will remain a sector wherethese agencies will have to be involved. In such a condition, the third parties can play avery good role. But under current, they are charging very hefty price for weather stationand data (5000/month /station).
What do you suggest to derive the best benefits from NAIS and WBCIS,
considering their respective strengths? In case these are to co-exist, what needs
to be done to facilitate farmers in defining the future role for these two variants?

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WBCIS will need heavy investments form Government firstly for installation of the stationand on the other hand in subsidizing the premium rates. Once the above is done, farmer
will have good risk cover for their inputs and also for their output (large samples aretaken for calculating the production losses in a region in a transparent manner).

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Annexure 4: List of NGOs / Micro Finance Institutions (MFIs) / Insurance Brokers /SAUs /Subject Matter Experts Covered during Primary Research
S.
No.

Name of Institution /Name of Officials interacted with*1.

Krushi, Hyderabad
Mr. Premchand, GM
BASIX, Hyderabad
Mr. A. Satheesh, Head (Insurance),
Mr.Venu P., AM and
Mr. Vatsal Joshi, Consultant (Micro Insurance)
Microensure / Micro insurance Agency, Hyderabad
Dr. G.Srinivasa Rao,
Head, Agriculture Risk Management and InsuranceCentre for Agriculture and Rural Development Studies(CARDS), Tamilnadu Agriculture University, Coimbatore
Dr. M. Chandrasekaran, Professor and Head, Deptt. Of
Agriculture Economics
Dr. K. Mani, Prof. Agri. Econ.
Dr. R. Venkataraman, Prof ARM
Dr. D. Suresh Kumar, Assoc. Prof, Agri. Econ.
India Meteorological Department (IMD), Pune
Shri H.R.Hatwar, Additional DGM
Dr. R.P.Samui (Dy.DGM)
Dr. N. Chattopadhyay, Director, Agro Met, IMD
Indian Agricultural Research Institute,
Division of Environmental Sciences, Delhi
Dr. P.K. Aggarwal, National Professor
Yes Bank, Mumbai
Mr. A. Chakravarthy
Sajjata Sangh, Ahmedabad
Mr. Natu Macwana, Coordinator
The Aga Khan Rural Support Programme in India [AKRSP (I)],
Ahmedabad
Mr. Apoorva Oza, CEO
Self Employed Women’s Association (SEWA), Ahmedabad
Ms. Reemaben Nanavaty, Director, Economic and Rural
Development Programme
Ms. Chhaya Bhavsar, District Coordinator of Bodeli District,
Gujarat, SEWA/Project Manager, CMF-SEWA Weather
Insurance ProjectCentre for Insurance and Risk Management(CIRM), Institutefor Financial Management and Research (IFMR), Chennai
Ms. Rupalee Ruchismita, ED
Centre for Sustainable Agriculture (CSA), Hyderabad/Wardha
Mr.Vijay Barapatre, Coordinator
Mr. Chandrashekhar Dorlikar, Coordinator
Gokhale Institute of Politics and Economics(GIPE), Pune
Dr. Rajas Parchure, Director in Charge

2.

3.

4.

5.

6.

7.
8.
9.

10.

11.

12.

13.

Agricultural Finance Corporation

Category(NGO/MFI/Broker/SAU/
Subject Matter Expert)
NGO/MFI
NGO/MFI

Micro Insurance Intermediary

SAU

Technical Institution

Research Institution

MFI/Broker
NGO/MFI
NGO/MFI

NGO/MFI

Subject Matter Experts

Sustainable Farming andTraditional Insurance
Mechanism Experts
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14.

15.
16.
17.

18.
19.
20.

21.

22.
23.
24.
25.
26.

27.
28.

Swiss Re, Mumbai
Ms. Harini Kannan, VP,
Weather and Environmental Risk
Dr. Shawn Cole,
Harvard Business School, USA
Dr. Jeremy Tobacman,
Wharton Business School, USA
College of Agriculture and Banking (CAB),
RBI, Pune
Faculty Members
Dr. Dewan Singh, Prof. & Head, Department of Agricultural
Meteorology, Haryana Agricultural University, Hisar
Dr. G.S. Bains, Prof. & Head, Department of Agricultural
Meteorology, Punjab Agricultural University, Ludhiana
Dr. V.P.N. Singh, Prof. & Head, Department of Agronomy,
Chandra Shekhar Azad University of Agriculture &Technology, Kanpur
Dr. Abdus Sattar and Dr. N.K. Choudhary, Head, Departmentof Agronomy, Rajendra Agricultural University, Samastipur,
Bihar
Dr. P.S. Dharamraj, Project Director, Agriculture Researchstation, University of Agricultural Sciences Gulbarga
Dr. Raju Teggelli, Programme Coordinator, Krishi Vigyan
Kendra, University of Agricultural Sciences, Gulbarga
Dr. Venkatesh Chief, Agro Climatology Division, RARS,
University of Agricultural Sciences, Bijapur
DCM Shriram Consolidated Ltd (DSCL), New Delhi
Mr. Saket Jain, CFO
JB Boda Group, Mumbai
Mr. P.P.Rao, General Manager
Mr. Varun, JB Boda Reinsurance Group
Centre for Microfinance (CMF), Chennai
Mr. Justin Oliver, Executive Director
Mr. Nirupam Datta, Research Scholar,
Indira Gandhi Institute for Developmental Research (IGIDR),
MumbaiInternational Reinsurance
Company
Subject Matter Expert
Subject Matter Expert
Training & Research Institution

SAU
SAU
SAU

SAU

SAU
SAU/KVKSAU
Brokers
Brokers

Weather Insurance
Operations Expert
Agricultural Economics
Expert*excluding officials of State Government and implementing insurance companies

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Annexure 5
Report on Additional Aspects indicated by Additional Secretary (Credit & Coop.),
Ministry of Agriculture, Government of India during the meeting held on 23.6. 2010
I. Comparative Analysis of Weather Insurance Products of AIC and ICICI Lombard

Study of Rainfall Insurance Contracts implemented in Rajasthan during Kharif 2009

A comparative analysis of rainfall insurance products of AIC and ICICI Lombard can serve
the following key objectives:a)To understand the strengths and weaknesses in the design of rainfall insurance contracts
offered by these insurersb)To understand the reasons for variation in the payout frequencies of the rainfall insurance
contracts offered for the same locationc) To determine the economic value offered by the rainfall insurance contracts of AIC andICICI Lombard
Among the Indian states that have made the greatest progress under the pilot WBCIS
scheme, Rajasthan can be regarded as the leader both in terms of outreach and penetration.
The Kharif 2009 season turned out to be the worst in India during the last 27 years. Western
India, particularly Rajasthan, suffered badly from drought. 26 out of the 33 districts in
Rajasthan were declared as drought-affected during Kharif 2009. This debilitating season
provided an opportunity for rainfall insurance to prove its benefits for farmers. Based on thedata provided by AIC and ICICI Lombard related to their rainfall insurance contracts for
Rajasthan during Kharif 2009, the following contracts are analyzed below for meeting theobjectives outlined above.

i)Groundnut and Maize rainfall insurance for Chittorgarhii) Guar rainfall insurance for Jodhpur and Jaisalmer
The term sheets and payout table for the above contracts are provided as Annexure to thisnote.

