Working Paper No. 352

Crop Insurance in India:

Key Issues and Way Forward

Ashok Gulati

Prerna Terway

Siraj Hussain

February 2018

INDIAN COUNCIL FOR RESEARCH ON INTERNATIONAL ECONOMIC RELATIONS

Table of Contents

List of Abbreviations ................................................................................................................ i

Acknowledgement .................................................................................................................. iii

Abstract .................................................................................................................................... iv

Executive Summary ................................................................................................................. v

1. Introduction ........................................................................................................................ 1

2. Evolution of Crop Insurance Schemes in India .............................................................. 4

2.1 Crop Insurance in India – The beginning ............................................................................ 4

2.2 Pradhan Mantri Fasal Bima Yojana (PMFBY) - Kharif 2016 onwards ............................. 4

3. Evaluation of the Performance of crop insurance schemes ........................................... 5

3.1 Rolling out PMFBY: Experience of Kharif 2016 and Rabi 2016-17 ................................... 7

3.2 Challenges in the Implementation of PMFBY ................................................................... 13

4. Learning from International Best Practices .................................................................. 20

4.1 Crop Insurance in USA ...................................................................................................... 20

4.1.1 Farm bill 2014 ......................................................................................................... 21

4.2 Crop Insurance in China ................................................................................................... 22

4.2.1 Government Support in Agriculture Insurance ....................................................... 24

4.2.2 Agriculture Reinsurance .......................................................................................... 24

4.2.3 AIR Worldwide ........................................................................................................ 24

4.3 Crop Insurance in Kenya- Kilimo Salama ......................................................................... 25

4.3.1 Progress of the scheme ............................................................................................ 26

4.4 Lessons for India ................................................................................................................ 26

5. Role of Technology in Crop Insurance .......................................................................... 27

5.1 Application of Satellites in Agriculture.............................................................................. 27

5.2 Application of Drones in Agriculture ................................................................................ 28

5.3 Low Earth Orbits (LEO) .................................................................................................... 29

5.3.1 Planet Labs .............................................................................................................. 30

5.4 Government of India’s Programmes of use of Satellite Data for Agriculture ................... 30

5.5 Remote sensing-based Information and Insurance for Crops in Emerging

Economies (RIICE) ............................................................................................................ 30

6. Conclusions and Policy Recommendations ................................................................... 31

References ............................................................................................................................... 39

Annexures ............................................................................................................................... 41

List of Tables

Table 1a: Farmers Covered (million) under NAIS, WBCIS, MNAIS and PMFBY

(Kharif 2013 to Kharif 2016) ............................................................................... 8

Table 1b: Farmers Covered (million) under NAIS, WBCIS, MNAIS and PMFBY

(Rabi 2012-13 to Rabi 2016-17) .......................................................................... 8

Table 2a: Area Insured (million ha.) under NAIS, WBCIS, MNAIS and PMFBY

(Kharif 2012 to Kharif 2016) ............................................................................... 9

Table 2b: Area Insured (million ha.) under NAIS, WBCIS, MNAIS and PMFBY (Rabi

2012-13 to Rabi 2016-17) .................................................................................. 10

Table 3a: Gross Premium (Rs crore) under NAIS, WBCIS, MNAIS and PMFBY

(Kharif 2012 to Kharif 2016) ............................................................................. 11

Table 3b: Gross Premium (Rs crore) under NAIS, WBCIS, MNAIS and PMFBY (Rabi

2012-13 to Rabi 2016-17) .................................................................................. 11

Table 4a: Sum Insured (Rs crore) under NAIS, WBCIS, MNAIS and PMFBY (Kharif

2013 to Kharif 2016) ......................................................................................... 12

Table 4b: Sum Insured (Rs crore) under NAIS, WBCIS, MNAIS and PMFBY (Rabi

2012-13 to Rabi 2016-17) .................................................................................. 12

Table 5a: Sum Insured (Rs) per hectare under NAIS, MNAIS and PMFBY (Kharif

2012 to Kharif 2016) ......................................................................................... 12

Table 5b: Sum Insured (Rs) per hectare under NAIS, MNAIS and PMFBY (Rabi

2012-13 to Rabi 2016-17) .................................................................................. 13

Table 6a: Gross Premium as a Percentage of Sum Insured (Kharif 2012 to Kharif

2016) .................................................................................................................. 13

Table 6b: Gross Premium as a percentage of Sum insured (Rabi 2012-13 to Rabi 2016-