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Fundamental Design of Rainfall Insurance Contracts
The involvement of designated agencies of the State Government in the administration of
WBCIS has led to the standardization of the fundamental design of WBCIS products. Since
weather insurance is aimed at protecting farmers against agricultural production risks, there
has been undue emphasis on the opinion of agricultural experts for suggesting andreviewing product designs of weather insurance contracts. Weather insurance products lie ata crossover of agriculture, statistics, meteorology, and financial economics, each of which isa specialized field of knowledge with limited expertise available. In the absence of an
integrated and specialized evaluation of weather insurance products, there are flagrant
weaknesses that abound in the contracts offered under the Pilot WBCIS scheme.
Table 1: Comparison of Designs of Groundnut and Maize Contracts for Chittorgarh
Groundnut

Maize

Design Feature

AIC

ICICI
LOMBARDAIC

ICICI LOMBARD

Sowing Trigger/Cover

N

N

N

N

Variable/Dynamic Start Dates

N

N

N

N

Y

N

Y

Y

Y

Y

Y

Y

Y

Y

Y

N

21 June-21 June25 June30 Sept-

30 Sept

29 Oct1 July1 July1 July-31 Aug

10 Sept

31 Aug

-

2.5

0

2.5

-

20

19

20

-

30

60

30

-

100

12

100

-

74

-

75

17

89

-

88

84

83

-

67

37

Daily

Two-day

Daily

Fortnightly &Deficient Rainfall Volume
CoverMultiple Strike/Trigger values
for Deficient Rainfall Covers
Rainfall Distribution / Dry
Spell Cover (CDD)
Deficient Rainfall Volume
Cover Start Day
Deficient Rainfall Volume
Cover End Day
Rainfall Distribution / Dry
Spell Cover Start Day
Rainfall Distribution / Dry
Spell Cover End Day
Dry Day Rainfall Threshold (inmm per day)Consecutive Dry Day Strike(Days)
Consecutive Dry Day Exit
(Days)
Percentage of Max Payout for
31 Consecutive Dry Days
Percentage of Max Payout for
10 mm Rainfall Volume in
Phase 1
Percentage of Max Payout for
10 mm Rainfall Volume in
Phase 2
Percentage of Max Payout for
10 mm Rainfall Volume in
Phase 3
Excess Rainfall Index

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Groundnut
Design Feature

Maize

AIC

ICICI LOMBARD

100

40

AIC

ICICI
LOMBARD
more

100

0

Computation Period
Percentage of Max Payout for
120 mm Rainfall Volume on
any 1 Day after 15 Sept

Key: Y- Yes; N-No
Key Observations based on Table 1:
 AIC has offered both deficient rainfall volume and deficient rainfall distribution (Consecutive
Dry Days or CDD) covers in the contracts for groundnut and maize in Chittorgarh. On theother hand, ICICI Lombard has offered only one of the two deficient rainfall covers aspart of the contracts for groundnut and maize in Chittorgarh.
 Sowing dates have a proven and significant influence on the occurrence of subsequentcrop stages and their durations. For the Kharif season, the sowing date is a function ofsowing rainfall which varies from location to location. For all the four contracts analyzed
above, the sowing dates for all the insured plots have been deemed to be the same. This
is incompatible with the ground realities where farmers within the same village are seen
to be sowing their crops on different dates. Variation in sowing dates leads to different
start dates for subsequent crop stages which follow sowing.
 Consecutive Dry Days (CDD) cover is one of the two deficient rainfall covers and is aimed
at insuring farmers against long dry spells that may culminate into losses from moisturestress. Under this cover, farmers will be indemnified for dry spells if there are ‘X’ numberof consecutive days with daily rainfall lower than 2.5 mm (for AIC) or equal to 0 mm (forICICI Lombard). If there is a rainfall incrementally greater than 2.5 mm (for AIC) or 0 mm
(for ICICI Lombard) just 1 or 2 days before ‘X’ number of consecutive dry days are over,farmers will not be entitled to any insurance payout on account of dry spells (or moisturestress, in effect). It is worth considering as to how a rainfall of less than 5 mm cancompensate for the moisture stress of a period as long as 18-20 days or more.
Furthermore, for such small quantum of rainfall (< 5 mm), the probability of lower rainfall
received by most farms within the insured area will also be quite high. Covers as thisone, reduce the insurance of the farmer to a largely discrete, binary (payout/no payout)
outcome which is not representative of the nature of loss experience (continuous
variable) in the farmers’ fields. Consecutive dry days cover is an extremely useful cover
but its definition and implementation under the pilot WBCIS have largely made it akin toa rainfall lottery. In case of the CDD covers provided by ICICI Lombard, it is staggering toknow that even 31 consecutive days of zero rainfall entitle an insured farmer to 12% ofAgricultural Finance Corporation

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sum assured under this cover whereas a farmer opting for the CDD covers from AIC
could easily claim 100% of maximum payout for this cover.
 Rainfall volume cover protects farmers against crop losses from stage-wise rainfall falling
below a threshold quantity (in mm). The threshold quantity under rainfall volume covercan be either the normal stage-wise rainfall or a critical rainfall required for sustenance ofthe crop against severe moisture stress. Normal stage-wise rainfall values are usuallyprovided in scientific literature like crop calendars, agro-meteorological publications.
Critical rainfall for sustenance against severe moisture stress is highly preferred byinsurers as the threshold quantity under the rainfall volume cover. The limitation of using
critical rainfall for sustenance as the threshold quantity vis-à-vis normal stage-wise
rainfall is that the former is mostly a small percentage of the latter. For example, thethreshold quantity for the third phase of the maize rainfall insurance for Chittorgarh is 50
mm for AIC (for a 41 day phase) and 80 mm for ICICI Lombard (for a 75 day phase).
 Multiple trigger or strike values are used by both AIC and ICICI Lombard to modulate
payout rates / notional. A tendency to keep payout rates / notional considerably highnear the exit value is a common practice. However, sometimes the differences in payout
rates/notional for various levels of trigger/strike values becomes too lopsided. Under the
deficient rainfall volume cover offered by ICICI Lombard for maize in Chittorgarh, a
farmer-subscriber was entitled to a mere 17% and 37% of the maximum sum assured forPhase 1 and Phase 3 when the total rainfall in each of these phases would have been
just 10 mm. The corresponding percentage of maximum sum assured payable under asimilar cover by AIC would have been 75% (for Phase 1) and 67% (for Phase 3)
respectively.
 Heavy and continuous rainfall within a short period of 1/2/3 days can cause damage tomost Kharif crops. Excess rainfall cover tries to provide protection to farmer against suchunfavorable rainfall events. Excess rainfall cover may also include coverage for untimely
rainfall which, in case of Kharif crops, may take place during stages like flowering andharvest. Nevertheless, continuous and heavy rainfall is a more potent threat during the
Kharif season and constitutes a major worry of farmers. Continuous and heavy rainfall
for more than 3 consecutive days is a relatively rare phenomenon. An excess rainfall
cover should preferably encompass 1-day excess rainfall followed by excess rainfall
events over 2 and 3 successive days. It is surprising that the definition of excess rainfall
in some Kharif weather insurance covers is so anomalous that they treat a cumulative
rainfall of more than 150 mm during a 44 day period as excess rainfall.

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Evaluation of Pilot Weather Based Crop Insurance Scheme A review of the excess rainfall covers offered for groundnut and maize in Chittorgarh
indicates that AIC has considered the daily rainfall as the index for excess rainfall
whereas ICICI Lombard has considered the cumulative rainfall during any twoconsecutive days as the excess rainfall index for groundnut and total rainfall during a 75day period as the excess rainfall index for maize. Assuming a 120 mm rainfall on any
one day after 15 September till end of excess rainfall cover period, the percentage ofmaximum payout payable to an insured farmer is 100% of maximum sum assured (forexcess rainfall cover) under both contracts of AIC. Taking the same conditions, theexcess rainfall covers by ICICI Lombard entitle the farmer-subscribers to 40% of
maximum sum assured under the groundnut contract and 0% of maximum sum assured
for the maize contract.
Table 2: Comparison of Designs of Guar Contracts for Jodhpur and Jaisalmer
Jodhpur
Design Feature

Jaisalmer

AIC

ICICI
LOMBARD

AIC

Sowing Trigger/Cover

N

N

N

N

Variable/Dynamic Start Dates

N

N

N

N

Y

N

N

N

Y

Y

Y

Y

1 July---30 Sept---

1 July25 July

1 July1 July31 Aug

31 Aug

31 Aug

10 Sept

2.5

0

1.0

0

28

19

35

29

42

37

45

70

50

30

15

4

20

-

-

-

54

-

-

-

Deficient Rainfall Volume
Cover
Rainfall Distribution / Dry
Spell Cover (CDD)
Deficient Rainfall Volume
Cover Start Day
Deficient Rainfall Volume
Cover End Day
Rainfall Distribution / Dry
Spell Cover Start Day
Rainfall Distribution / Dry
Spell Cover End Day
Dry Day Rainfall Threshold (inmm per day)Consecutive Dry Day Strike(Days)
Consecutive Dry Day Exit
(Days)
Percentage of Max Payout for
35 Consecutive Dry Days
Percentage of Max Payout for
10 mm Rainfall Volume in
Phase 1
Percentage of Max Payout for
10 mm Rainfall Volume in
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Percentage of Max Payout for
10 mm Rainfall Volume in
Phase 3
Excess Rainfall Index
Computation Period
Percentage of Max Payout for
100 mm Rainfall Volume on
any 1 Day after 15 Sept

10

-

-

-

Daily

Two-day

Daily

Two-day

66

0

100

65

Key Observations based on Table 2:
 AIC has offered both deficient rainfall volume and deficient rainfall distribution covers forguar in Jodhpur while it has offered only deficient rainfall distribution cover (CDD) for
guar in Jaisalmer. On the other hand, ICICI Lombard has offered only deficient rainfall
distribution cover (CDD) as part of the guar contracts for Jodhpur and Jaisalmer.
 For all the four guar contracts analyzed above, the sowing dates for all the insured plots
have been deemed to be the same.
 In case of the CDD covers provided by ICICI Lombard, a spell of 35 consecutive days ofzero rainfall entitles an insured farmer to 30% of maximum sum assured (under CDD
cover) for Jodhpur and 4% of maximum sum assured for Jaisalmer. For AIC, a dry spellof 35 consecutive days entitles the farmer to 50% of maximum sum assured for Jodhpur
and 15% of maximum sum assured for Jaisalmer.
 Under the deficient rainfall volume cover offered by AIC for guar in Chittorgarh, a farmersubscriber was entitled to a mere 20% and 10% of the maximum sum assured for Phase
1 and Phase 3 when the total rainfall in each of these phases would have been just 10
mm. This indicates the difficulty of insuring a reasonable amount of rainfall volume in thegiven premium limits set under WBCIS. Regions with different levels of systemic rainfall
have to be classified and provided different levels of premium subsidy support under
WBCIS to maintain reasonableness in design of rainfall insurance contracts.
 A review of the excess rainfall covers offered for guar in Jodhpur and Jaisalmer indicatesthat AIC has considered the daily rainfall as the index for excess rainfall whereas ICICI
Lombard has considered the cumulative rainfall during any two consecutive days as theexcess rainfall index. Assuming a 100 mm rainfall on any one day after 15 September tillend of excess rainfall cover period, the percentage of maximum payout payable to an
insured farmer from AIC is 66% and 100% of maximum sum assured (for excess rainfall
cover) under guar contracts for Jodhpur and Jaisalmer. Taking the same conditions, theexcess rainfall covers by ICICI Lombard entitle the farmer-subscribers to 0 % ofAgricultural Finance Corporation

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maximum sum assured under the guar contract for Jodhpur and 65% of maximum sum
assured under the guar contract in Jaisalmer.

Payout Frequencies for Rainfall Insurance Contracts
The payout frequency of an insurance contract is more easily understood in terms of thepayout cycle or the return period for payout. The payout cycle or return period indicates theaverage number of years in which an insurance contract has yielded a payout historically.
For a more comprehensive assessment, the payout cycles or return periods corresponding
to various levels of payout can be calculated and compared for different insurance contracts.
Such an exercise has been done below for the contracts analyzed in the previous section.

Table 3: Comparison of Payout Cycle for Groundnut and Maize Contracts for
Chittorgarh
Groundnut
Feature

Maize
AIC

ICICI
LOMBARD

44

26

44

16

10

17

25

13,00015,00015,00013,500

Payout Cycle (in Years) for
Payout > INR 0

1.6

4.4

1.5

1.8

Payout Cycle (in Years) for
Payout > 1% of Policy Limit

1.7

4.9

2.7

2.1

Payout Cycle (in Years) for
Payout > 2.5% of Policy Limit

2.4

14.7

4.3

4

Payout Cycle (in Years) for
Payout > 5% of Policy Limit

8.7

22

5.2

5.5

Payout Cycle (in Years) for
Payout > 10% of Policy Limit

13

Undefined

6.5

Undefined

Payout Cycle (in Years) for
Payout > 15% of Policy Limit

13

Undefined

8.7

Undefined

Payout Cycle (in Years) for
Payout > 20% of Policy Limit

Undefined

Undefined

26

Undefined

Payout Cycle (in Years) for
Payout > 25% of Policy Limit

Undefined

Undefined

Undefined

Undefined

AIC

ICICI LOMBARD

No. of Years Considered26

No. of Payout Years
Policy Limit (INR)

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Key Observations based on Table 3:

 Considering the groundnut contract for Chittorgarh, the payout cycle for a payout(irrespective of its quantum) is 1.6 years for AIC and 4.4 years for ICICI Lombard. This
indicates that a payout under the groundnut contract for Chittorgarh, on an average,
takes place every 1.6 years in case of AIC and every 4.4 years in case of ICICI Lombard.
Regarding the maize contract for the same location, the payout cycle for both insurers ismore comparable being 1.5 years for AIC and 1.8 years for ICICI Lombard.
 Moving on to the payout cycles for payout levels exceeding 1%, 2.5% and 5% of thecorresponding policy limits, the payout cycle for groundnut contract in Chittorgarh
increases to 1.7 years, 2.4 years and 8.7 years for AIC whereas it is significantly higher
at 4.9 years, 14.7 years and 22 years for ICICI Lombard. The payout cycles of thegroundnut contract from AIC are much shorter than those of the contract from ICICI
Lombard. A shorter payout cycles would manifest into higher frequency of payout.
 For the maize contract in Chittorgarh, the payout cycles for the same level of payouts are
2.7 years, 4.3 years and 5.2 years for AIC while they are 2.1 years, 4 years and 5.5
years for ICICI Lombard. The maize contract from ICICI Lombard has a lower payout
cycle compared to AIC at lower levels of payout (1% and 2.5% of policy limit) while it
becomes higher at the level of 5% of the policy limit.
 At the payout level of 10% of the policy limit, the payout cycles under both the contracts
from ICICI Lombard become infinite, indicating the absence of any payout exceeding
10% of policy limit, as per the historical payout distribution. For AIC, the payout cycles atthis level are 13 years for its groundnut contract and 6.5 years for its maize contract.
 At the payout level of 15% of the policy limit, the payout cycles for the groundnut and maize
contract of AIC for Chittorgarh are 13 years and 8.7 years respectively.
 At the payout level of 20% of the policy limit, all the contract except the maize contract of
AIC indicate an undefined return period. The maize contract of AIC shows a return
period of 26 years for a payout equivalent to 20% of the policy limit.
 No contract has a finite payout cycle for payout levels exceeding 25% of policy limit
highlighting the fact that there are no historical incidences of payout greater than 25% of
the policy limit for any of the four contracts.

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Table 4: Comparison of Payout Cycle for Guar Contracts in Jodhpur and Jaisalmer
Jodhpur
FeatureJaisalmer

ICICI
LOMBARD

AIC

AIC

ICICI
LOMBARD

No. of Years Considered

25

39

23

46

No. of Payout Years

18

15

11

18

Policy Limit (INR)

10,00015,00010,00015,000

Payout Cycle (in Years) for
Payout > INR 0

1.4

2.6

2.1

2.5

Payout Cycle (in Years) for
Payout > 1% of Policy Limit

2.1

2.6

2.3

2.5

Payout Cycle (in Years) for
Payout > 2.5% of Policy Limit

2.1

5.6

3.3

11.5

Payout Cycle (in Years) for
Payout > 5% of Policy Limit

2.5

13

3.8

15.3

Payout Cycle (in Years) for
Payout > 10% of Policy Limit

3.1

39

4.6

15.3

Payout Cycle (in Years) for
Payout > 15% of Policy Limit

6.25

Undefined

4.6

46

Payout Cycle (in Years) for
Payout > 20% of Policy Limit

12.5

Undefined

7.7

46

Payout Cycle (in Years) for
Payout > 25% of Policy Limit

12.5

Undefined

23

46

Payout Cycle (in Years) for
Payout > 30% of Policy Limit

Undefined

Undefined

23

46

Payout Cycle (in Years) for
Payout > 35% of Policy Limit

Undefined

Undefined

Undefined46

Key Observations based on Table 4:
 Examining the guar contract for Jodhpur, the payout cycle for a payout (irrespective of its
quantum) is 1.4 years for AIC and 2.6 years for ICICI Lombard. This indicates that apayout under the guar contract for Jodhpur, on an average, takes place every 1.4 years
in case of AIC and every 2.6 years in case of ICICI Lombard. Regarding the guar
contract for Jaisalmer, the payout cycle for both insurers is more comparable being 2.1
years for AIC and 2.5 years for ICICI Lombard.