17) ...................................................................................................................... 13

Table 7: Comparison between NAIS, MNAIS and PMFBYs ......................................... 18

Table 8: Area Insured and Premiums paid by the Government (USA) ........................... 21

Table 9: Premium Subsidy given by the Central and Provincial Government ................ 24

List of Figures

Figure 1: Average Annual Growth Rate (%) and Coefficient of Variation of GSDP

Agriculture (2005-06 to 2014-15) ............................................................................ 2

Figure 2: Total Premium Paid and Claims Received from Agriculture Insurance (2001-

2013) ...................................................................................................................... 23

Figure 3: Representation of Replanting Guarantee ............................................................... 26

Figure 4: Area under Crop Insurance in India, China and USA ........................................... 32

Figure 5: Gross Premium and Sum Insured (all schemes combined) under Crop Insurance ..... 34

List of Abbreviations

ADWDRS Agricultural Debt Waiver and Debt Relief Scheme

AIC Agriculture Insurance Company of India Limited

ARC Agriculture Risk Coverage

AWS Automatic Weather Stations

CCIS Comprehensive Crop Insurance Scheme

CCE Crop Cutting Experiment

CHAMAN Coordinated Horticulture Assessment and Management using geo

informatics

CPIS Coconut Palm Insurance Scheme

DACFW Department of Agriculture, Cooperation and Farmers Welfare, GoI

DLTC District Level Technical Committee

ESA European Space Agency

FAA Federal Aviation Administration

FCIC Federal Crop Insurance Corporation

FCIP Federal Crop Insurance Program

FCOS Food Crops and Oilseeds

FIIS Farm Income Insurance Scheme

F&V Fruits and Vegetables

GPS Global Positioning System

GoI Government of India

IA Implementing Agency

IASRI Indian Agricultural Statistical Research Institute

ISRO Indian Space Research Organisation

IRDA Insurance Regulatory and Development Authority

LEO Low Earth Orbits

MNAIS Modified National Agriculture Insurance Scheme

MSP Minimum Support Price

MPCI Multiple Peril Crop Insurance

NAIS National Agriculture Insurance Scheme

NCIP Nation Crop Insurance Programme

NADAMS National Agricultural Drought Assessment and Monitoring System

NSSO National Sample Survey Organisation

PCICC Peoples Crop Insurance Company of China

PLC Price Loss Coverage

PMFBY Pradhan Mantri Fasal Bima Yojana

RIICE Remote sensing-based Information and Insurance for Crops in

Emerging economies

RMA Risk Management Agency

RST Remote Sensing Technology

RUA Reference Unit Area

RWBCIS Restructured Weather Based Crop Insurance Scheme

RWS Reference Weather Station

SLCCCI State Level Co-ordination Committee on Crop Insurance

SFSA Syngenta Foundation for Sustainable Agriculture

SoF Scale of Finance

STAX Stacked Income Protection Plan

UAV Unmanned Aerial Vehicle

WBCIS Weather Based Crop Insurance Scheme

iii

Acknowledgement

The research leading to this paper was undertaken at ICRIER as a part of the project

“Supporting Indian Farms the Smart Way: Rationalising Subsidies and Investments for

Faster, Inclusive and Sustainable Growth". The project is supported by Syngenta

Foundation to which we are grateful. We would like to thank Dr. Marco Ferroni, Dr. Yuan

Zhou, and Baskar Reddy, of Syngenta Foundation for Sustainable Agriculture for their

detailed and very useful comments.

The authors would like to acknowledge the invaluable comments from officers of various

insurance companies, Dr. Ashish Kumar Bhutani, Joint Secretary (Credit and Cooperation),

Government of India, Dr. Shibendu S. Ray, Director of Mahalanobis National Crop Forecast

Centre, Scott Sindelar, former Minister Counselor, U.S. Department of Agriculture, U.S.

Embassy in India, Rajeev Chawla, Additional Chief Secretary, Karnataka and Vinod Kumar

Singh, Directorate of Economics and Statistics, Uttar Pradesh.

Our special thanks are due to Prof. Anwarul Hoda, Chair Professor of ICRIER’s Trade Policy

and WTO Research Programme and Mr Umesh Mongia, Associate Vice President at ICICI

Lombard General Insurance Company Limited for their helpful comments and suggestions to

improve the paper.

Needless to say, the authors are fully responsible for the analysis carried out and views

expressed in the paper.

Abstract

Farmers in India are exposed to large agriculture risks due to vagaries of nature. One of the

most effective mechanisms to mitigate agricultural risks is to have a robust insurance system.

Although crop insurance has been in the country since 1972, yet it has been beset with

several problems such as lack of transparency, high premium, delay in conducting crop

cutting experiments and non-payment/delayed payment of claims to farmers. Realizing the

limitations of existing system of crop insurance, a new crop insurance scheme was launched

on Baisakhi day, Pradhan Mantri Fasal Bima Yojana (PMFBY), from Kharif 2016. Although

the overall area insured has increased by a modest 6.5 percent (from 53.7 million ha in 2015-

16 to 57.2 million ha in 2016-17), the number of farmers insured has increased by 20.4

percent (from 47.5 million to 57.2 million), the sum insured has increased by 74 percent

(from Rs 115432.4 crore to 200618.9 crore), and premium paid has increased by 298 percent

(from Rs 5491.3 crore to Rs 21882 crore) over the same period. The scheme has faced

several challenges during its first year of implementation which pertain to extension of cut off

dates for registration resulting in high premium rates; delay in submission of yield data to

assess damages as the system relies on thousands of Crop Cutting Experiments (CCE); lack

of trust in the quality of such data as they are not being video recorded and delay in payment

of premium subsidy by the state governments to the insurance companies, etc. The litmus test

of any crop insurance program is quick assessment of crop damages and payment of claims

into farmers’ accounts directly, and from that point of view, the first year of implementation

of PMFBY has not been very successful.

This paper recommends use of high technology and JAM trinity by linking land records of

farmers with their Aadhaar numbers and bank accounts for assessment and faster settlement

of claims. A portal linking Core Banking Solution (CBS) and crop insurance is need of the

hour giving information on real time basis. India’s prowess in Information Technology

should come handy to achieve this.

_________

Keywords: Agricultural Risk, Crop Insurance, India, Premium Subsidy

JEL Classification: Q18, G22, G32

Authors’ email: [email protected]; agulat[email protected]; [email protected];

shussain@icrier.res.in

________

Disclaimer: Opinions and recommendations in the report are exclusively of the author(s) and not of any other

individual or institution including ICRIER. This report has been prepared in good faith on the basis of

information available at the date of publication. All interactions and transactions with industry sponsors and

their representatives have been transparent and conducted in an open, honest and independent manner as

enshrined in ICRIER Memorandum of Association. ICRIER does not accept any corporate funding that comes

with a mandated research area which is not in line with ICRIER’s research agenda. The corporate funding of

an ICRIER activity does not, in any way, imply ICRIER’s endorsement of the views of the sponsoring

organization or its products or policies. ICRIER does not conduct research that is focused on any specific

product or service provided by the corporate sponsor.

Executive Summary

Farmers are often exposed to natural vagaries, which adversely affect their agricultural

production and farm incomes. One of the most effective mechanisms to mitigate agricultural

risks emanating from natural calamities is adoption of a robust insurance system. Although

crop insurance has been in the country since 1972, yet it has been beset with several problems

such as lack of transparency and non-payment/delayed payment to farmers. Therefore, it

would be important to streamline its operation by developing an institutional mechanism that

can bring greater transparency and effective implementation, particularly in terms of quick

and accurate compensation to farmers for the damages incurred.

Until recently (till March 2016), there were three crop insurance schemes operating in India –

National Agriculture Insurance Scheme (NAIS), Modified National Agriculture Insurance

Scheme (MNAIS) and Weather Based Crop Insurance Scheme (WBCIS). The penetration of

agricultural insurance was low and stagnant in terms of area insured and farmers covered. In

the three year period from 2013-14 to 2015-16, the average area insured under all the

schemes was 47 million hectare covering 39 million farmers. The high premium rates of 8-10

per cent under MNAIS and WBCIS, delay in settlement of claims, which took around 6 to 12

months, inadequate sum insured and their capping under MNAIS and inadequate government

support in the form of premium subsidies had left a vast majority of farmers without any

significant insurance coverage.

Realizing the limitations of existing system of crop insurance, the GoI launched a new crop

insurance scheme, Pradhan Mantri Fasal Bima Yojana (PMFBY) from Kharif 2016. Some of

the improved features of this scheme are: removal of capping on premium rates leading to

higher amount of sum insured, fixing premium rates at 2 percent in Kharif season and 1.5

percent in Rabi season for farmers, leading to substantial increase in premium subsidy by the

government. The use of mobile based technology, smart Crop Cutting Experiments (CCEs),

digitisation of land record and linking them to farmers' account for faster

assessment/settlement of claims are other steps required for effective implementation of the

new crop insurance scheme.