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Evaluation of Pilot Weather Based Crop Insurance Scheme Moving on to the payout cycles for payout exceeding 1%, 2.5% and 5% of thecorresponding policy limits, the payout cycles for guar contract in Jodhpur increase to 2.1
years, 2.1 years and 2.5 years for AIC whereas these are at 2.6 years, 5.6 years and 13
years for ICICI Lombard. The payout cycles of the guar contract for Jodhpur from AIC
become shorter at higher payout levels compared to those of the contract from ICICI
Lombard.
 For the guar contract of Jaisalmer, the payout cycles for the same level of payouts are 2.3
years, 3.3 years and 3.8 years for AIC while they are 2.5 years, 11.5 years and 15.3
years for ICICI Lombard. The payout cycles of the guar contract for Jodhpur from AIC
become shorter at higher payout levels compared to those of the contract from ICICI
Lombard.
 At the payout level of 10% of the policy limit, the payout cycles of all the four contracts(from both AIC and ICICI Lombard) remain finite unlike in the case of the contracts for
Chittorgarh analyzed in the previous section. For the guar contracts of Jodhpur andJaisalmer, the payout cycles are 3.1 years and 4.6 years respectively in case of AIC
whereas they are significantly higher at 39 years and 15.3 years respectively. The
payout cycle for guar contract of ICICI Lombard for Jaisalmer becomes constant at 46
years till the payout level of At the payout level of 15% of the policy limit, the guar contract of ICICI Lombard for
Jodhpur starts showing an undefined payout cycle. At this payout level, the payout cycleof the guar contract of ICICI Lombard for Jaisalmer becomes 46 years which remains
constant till the contract reaches a payout level of slightly more than 37.5%. In case of
the contracts of AIC for Jodhpur and Jaisalmer, the payout cycles are 6.25 years and 4.6
years respectively at payout level of 15% of policy limit; 12.5 years and 7.7 years at
payout level of 20% of policy limit; 12.5 years and 23 years respectively at payout levelof 25% of the policy limit.
 At the payout level of 30% of the policy limit, the payout cycle for the Jodhpur contract of
AIC becomes undefined whereas it is 23 years for the Jaisalmer contract of AIC. Whenthe payout level becomes 35% of the policy level, the payout cycle for the Jaisalmer
contract of AIC also becomes undefined leaving the ICICI Lombard contract for
Jaisalmer with a finite payout cycle of 46 years.

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Evaluation of Pilot Weather Based Crop Insurance Scheme No contract has a finite payout cycle for payout levels exceeding 25% of policy limit
highlighting the fact that there are no historical incidences of payout greater than 25% of
the policy limit for any of the four contracts.

Economic Value and Pricing Indicators for Rainfall Insurance Contracts

Pricing of weather insurance is almost entirely done in India using the burning cost method.
This is the simplest method of weather contract pricing. It involves taking historical values of
the weather index, and applying the weather contract to these values for computing thehistorical payouts. The average of these historical payouts is deemed as the burning costwhich Assuming the data used to calculate the historical indexes are of good quality for therisk analysis, HBA can give a useful and intuitive first indication of the mean and range ofpossible payouts of a weather contract. The total premium charged by an insurer for its
weather insurance product is constituted of two main components namely expected loss andrisk margin. The burning cost derived from the simulation of payouts of a weather insurance
contract is taken as the expected loss. This component is expected to be paid out (on anaverage basis) for every season of coverage under a given weather insurance contract. The
other component – risk margin, is determined by the risk preferences of the (re)insurancecompany providing the risk protection: that is, by how they measure the cost of risk withrespect to return for the purposes of risk management, capital allocation and business
expenses.

The following tables compare the chosen contracts on the key pricing indicators which canenable a better understanding of the economic value imparted to the customer and thefinancial support provided to crop insurance (the Indian Government, in this case).

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Table 5: Comparison of Pricing Indicators for Groundnut & Maize
Contracts for Chittorgarh

Groundnut
Feature

Maize

ICICI
LOMBARD

AIC

AIC

ICICI
LOMBARD

Total Policy Limit (in INR)

13,00015,00015,00013,500Total Premium (in INR)

1,300

1,655

1,500

1,100

Burning Cost (in INR)

402

108

563

286

Risk Margin (in INR)

898

1,547

937

814

Risk Margin (as % of Total
Premium)

69

93

62

74

Highest Historical Payout
(INR)

2484

1435

3086

1316

Highest Historical Payout (as% of Policy Limit)

19

9.6

21

9.8

Key Observations based on Table 5:
 The expected loss components (burning costs) for groundnut and maize contracts of AIC
for Chittorgarh are 31% and 38% respectively. This means that these contracts can be
expected to pay out (on an average basis) 31% and 38% of their total premiums forevery season of coverage. On the other hand, the expected loss components for thesame contracts of ICICI Lombard are 7% and 26% respectively.
 The highest historical payouts for groundnut and maize contracts of AIC for Chittorgarh are
19% and 21% respectively of the respective policy limits. On the other hand, the highest
historical payouts for the same contracts of ICICI Lombard are 7% and 26% respectively.
 The policy limit or maximum sum assured of a weather insurance contract is a misnomer as
it is perceived as the maximum payout that can be expected for that contract. However,in actual practice, the highest historical payout denotes the greatest cumulative payout(sum of payouts of all constituent covers) among all the cumulative payouts simulated
historically from a weather insurance contract. The quantitative difference between

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maximum sum assured and the highest historical payout for a weather insurance
contract represents a financial gap between the maximum payout committed by that
contract and the actual payout that could be expected from that contract even in adverse
years. In order to improve the economic value imparted to farmer-subscribers, it should
be ensured that the highest historical payout for WBCIS products lies between 33% and
50% of the policy limit.
Table 6: Comparison of Pricing Indicators for Guar Contracts in Jodhpur andJaisalmer
AIC
Jaisalmer

ILGIC
Jaisalmer

15,00010,00015,000

1,000

1,655

1,000

1,655

Burning Cost (in INR)

641

214

585

311

Risk Margin (in INR)

359

1,441

415

1,344

Risk Margin (as % of Total
Premium)

36

87

42

81

Highest Historical Payout
(INR)

2712

2075

3500

5884

Highest Historical Payout (as% of Policy Limit)

27

14

35

39

AIC Jodhpur

ILGIC Jodhpur

Total Policy Limit (in INR)

10,000Total Premium (in INR)

Key Observations based on Table 6:
 The expected loss components (burning costs) for guar contracts of AIC for Jodhpur andJaisalmer are 64% and 58% respectively. This means that these contracts can be
expected to pay out (on an average basis) 64% and 58% of their total premiums forevery season of coverage. On the other hand, the expected loss components for thesame contracts of ICICI Lombard are 13% and 19% respectively.
 The highest historical payouts for guar contracts of AIC for Jodhpur and Jaisalmer are 19%
and 21% respectively of the respective policy limits. On the other hand, the highest
historical payouts for the same contracts of ICICI Lombard are 14% and 39%
respectively.

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II. Comparative Analysis of IMD and Private Weather Stations based on Payouts

A comparison of IMD and private weather stations based on their payout performance isfraught with anomaly as a weather station that is working properly should have no influenceon the payout for the location covered by it. The relative efficacy of weather stations needs
to be ideally gauged on the basis of technical performance yardsticks provided in thespecifications for the equipments used in the weather station. The role of human inputs inthe quality of measurement of weather data has become negligible after introduction of
automatic weather stations. However, for the sake of exploratory understanding, acomparison between the payout performance of IMD and private weather stations isprovided below. Homogeneity, in terms of the location of these weather stations under thesame district, has been ensured for comparability. The following tables are drawn from thedata furnished by AIC related to their WBCIS coverage in Rajasthan during Kharif 2009season. Table 7 compares the payout (Rs/Ha) between IMD and private weather stations of
two districts for the same crop. Table 8, on the other hand, compares the district-wise
portfolio for five other districts where both IMD and private weather stations were utilized
under WBCIS.