The analysis done for the new scheme reveals that overall area insured has increased

marginally by 6.5 percent (from 53.7 million ha in 2015-16 to 57.2 million ha in 2016-17).

However, over the same period, the number of farmers insured has increased by 20.4 percent

(from 47.5 million to 57.2 million), the sum insured has increased by 74 percent (from Rs

115432.4 crore to 200618.9 crore), and premiums paid have increased by 298 percent (from

Rs 5491.3 crore to Rs 21882 crore). The government must be complimented for taking bold

decision to increase premium subsidy and scaling up crop insurance.

However, the scheme with a noble intention to protect farmers can succeed only if

operational guidelines are strictly followed and cut off dates are not extended frequently as

was done in Kharif 2016. One of the reasons for high actuarial premium rates quoted by the

reinsurance companies was the extension of cut off dates. Moreover, timely submission of

yield data of CCEs and payment of premium subsidy to insurance companies will smoothen

and fasten the process of claim settlements as done by Tamil Nadu in Rabi 2016-17, Tamil

Nadu when they experienced one of the worst droughts. Unfortunately, even after almost two

years of the implementation of the scheme, mobile devices have not been procured to capture

data of assessment of crop yield for assessment of crop damage. There were allegations of

data manipulations while conducting CCEs like the yield of groundnut of Rajkot district in

Gujarat in Kharif 2016. Karnataka has gone ahead and made compulsory use of mobile

phones while conducting the CCE. They have made Samarakshane portal which provides

information related to CCE claim statements, farmer-wise, including farmer’s Aadhaar

number and account number.

A large scheme like crop insurance takes away almost one-third of financial resources of the

Department of Agriculture Cooperation and Farmers Welfare and it is administered by only

two director level officers in the Ministry. Such a large and important scheme deserves a

dedicated team of professionals which can collate and analyse the data collected from the

states and insurance companies.

This paper draws lessons from some of the best international practices followed by countries

such as China, Kenya and the USA. The heavy premium subsidy programme started by the

Government of China in 2007 led to an expansion of insured farm area from 15 million

hectares in 2007 to 115 million hectares in 2016. In India, total area covered under insurance

in 2016-17 amounts to about 30 percent coverage of gross cropped area, less than half of

what USA (89 percent coverage) and China has achieved (69 percentage coverage). The

premium subsidy payable by the government is 80 per cent and 70 percent in China and

USA, respectively. The Kenyan experience is significant due to its efficiency in settlement of

claims within 2-4 days. Kilimo Salama (Safe Agriculture) is a weather index based insurance

product developed by Syngenta Foundation for Sustainable Agriculture (SFSA) in 2009.

They have developed an application that uses Safaricom mobile technology, M-pesa, to

transfer money for payment of claims. Whenever there is a deviation from normal rainfall

resulting in germination failure, the claim amount automatically gets transferred into the

accounts of insured farmers.

This paper recommends widespread use of remote sensing technology in agriculture

insurance programme with minimum human intervention in order to assess crop damages and

expeditious settlement of claims. The application of drones, LEOS, and remote sensing

satellites at fine resolution can prove to be effective in taking images, which could be used to

assess crop damages in an area. Drones could be used to take images of crops affected by

hail, wind, rainfall, etc. Because they fly at lower heights, problems such as cloud obstruction

can be minimised. As soon as there is information on damage in a particular area, they could

be deployed to assess damages in the area so that accurate scenario can be captured

expeditiously. Recently, the world’s largest corn processor Archer-Daniels-Midland Co. in

USA received approval from the Federal Aviation Administration (FAA) to use drones to

gather data on crop insurance claims. China has launched Low Earth Orbits (LEO) to capture

images of vegetation in order to monitor crop growth around the world. Planet Labs, an

vii

organisation based in San Francisco has developed satellites called “doves”. They fly on low

orbit and collect data from any place on earth.

Based on the experience of other countries and rapid advancement in technology, this paper

recommends adoption of modern technology to assess crop damage:

satellite/LEOs/doves/drone/ images, Automatic weather stations and use of mobile-based

technology for crop cutting experiments (CCE). We also suggest conducting high quality

CCEs, switching from random selection of CCE to a science based selection approach on the

basis of satellite technology and gradual transition from CCEs to using technological

solutions for assessment of crop damage. A dedicated constellation of 5 satellites of high

resolution with five day frequency is recommended to increase the precision of crop loss

assessment at village level, which is expected to have an additional cost of Rs 1000 crore to

the exchequer. There is a need to increase the density of Automatic Weather Stations (AWS)

and rainfall data loggers. The entire country could be covered by installing additional 33000

AWS and 170,000 rainfall data loggers; this would cost the government between Rs.300-

Rs.1400 crore, depending on the parameters required for AWS. In order to ensure timely

settlement of claims, the government could make use of the JAM trinity by linking land

records of farmers with their Aadhaar numbers and bank accounts. The use of mobile

technology could be used for smart CCEs and direct submission of crop cutting data to

servers. This can substantially reduce the time taken in compiling reports of crop cutting

experiments from districts. It can also make the process of claim settlement much faster.

We recommend scaling up area insured to 100 million hectares as envisaged in the

operational guidelines of the PMFBY. With more experience of PMFBY and enhanced

competition among state governments to cover larger number of farmers, and as scale of

insurance coverage increases, we hope rates of actuarial premiums will also come down. As

they settle at lower levels (say below 8 percent) than the current ones (12.5 percent), the

Government can think of raising the sum insured from just covering cost of cultivation to

expected levels of income based on last three to five years yields and MSP data.

An increase in awareness among farmers through government agencies, insurance companies

and banks is required. Farmers should be informed through an aggressive media campaign

about compulsory deduction of premium, amount of sum insured, name of insurance

company and the procedure for settlement of claims. IRCTC has already shown the way for

railway tickets booked online by informing the passengers about insurance policy through an

SMS and email. . There is also need to create excitement in this scheme as was done in the

case of the PM’s Suraksha BimaYojana and PM’s Jan DhanYojana.

Crop Insurance in India: Key Issues and Way Forward

Ashok Gulati

, Prerna Terway

, Siraj Hussain

1. Introduction

Indian agriculture has little more than half (53 percent) of its area still rain fed. This makes it

highly sensitive to weather conditions, causing uncertainty in agricultural output. Extreme

weather conditions such as floods, droughts, heat waves, cyclones and hailstorms cause

extensive crop damage. Subtle fluctuations in weather during critical phases of crop

development can have a substantial impact on yields. Climate change increases agricultural

risk by increasing variability in rainfall, causing water stress, enhancing susceptibility to plant

diseases and pest attack and, more importantly, raising the frequency, intensity and duration

of extreme weather events like droughts, floods, cyclones and storm surges. According to the

fifth report of the Inter-governmental Panel on Climate Change (IPCC), the average

combined land and ocean surface temperature data has shown an increase of 0.85°C over the

period 1880 to 2012. Climate change will be particularly hard on agricultural production in

Africa and Asia. For wheat, rice and maize in tropical and temperate regions, climate change

without adaptation is projected to negatively impact production. Therefore, it is necessary for

countries to develop strategies for adaptation to climate changes.

The growth of agriculture in India has varied across states (Figure 1). Variations in the

performance of agricultural growth across states and year to year fluctuations are major

causes for concern for long term food security and also for welfare of farmers. The

coefficient of variation indicates the volatility in agricultural growth rates across various

states. A high coefficient of variation, indicating high volatility is observed in states like

Kerala, Bihar, Rajasthan, Karnataka and Maharashtra.

Ashok Gulati is Infosys Chair Professor for Agriculture at ICRIER and former Chairman of the

Commission for Agricultural Costs and Prices (GoI); contact email: agulati1[email protected];

[email protected]

Prerna Terway is a Research Associate at ICRIER; contact email: prernater[email protected]

Siraj Hussain is Visiting Senior Fellow at ICRIER and former Secretary, Ministry of Agriculture and

Farmers’ Welfare, GoI; email contact: [email protected]

Figure 1: Average Annual Growth Rate (%) and Coefficient of Variation of GSDP

Agriculture (2005-06 to 2014-15)

Source: National Accounts Statistics, CSO

Farmers primarily face two types of risks – yield risk and price risk. An unplanned and major

variation in either the yield or price of a crop in a particular agricultural cycle can translate

into significant losses to the farmer.