Table 7: Comparison of Payout Performance of IMD
and Private Weather Stations (Crop-wise)

AWS
Bharatpur
Bharatpur
Bharatpur
Sawai Madhopur
Sawai Madhopur
Sawai Madhopur
Sawai Madhopur

Agricultural Finance CorporationAWS Source
IMD
NCMSL
IMD
NCMSL
IMD
NCMSL
IMD
NCMSL
IMD
NCMSL
IMD
NCMSL
IMD
NCMSL

Crop
Payout (INR/Ha)
Bajra
504
Bajra
262
Guar
857
Guar
446
Jowar
504
Jowar
262
Bajra
63
Bajra
0
Groundnut
535
Groundnut
226
Chilly0
Chilly1475
Sesamum/Til
57
Sesamum/Til
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Evaluation of Pilot Weather Based Crop Insurance Scheme

Table 8: Comparison of Payout Performance of IMD and Private Weather Stations(District-wise Portfolio)

State

District

RWS

Ajmer
Rajasthan Ajmer(IMD)
Banswara Banswara
Bhilwara Bhilwara
Bundi
Bundi
Jodhpur Jodhpur
Rajasthan Ajmer
Nasirabad(NCMSL)
SarvarBanswara Bagidora
Gardhi
Bhilwara Mandalgarh
Shahpura
Bundi
Hindoli
Jodhpur Bhopalgarh
FalodiOsiya
Shergarh

Area Insured Gross Premium Claims Paid
(Ha)
(INR)
(INR)
256.44279495
6695
41.02
60957
12774
206.50311600112469616.66845278411720369.93369729342350700.19763791102847130.47126250
3543
9
17999
17046
142.5
234215138890501.76749590
56233
32.67
36779
2000
893.471248240895228
36.12
32293
35230
1412
1404400
4314612214.25221449338228584
6159841328580

Avg. Claims(INR / Ha)
26
311
545
668
925
147
27
1894
975
112
61
1002
975
3056
1579
2275

Key Observations based on Table 7 and Table 8: In table 7, the payouts of all the three contracts for Bharatpur are higher under the IMD
weather station than under the private (NCMSL) weather station. These outcomescannot be attributed to the type (service provider) of weather stations but are most likely
to be the result of difference in rainfall received by them owing to their distinct locations.
Furthermore, all the three contracts are likely to follow a similar pattern of payouts since
the crops covered under the three contracts for Bharatpur are Bajra, Jowar and Guar; allof which are sturdy, rain-fed crops having relatively similar rainfall requirements for cropsustenance. In case of the three contracts for Sawai Madhopur, the payout (per unit) ofcontract for one crop (groundnut) is significantly higher under the IMD weather station
while the payout (per unit) of contract for another crop (chilly) is substantially higher forthe private (NCMSL) weather station. The payout (per unit) of the contract for the third
crop (sesamum) does not significantly vary between IMD and private weather stations.

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Evaluation of Pilot Weather Based Crop Insurance Scheme In table 8, the comparative analysis of weather stations based on payout performance isdone for the entire portfolio under a weather station, irrespective of the type of rainfall
insurance contract. Taking into account the even higher probability of error in drawing
inferences, it can be observed that in three (Banswara, Bundi and Jodhpur etc.), out of
the five districts considered in table 8, the average payout per unit are significantly higher
under private weather stations (NCMSL) than under IMD weather stations. For one
district (Bhilwara), the average payout per unit is considerably higher under IMD weather
station than under private (NCMSL) weather station. For the remaining one district(Ajmer), the difference in average payout per unit for IMD and private weather stations ismarginal.
 On the basis of the above points, it is apparent that there is no conclusive or discernible
difference in the average payout per unit under IMD and private weather stations. It is
more important to ensure the technical robustness, proper maintenance/calibration,
tamper-free operation (recording, transmission and processing of weather data) which
will go a long way in achieving fair outcomes for all the stakeholders in WBCIS.

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III. Establishment of Technical Support Unit for Crop Insurance in the Ministry ofAgriculture and its Proposed Structure

Background and Rationale
In the recent years, the crop insurance domain in India has witnessed an increase in focus
from key stakeholders amid a quest for dynamism and constant improvement in design anddelivery of crop insurance products. Weather insurance - introduced in the developing world
through a pilot in India during 2003 instantly caught the fancy of policymakers anddevelopmental entities by virtue of its potential for shielding the Indian farmers from thespectre of weather risks. Starting on a high note, the hopes from weather insurance started
dwindling in India until the Indian Government lent a vital impetus to it through the launch of
WBCIS in 2007. Ever since, the penetration and outreach of weather insurance in India has
increased phenomenally.
The large quantum of financial support by the Government to weather insurance (mainly inthe form of premium subsidy) in particular and to crop insurance (both as premium subsidy
and stop-loss reinsurance to NAIS) in general, warrants that crop insurance delivers bestvalue to farmer-subscribers. With the increased scope for blending of different types of crop
insurance indices (yield/weather/remote-sensing) and the resultant limitless number ofdesigns possible for crop insurance products, the task of appraising a diverse portfolio ofcrop insurance products and their contextual suitability is a specialized task that
unfortunately has not been able to attract the level of attention and technical rigor which it
truly deserves. The challenges in comprehensively evaluating the current and upcoming
crop insurance products are compounded by the fact that these insurance products lie at a
crossover of multiple specialized fields of knowledge.

A need for a well-equipped Technical Support Unit (TSU) with a mandate for ensuring thebest value from crop insurance in India and making the Indian crop insurance programme ashining example for other crop insurance programmes cannot be overemphasized. India has
already gained a leadership stature in implementation of weather insurance and is poised to
become the innovation factory of the world with respect to new developments in crop
insurance. Besides the argument for best value realization from public funds, the rationale
for a specialized Technical Support Unit for crop insurance is reinforced by the following
observations:

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Evaluation of Pilot Weather Based Crop Insurance Scheme Based on the exercises carried out in Section I of this note, the need for benchmarking of
weather insurance products has gained reasonable credence as it would help ensurethat the different products offered under WBCIS carry comparable benefits for farmersubscribers. Benchmarking of crop insurance products would be a fundamental mandateof the proposed TSU which would ensure the roll-out of only those crop insurance
products which can ensure balance between expectations of the demand side anddeliverability of the supply side.
 Some of the key stakeholders in the crop insurance domain have indicated loopholes andweakness in the institutional design and process control of WBCIS. The relatively flexible
stipulations related to underwriting and process control under WBCIS, need to bereviewed rigorously and tightened which may be best carried out by a specialized
agency like the proposed TSU
 Lack of adequate and reliable weather and crop loss data is considered to be a major
constraint in developing an accurate understanding of the current and future crop
production loss variability. An integrated data system for management of agricultural risk
management initiatives in India is the need of the hour. The responsibility fordevelopment and implementation of such an integrated data system can be entrusted tothe proposed Technical Support Unit. Improvements in crop and weather system will
lend a cutting-edge to the Indian crop insurance programme in which long-term
investments like development of an integrated databank for agricultural risk
management, large investments in awareness/capacity building for crop insurance etc.
have taken a backseat.
 To complement the process for improving weather insurance products, medium-term
research projects may be commissioned by the Government under the purview of the
proposed Technical Support Unit. As part of these projects, taluka level weather indicesfor catastrophic insurance can be developed as an initial step towards more robust
systems to mitigate climate change impacts. Catastrophic risks being low probability andhigh severity events have in principle a lower actuarially fair premium compared to more
frequent and moderately severe crop loss events. The low premium catastrophic covers
would ensure an excellent risk mitigation alternative to farmers at a higher level of
granularity (e.g. at taluka level). Simultaneously, high quality weather data from IMD andother agencies may be analyzed through inter-disciplinary research exercises involving
research institutions, agricultural universities and industry think-tanks which can take up
region/crop specific calibration exercises for improvement of crop insurance throughblending of indices and actual field pilots.

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Evaluation of Pilot Weather Based Crop Insurance Scheme Service delivery issues in crop insurance have emerged as the key concerns of farmers
during the field research undertaken as part of this study. The proposed TechnicalSupport Unit can develop service and quality guidelines for crop insurance and ensure
their proper implementation through mechanisms like audits, monitoring, customer
feedback etc.