Yield risk refers to uncertainty regarding the quantity and quality of agricultural product

harvested at the end of an agricultural cycle. Erratic rainfall distribution has an adverse

impact on agricultural production. On an average, crops on 12 million hectares of land are

damaged annually by natural calamities and adverse seasonal conditions in the country

(Planning Commission, Eleventh Five-year Plan, 2007-2012). In the last fifteen years, there

have been several years when deficiency in rainfall has adversely affected agricultural

production. In 2002, rainfall deficit was 19 per cent due to which there was a loss of 38

million tonnes of food grains. The 2009 drought was the third worst since 1901, when a

rainfall deficit of 18 per cent was recorded and there was a production loss of about 16

million tonnes of food grains.

Price risk refers to the uncertainty about prices that farmers receive for their produce. During

years of high production, prices of crops slide downwards, affecting the incomes of farmers.

There have been times when higher production of crops has led to prices falling to very low

levels, even below MSP levels as happened after the Kharif of 2016 and 2017 in case of

several pulses and oilseeds. Furthermore, farmers have not been adequately protected by

MSPs in all states. Although MSPs are announced by the government for 23 commodities,

they are mainly implemented for rice and wheat and that too in a few states of the country.

The price risk is becoming more pronounced as Indian agriculture opens to global trade. In

2017-18, prices of several agricultural commodities like tur, urad, soybean, groundnut etc

remained much lower than MSP causing widespread distress to farmers in several states.

-12

-7

-2

Madhya Pradesh

Jharkhand

Gujarat

Chattisgarh

Rajasthan

Karnataka

Andhra Pradesh

Assam

Bihar

Odhisha

Haryana

Uttar Pradesh

West Bengal

Uttarakhand

Tamil Nadu

J&K

Maharashtra

Punjab

Himachal Pradesh

Kerala

Growth Rate Coefficent of Variation

Income of farmers depends on both prices and yield, which are inversely related to each

other. When aggregate production of a commodity increases, market prices tend to decrease,

and when yields fall, prices generally rise. This offsetting nature of price and production

effects has somewhat cushioning impact on farmers’ incomes.

Traditionally, successive governments have dealt with agricultural distress by relying on the

practice of announcing relief packages from time to time. In 2006, a rehabilitation package of

Rs 16,978.69 crore for farmers in 31 suicide-prone districts in Maharashtra, Kerala,

Karnataka and Andhra Pradesh was approved. The Agricultural Debt Waiver and Debt Relief

Scheme (ADWDRS) was announced in May 2008, which cost the government Rs 52,516.86

crore. Recently, six states-Tamil Nadu, Maharashtra, Uttar Pradesh, Rajasthan, Karnataka and

Punjab have announced farm-debt waivers and this is expected to spread to other states as

well. Besides these irregular relief packages, the government also provides assistance to

states from the State and National Disaster Response Fund. The cumulative amount released

by the Centre for all calamities including drought and flood from National Disaster Response

Fund between 2011-12 and 2015-16 amount to Rs. 24,055 crore

. These ad hoc relief

measures provided by the government, in the wake of natural calamities, are characterised by

severe limitations – lack of transparency in terms of any robust scientific basis for estimating

compensation, non-payment in many cases, inadequate amount of compensation under SDRF

and NDRF and delayed payment to farmers. Therefore, there is urgent need to develop a

robust insurance system to insulate farmers from risks faced by them.

Although agricultural insurance has been present in the country since 1972, it suffers from

operational weaknesses and it has not been able to adequately protect farmers against yield

and price volatility.

This paper evaluates the agriculture insurance schemes that existed in the country before the

PMFBY was introduced in Kharif 2016, how a transition was made to PMFBY and

highlights the major challenges in implementation of PMFBY. Based on this evaluation, and

also a review of how USA, China and Kenya are implementing crop insurance schemes, we

make some recommendations that may help develop a robust crop insurance system in the

country that is transparent, just in terms of sums insured, and quick in settling farmers’ claims

by using high end technology. A particular focus of this paper is on emphasising the role of

technology and experience from some of the best international practices in crop insurance.

Section 2 of the paper deals with various agricultural insurance schemes implemented in the

country since 1985.

Section 3 evaluates the performance of these insurance schemes with a particular focus on the

new crop insurance scheme – PMFBY for Kharif 2016 and Rabi 2016-17.

Section 4 highlights some of the best international practices followed by countries such as

USA, China and Kenya.

Data source is Lok Sabha Starred Question No. 31 and Question No. 206

Section 5 highlights the use of technology in assessment and settlement if crop damage.