Design and Composition of Technical Support Unit for Crop Insurance

The proposed Technical Support Unit (TSU) should ideally be a separate, independent unit
operating under the overall leadership and guidance of the Ministry of Agriculture andCooperation (GOI).
The suggestions of insurers and premier research institutions can be invited for identifying
such subject matters experts from India who can objectively assess crop insurance products
and provide inputs for improving them. Since weather-based crop insurance and blended
crop insurance products are relatively new financial instruments even globally, the possibilityof involving international experts (like actuaries, crop-weather simulation experts etc) in sucha body may also be considered. The proposed TSU can have both full-time and invitedmembers and a small team of full-time professionals. A broad composition of the TSU issuggested in the following table.

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Table 9: Suggested Composition of TSU for Crop Insurance
Role

Background / Organization

Crop Insurance Experts

Insurance Companies / Crop
Insurance Consultants

No.
4

Membership Type
1 Full-time /3 Invited

Agricultural Research Institutions/ Govt. Organizations /Agribusiness Companies

2

Agricultural Research Institutions/ Govt. Organizations /Agribusiness Companies

2

Plantation Crop Experts

Agricultural Research Institutions/ Govt. Organizations /Agribusiness Companies

1

1 Invited

Actuarial Experts

Insurance Companies /Independent Practitioners

2

2 Invited

Agricultural Statistics
Professionals / Data
Modellers

Agricultural Research Institutions/ Govt. Organizations / FIs

3

2 Full-time /Agro-meteorology
Experts

IMD/ Agricultural ResearchInstitutions

Field Crops Experts

Horticultural Experts

1 Full-time /1 Invited
1 Full-time /1 Invited

1 Invited2

1 Full-time /1 Invited

Agricultural Economists

Agricultural Research Institutions/ Govt. Organizations / FIs

2

1 Full-time /1 Invited

Reinsurance Experts

Reinsurance Brokers / Insurance
Consultants

1

1 Invited

IT Professionals(Database Mgmt & S/W
Development)

IT Companies / IT Consultants

3

2 Full-time /Legal & Regulatory
Issues Expert

IRDA / Insurance Companies

1

1 Invited

Rural InsuranceMarketing Experts

Insurance Brokers / Corporate
Insurance Agents /1

1 Invited

Rural Development
Specialists

NGOs

2

2 Invited

System Development
Experts (System
Architects)

IT Companies / Risk Mgmt. or
Insurance Consultants

2

1 Full-time /1 Invited

GOI Representatives(Agriculture Ministry)
Total Members in Proposed TSU

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1 Invited2

2 Invited
10 Full-time & 20 Invited221Evaluation of Pilot Weather Based Crop Insurance Scheme

Operational Leadership of Technical Support Unit for Crop Insurance
The responsibility of the operational leadership of the proposed TSU should be assigned to
an individual with a minimum of 15 years experience in handling major areas related to crop
insurance. The operational head of TSU should be at least a postgraduate in agriculture andshould ideally have earned a doctorate in a relevant area. The ideal candidate would besomeone who is familiar with the process for development of weather insurance products
and other upcoming blended crop insurance. A judicious mix of implementation work,
research, academic publication and relevant international experience in crop insurance will
enhance the suitability of the candidate for the operational leadership of TSU and will enablea better utilization of the diverse profile of the TSU members.

FinancingIn the current scenario when the cumulative annual premiums for weather insurance can beprojected reasonably to exceed 1000 crores by 2010-11 marketing year, 1% of the total
premiums from the WBCIS portfolio of the participating insurers could be arranged ascontribution towards technical assistance for weather insurance provided by the proposed
TSU. Further, funding for specific research and pilot projects can be raised by the TSU itself
from relevant Government Agencies or suitable developmental funding agencies.

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Appendix 1: Sample Rainfall Insurance Contracts for Rajasthan during Kharif 2009

RAJASTHAN

State:

WEATHER BASED CROP INSURANCE SCHEME (KHARIF 2009)
TERM SHEET
Ref. Weather Station : As per Govt. Notification
District: CHITTORGARH

Crop: GROUNDNUT
1.

1 A.

Unit: Hectare

DEFICIT RAINFALLRAINFALL VOLUMEPERIOD
TRIGGER I (<)
TRIGGER II (<)
EXIT
RATE I (Rs./ mm)
RATE II (Rs./ mm)
Max. Payout (Rs.)

21-Jun
60
30
0
15
52
2000

TOTAL PAYOUT (Rs.)

1 B.

to
mm
mm

PHASE - I
15-JulPHASE - II
16-Jul to
15-Aug
120 mm
60
mm
0
15
27
2500

PHASE - III
16-Aug to
30-Sep
80
mm
40
mm
0
20
43
2500

7000

PERIOD
1-Jul
RAINFALL DISTRIBUTION
TRIGGER DAYS (>=) 20(Consecutive Dry Days)
PAYOUT (Rs.)
1000

to
25
2000

31-Aug30
3500

TOTAL PAYOUT (Rs.) 3500
Note: Rainfall of less than2.

2.5 mm in a day shall not be considered as a rainy day and multiple events shall be considered for the final payout.
PHASE - I
PHASE - II
PHASE - III
PERIOD
1-Sep
to
31-Oct 1-Oct to30-Nov
1-Oct to15-Nov
DAILY RAINFALL TRIGGER (>)
50
mm
0
mm
0
mm
EXCESS RAINFALL
EXIT (mm)
120
mm
0
mm
0
mm
(Multiple events)
00Payout (Rs. / mm)
36Max. Payout
2500
00
TOTAL PAYOUT (Rs.)

2500

TOTAL SUM INSURED (Rs.)
PREMIUM (Rs.)
PREMIUM %

13000
1300
10%

Agricultural Finance Corporation

223

Evaluation of Pilot Weather Based Crop Insurance Scheme

TERMSHEET FOR WEATHER INDEX INSURANCE
Crops

Groundnut

Reference Weather Station

IMD: Chittorgarh, NCMSL: Begun, Nimbahera, Bassi, Badi Sadri, Kapasan, Kanera,
Bhadesar

Index A

Maximum Number of Consecutive Dry Days (CDD) where a dry day is a day with rainfall
equal to 0 mm

Cover Phase, From
To
Strike 1 (CDD's)
Strike 2 (CDD's)
Strike 3 (CDD's)
Strike 4 (CDD's)
Exit (CDD's)
Payout 1(in Rs) for CDD > strike1and <= strike2
Payout 2 (in Rs) for CDD > strike2
and <= strike3
Payout 3 (in Rs) for CDD > strike3and <= strike4
Payout 4 (in Rs) for CDD > strike4and <= Exit
Maximum Payout (in Rs) for CDD> ExitIndex B

Cover Phase,

1-Jul-09
10-Sep-09
19
30
40
50
60

300
900
2250
4500
7500

Maximum rainfall on any two consecutive days recorded in mm during cover phase

From

1-Jul-09
15-Aug-09
250
400

16-Sep-09
9-Oct-0980
180

Standard Loss Rate between
strike and exit - Notional (Rs / mm)Policy Limit Call (Rs)

25.00
3750

37.50
3750

Premium (Rs)
Farmer's Share (Rs)

1,655
580

Combined policy limit (Rs)

15,000To
Strike Call (mm) >Exit Call (mm) =>Data Source

Independent Third party

Settlement Date

Thirty days after the data release by data provider and verified by Insurer.

- Premium inclusive of service tax

Agricultural Finance Corporation

224

Evaluation of Pilot Weather Based Crop Insurance Scheme

RAJASTHAN

State:

WEATHER BASED CROP INSURANCE SCHEME (KHARIF 2009)
TERM SHEET
Ref. Weather Station : As per Govt. Notification
Distrcit: CHITTORGARH

Crop: MAIZE
1.

1 A.

Unit: Hectare

DEFICIT RAINFALLRAINFALL VOLUMEPERIOD
TRIGGER I (<)
TRIGGER II (<)
EXIT
RATE I (Rs./ mm)
RATE II (Rs./ mm)
Max. Payout (Rs.)

21-Jun
60
30
0
8
25
1000

TOTAL PAYOUT (Rs.)

1 B.

to
mm
mm

PHASE - I
15-JulPHASE - II
16-Jul to
20-Aug
120
mm
60
mm
0
15
43
3500

PHASE - III
21-Aug to
30-Sep
50
mm
25
mm
0
25
115
3500

8000

PERIOD
1-Jul
RAINFALL DISTRIBUTION
TRIGGER DAYS (>=) 20(Consecutive Dry Days)
PAYOUT (Rs.)
1000

to
25
2500

31-Aug30
4000

TOTAL PAYOUT (Rs.) 4000
Note: Rainfall of less than2.

2.5 mm in a day shall not be considered as a rainy day and multiple events shall be considered for the final payout.
PHASE - I
PERIOD
1-Sep
to
15-OctDAILY RAINFALL TRIGGER (>)
50mm
EXCESS RAINFALL
EXIT (mm)120
mm(Multiple events)
Payout (Rs. / mm)
43Max. Payout
3000
TOTAL PAYOUT (Rs.)

3000

TOTAL SUM INSURED (Rs.)
PREMIUM (Rs.)
PREMIUM %

15000
1500
10%

Agricultural Finance Corporation

225

Evaluation of Pilot Weather Based Crop Insurance Scheme

TERMSHEET FOR WEATHER INDEX INSURANCE
Crops

Maize

Reference Weather Station

IMD: Chittorgarh, NCMSL: Begun, Nimbahera, Bassi, Badi Sadri, Kapasan,
Kanera, Bhadesar

Index

Aggregate rainfall during the cover phases in mm.

CoverPhase,FromTo

25-Jun-09
9-Jul-09
PUT

10-Jul-09
14-Aug-09

40
5
0
7.14
200.001,250

Strike 1 Put (mm) <Strike 2 Put (mm) <Exit Put (mm) =<Notional 1 Put (Rs / mm)
Notional 2 Put (Rs / mm)Policy Limit Put (Rs)

15-Aug-09
29-Oct-09

200
50
0
6.67
80.00
5,000

80
5
0
20.00
450.00
3,750

-

575
1275
3.57
2500.00

CALL
Strike Call (mm)Exit Call (mm)Notional Call (Rs / mm)Policy Limit Call (Rs)

400
600
5.00
1,000.00

Premium (Rs)
Farmer's Share ( Rs )

1,100
275

Combined policy limit (Rs)

10,000

Data Source

Independent Third party

Settlement Date

Thirty days after the data release by data provider and verified by Insurer.

- Premium inclusive of service tax

Agricultural Finance Corporation

226

Evaluation of Pilot Weather Based Crop Insurance SchemeState:RAJASTHAN
Crop: GUAR

1.

1 A.

WEATHER BASED CROP INSURANCE SCHEME (KHARIF 2009)
TERM SHEETDistrcit: JODHPUR
Ref. Weather Station : As per Govt. Notification

Unit: Hectare

DEFICIT RAINFALLRAINFALL VOLUMEPERIOD
TRIGGER I (<)
TRIGGER II (<)
EXIT
RATE I (Rs./ mm)
RATE II (Rs./ mm)
Max. Payout (Rs.)

1-Jul
25
10
0
10
60
750

to
mm
mm

PHASE - II
21-Jul to
20-Aug
40
mm
20
mm
0
8
92
2000

PHASE - I
20-JulPHASE - III
21-Aug to
30-Sep
25
mm
10
mm
0
15
203
2250

TOTAL PAYOUT (Rs.) 5000

1 B.

1-Jul
PERIOD
RAINFALL DISTRIBUTION
TRIGGER DAYS (>=) 28(Consecutive Dry Days)
500
PAYOUT (Rs.)

to
35
1250

31-Aug
42
2500

TOTAL PAYOUT (Rs.) 2500
Note: Rainfall of less than2.

2.5 mm in a day shall not be considered as a rainy day and multiple events shall be considered for the final payout.
PHASE - I
1-Sep
to
31-OctPERIOD
60mm
DAILY RAINFALL TRIGGER (>)
EXCESS RAINFALL
EXIT (mm)120
mm(Multiple events)
42
Payout (Rs. / mm)
2500
Max. Payout
TOTAL PAYOUT (Rs.)

2500

TOTAL SUM INSURED (Rs.)
PREMIUM (Rs.)
PREMIUM %

10000
1000
10%

Agricultural Finance Corporation

227

Evaluation of Pilot Weather Based Crop Insurance Scheme

TERMSHEET FOR WEATHER INDEX INSURANCE
Crops

Guar

Reference Weather Station

IMD: Jodhpur; NCMSL: Phalodi, Shergarh, Bhopalgarh

Index A

Maximum Number of Consecutive Dry Days (CDD) where a dry day is a day with rainfall
equal to 0 mm

Cover Phase, From
To
Strike 1 (CDD's)
Strike 2 (CDD's)
Strike 3 (CDD's)
Strike 4 (CDD's)
Exit (CDD's)
Payout 1(in Rs) for CDD > strike1and <= strike2
Payout 2 (in Rs) for CDD > strike2
and <= strike3
Payout 3 (in Rs) for CDD > strike3and <= strike4
Payout 4 (in Rs) for CDD > strike4and <= Exit
Maximum Payout (in Rs) for CDD> ExitIndex B

Cover Phase,

25-Jul-09
31-Aug-09
19
25
30
35
37

300
900
2250
4500
7500

Maximum rainfall on any two consecutive days recorded in mm during cover phase

From
To

Strike Call (mm) >Exit Call (mm) =>1-Jul-09
15-Aug-09
170
320Standard Loss Rate between
strike and exit - Notional (Rs / mm)Policy Limit Call (Rs)

50.00
7500

Premium (Rs)
Farmer's Share (Rs)

1,655
414

Combined policy limit (Rs)

15,000

Data Source

Independent Third party

Settlement Date

Thirty days after the data release by data provider and verified by Insurer.

- Premium inclusive of service tax

Agricultural Finance Corporation

228

Evaluation of Pilot Weather Based Crop Insurance SchemeState:RAJASTHAN
Crop: GUAR

1.

WEATHER BASED CROP INSURANCE SCHEME (KHARIF 2009)
TERM SHEET
Ref. Weather Station : As per Govt. Notification
Distrcit: JAISALMER
Unit: Hectare

DEFICIT RAINFALLPERIOD
1-Jul
RAINFALL DISTRIBUTION
TRIGGER DAYS (>=) 35(Consecutive Dry Days)
1000
PAYOUT (Rs.)

to
40
3500

31-Aug
457500

TOTAL PAYOUT (Rs.) 7500
Note: Rainfall of less than2.

1.0 mm in a day shall not be considered as a rainy day and multiple events shall be considered for the final payout.
PHASE - I
PHASE - II
PHASE - III
1-Sep
to
31-Oct ##### to15-Oct1-Oct toPERIOD
35
mm
0
mm
0
mm
DAILY RAINFALL TRIGGER (>)
EXCESS RAINFALL
100
mm
0
mm
0
mm
EXIT (mm)(Multiple events)
Payout (Rs. / mm)
38
0
0
0
0
2500
Max. Payout
TOTAL PAYOUT (Rs.)

2500

TOTAL SUM INSURED (Rs.)
PREMIUM (Rs.)
PREMIUM %

10000
1000
10%

Agricultural Finance Corporation229

Evaluation of Pilot Weather Based Crop Insurance Scheme

TERMSHEET FOR WEATHER INDEX INSURANCE
Crops

Guar

Reference Weather Station

IMD: Jaisalmer; NCMSL: Mohangarh, Pokhran, Fatehgarh, Ramgarh, Nachna, Sultana

Index A

Maximum Number of Consecutive Dry Days (CDD) where a dry day is a day with rainfall
equal to 0 mm

Cover Phase, From
To
Strike 1 (CDD's)
Strike 2 (CDD's)
Strike 3 (CDD's)
Strike 4 (CDD's)
Exit (CDD's)
Payout 1(in Rs) for CDD > strike1and <= strike2
Payout 2 (in Rs) for CDD > strike2
and <= strike3
Payout 3 (in Rs) for CDD > strike3and <= strike4
Payout 4 (in Rs) for CDD > strike4and <= Exit
Maximum Payout (in Rs) for CDD> ExitIndex B

Cover Phase,

1-Jul-09
10-Sep-09
29
46
52
63
70

300
900
2250
4500
7500

Maximum rainfall on any two consecutive days recorded in mm during cover phase

From

1-Jul-09
15-Aug-09
170
320

16-Sep-09
9-Oct-0935
135

Standard Loss Rate between
strike and exit - Notional (Rs / mm)Policy Limit Call (Rs)

25.00
3750

37.50
3750

Premium (Rs)
Farmer's Share (Rs)

1,655
414

Combined policy limit (Rs)

15,000To
Strike Call (mm) >Exit Call (mm) =>Data Source

Independent Third party

Settlement Date

Thirty days after the data release by data provider and verified by Insurer.

- Premium inclusive of service tax

Agricultural Finance Corporation

230

Evaluation of Pilot Weather Based Crop Insurance Scheme

Appendix 2: Historical Payouts for Contracts in Appendix 1
Chittorgarh Groundnut

AIC ICICI Lombard

Year

Total Payout (INR)
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008

Agricultural Finance Corporation

0
0
457
497
0
591
979
0
0
221
363
646
0
646
215
428
428
2484
172
0
86
143
0
0
2109
DNA
0

Year

Deficit Rainfall
1963 DNA
1964 DNA
1965
1966
1967
1968
1969
1970
1971
1972
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
Average

Excess Rainfall 1
DNA
DNA
300
0
0
300
0
0
0
300
0
0
0
0
0
0
0
0
0
0
0
0
0
300
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0

0
0
628
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
285
0
0
0
0
0
0
0
0
1435
0
0
0
0
0
0
0
0
825

Excess Rainfall 2
DNA
DNA
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
293
0
0
0
0
0
0
90
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0

231

Total
DNA
DNA
300
0
628
300
0
0
0
300
0
0
0
0
0
0
0
0
293
0
0
0
0
300
0
90
0
285
0
0
0
0
0
0
0
0
1435
0
0
0
0
0
0
0
0
825
108.07

Evaluation of Pilot Weather Based Crop Insurance Scheme

Chittorgarh Maize
AIC ICICI Lombard

Year

Total Payout (INR)
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008

Agricultural Finance Corporation

0
0
549
263
2879
229
1174
0
0
266
194
358
0
358
114
331
331
1752
208
3086
0
171
0
0
2387
DNA
0

Year

Peril 1

Peril 2

Peril 3

Total

1965
1966

215
193

25
0

0
0

240
193

1967
1968
1969
1970

0
0
0
0

0
0
0
0

0
0
0
0

0
0
0
0

1971
1972
1973
1974

0
0
0
0

0
627
0
0

0
0
802
0

0
627
802
0

1975
1976

0
137

0
0

74
0

74
137

1977
1978
1979
1980

0
0
0
0

0
0
0
0

0
0
1316
0

0
0
1316
0

1981
1982
1983

0
1250
0

0
0
7

0
0
0

0
1250
7

1984
1985
1986
1987

0
1250
0
127

159
0
0
575

0
0
0
0

159
1250
0
702

1988
1989
1990
1991

0
0
0
248

0
18
0
0

0
0
0
0

0
18
0
248

1992
1993

1250
0

0
46

0
186

1250
232

1994
1995
1996
1997
1998

0
231
1170
8
0

0
0
0
150
410

0
0
0
0
0

0
231
1170
158
410

1999
2000

140
249

0
0

910
0

1050
249

2001
2002
2003
2004

0
11
0
0

0
10
0
0

0
0
0
0

0
21
0
0

2005
2006
2007
2008
Average

0
0
0
0

0
519
273
0

0
0
0
0

0
519
273
0
286.02232

Evaluation of Pilot Weather Based Crop Insurance Scheme

Jodhpur Guar
AIC ICICI Lombard

Year

Total Payout (INR)
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006

Agricultural Finance Corporation

60
30
500
630
2520
1501
0
9
0
0
1057
1448
0
0
0
100
333
710
1845
1238
2712
30
1251
0
50

Year

Deficit Rainfall Excess RF 1 Excess RF 2 Total
1963
300
0
0
300
1964
0
0
0
0
1965
0
0
0
0
1966
300
0
0
300
1967
0
0
0
0
1968
0
0
0
0
1969
0
0
0
0
1970
0
0
636
636
1971
0
0
0
0
1972 DNA
DNA
DNA
DNA
1973
0
0
0
0
1974
300
0
0
300
1975
0
0
0
0
1976
0
0
0
0
1977 DNA
DNA
DNA
DNA
1978
0
0
0
0
1979
300
200
0
500
1980
300
0
0
300
1981
0
0
444
444
1982 DNA
DNA
DNA
DNA
1983
0
475
0
475
1984
300
0
0
300
1985
0
0
0
0
1986
300
0
0
300
1987
0
0
0
0
1988
0
0
0
0
1989
0
0
0
0
1990
0
2075
0
2075
1991
0
0
0
0
1992
0
0
0
0
1993
300
0
0
300
1994
0
0
0
0
1995
0
0
0
0
1996 DNA
DNA
DNA
DNA
1997 DNA
DNA
DNA
DNA
1998 DNA
DNA
DNA
DNA
1999
300
0
0
300
2000
0
0
0
0
2001
0
0
0
0
2002
900
0
0
900
2003
0
0
0
0
2004
0
0
0
0
2005
900
0
0
900
2006 DNA
DNA
DNA
DNA
2007
0
0
0
0
2008
0
0
0
0
Average
213.59233

Evaluation of Pilot Weather Based Crop Insurance Scheme

Jaisalmer Guar
AIC ICICI Lombard

Year

Total Payout (INR)
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004

Agricultural Finance Corporation

0
0
3500
0
0
2500
0
115
58
285
0
0
192
0
0
615
2500
0
192
0
1923
0
1577

Year
1963
1964
1965
1966
1967
1968
1969
1970
1971
1972
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
Average

Deficit Rainfall Excess RF 1 Excess RF 2 Total
300
0
0
300
300
0
0
300
0
0
0
0
300
0
0
300
300
0
0
300
300
0
0
300
0
0
0
0
300
0
0
300
0
0
0
0
300
0
0
300
300
0
0
300
300
0
0
300
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
300
0
0
300
0
0
0
0
0
0
0
0
0
0
0
0
300
0
0
300
0
0
0
0
2250
0
0
2250
300
0
0
300
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
300
92
0
393
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
4500
0
1384
5884
300
1270
0
1570
0
0
0
0
0
0
0
0
300
0
0
300
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
300
0
0
300
0
0
0
0
310.79234

Evaluation of Pilot Weather Based Crop Insurance Scheme

Annexure 6: References

Anderson, J.R. and P.B.R. Hazell (eds.) (1989) Variability in Grain Yields: Implications for
Agricultural Research and Policy in Developing Countries. Johns Hopkins University Press.
Babcock, B.A. (1999) “Provision of a Safety Net for U.S. Agriculture,” and Babcock , B.A.
and D. J. Hayes (1999) “Whole Farm Revenue Insurance for Crop and Livestock
Producers,” Briefing Paper 99-BP 22, Center for Agricultural and Rural Development, Iowa
State University, Ames, Iowa (cited in Chandrasekear, M. and K.Mani (2009) ‘Evaluating
the Adoptability of Crop Insurance Schemes in Tamil Nadu,’ Department of Agricultural
Economics, Centre for Agricultural and Rural Development Studies, Tamil Nadu
Agricultural University (TNAU), Coimbatore).
Binswanger, H.P. (1980) "Attitudes towards Risk: Experimental Measurement in Rural
India," American Journal of Agricultural Economics, 62, pp. 395‐407.
Chakravarti, S. (1920) Agricultural Insurance: A Practical Scheme Suited to Indian
Conditions, Government Press, Bangalore (cited in Mishra, P.K. (1995)).
Chandrasekear, M. and K.Mani (2009) ‘Evaluating the Adoptability of Crop Insurance
Schemes in Tamil Nadu,’ Department of Agricultural Economics, Centre for Agricultural and
Rural Development Studies, Tamil Nadu Agricultural University (TNAU), Coimbatore.
Cole, S.A., X. Gine, J. Tobacman, P. Topalova, R. Townsend, and J. Vickery (2009)"Barriers to Household Risk Management: Evidence from India," Harvard Business School
Working Paper, No. 09‐116.
Dandekar, V.M. (1976) “Crop Insurance in India,” Economic and Political Weekly, XI: 26, pp
A 61-80.
Dandekar, V.M. (1985) “Crop insurance in India: A review; 1976-77 to 1984-85,” Economic
and Political Weekly, 20:25&26, pp.A-46 to A-59.
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