Section 6 concludes based on analysis carried out in previous sections and makes some

recommendations with a view to improvise the functioning of PMFBY for the benefit of

millions of farmers, especially small and marginal that dominates the landscape of Indian

peasantry.

2. Evolution of Crop Insurance Schemes in India

2.1 Crop Insurance in India – The beginning

The first nation-wide crop insurance scheme was the Comprehensive Crop Insurance Scheme

(CCIS) introduced in Kharif, 1985-. This scheme was based on an area approach and area

units were identified for the purpose of assessing indemnity. This was replaced by National

Agriculture Insurance Scheme (NAIS) in Rabi 1999-2000, which was further changed to the

Modified National Agricultural Insurance Scheme (MNAIS) during Rabi 2010-11 (Annexure

1). Apart from these schemes, several other pilot projects such as Seed Crop Insurance (1999-

00), Farm Income Insurance Scheme (Rabi 2003-04) and Weather Based Crop Insurance

Scheme (Kharif 2007) were implemented from time to time. In April 2016, Pradhan Mantri

Fasal Bima Yojana (PMFBY) - an area based scheme and Restructured Weather Based Crop

Insurance Scheme (RWBCIS) was introduced.

2.2 Pradhan Mantri Fasal Bima Yojana (PMFBY) - Kharif 2016 onwards

Realizing the limitations of existing system of crop insurance that was not able to meet the

needs of farmers, the NDA government announced a new crop insurance program. PMFBY

scheme became operational from Kharif, 2016 with an objective to provide adequate

insurance coverage and financial support to the farmers in the event of crop failure.

Features of the new scheme

(i) Sum Insured- The sum insured is equal to the Scale of Finance (SoF) for that crop as

fixed by District Level Technical Committee. Sum Insured for individual farmer is now

equal to the Scale of Finance per hectare multiplied by area of the notified crop

proposed by the farmer for insurance. The scale of finance takes into account the cost of

cultivation on the basis of land quality, irrigation expenses and facility as well as cost of

fertilizers, seeds and labour which varies from one district to another.

(ii) Premium Rates: The premium rates payable by farmers for Food Crops and Oilseeds

(FCOS) is fixed at 2 percent of the Sum Insured or Actuarial rate, whichever is less, for

Kharif season and 1.5 percent for Rabi season. For commercial/horticulture crops,

premium rate of 5 percent is fixed to be paid by the farmer. The difference between

premium rate and rate of insurance payable by farmers will be shared by the Central

government and the State government equally as premium subsidy.

(iii) Estimation of Crop Yield: The minimum number of Crop Cutting Experiments (CCEs)

required at village level is 4 for major crops and 8 for other crops. Inputs from

RST/satellite imagery would also be utilized in optimizing the sample size of CCEs.

(iv) Use of modern technology: The CCEs have been lacking in reliability and speed in

estimation of crop yield. The use of mobile based technology with GPS stamping was

recommended to improve the quality of data and make faster assessment of claims. The

expense in procuring handheld devices/smart phones are to be borne equally by the

Centre and the State, with a cap on total funds to be made available by the Central

government. The use of technology available in the fields of remote sensing, aerial

imagery, satellites etc. would reduce manpower and infrastructure. It is estimated that

using a mix of modern technology can be expected to minimize the number of CCEs by

about 30 percent.

(v) Role of Private players: The public sector company, Agriculture Insurance Company

(AIC) of India along with other public and private insurance companies are

participating in the new crop insurance scheme. The selection of Implementing Agency

(IA) is made by state governments by adopting a cluster approach consisting of 15-20

‘good’ and ‘bad districts’, based on risk profile, with reference to the bid to be laid out.

Selection of IA is to be made through competitive bidding upto 3 years.

(vi) Time frame for loss assessment: The cut-off date for the receipt of yield data is within

one month of final harvest. Processing, approval and payment of final claims is based

on the yield data and it is to be completed within three weeks from receipt of yield data.

(vii) Timely release of premium subsidy to Insurance Companies: The government (both

Central and State) must release 50 percent share of premium subsidy to insurance

companies, in the beginning of every crop season, based on fair estimates submitted by

them, and settle balance of actual premium subsidy for season as soon as final figures

are submitted by insurance company.

(viii) Publicity and awareness: Adequate publicity is to be given in all villages of the notified

districts through fairs, exhibitions, SMS, short films, electronic and print media and

documentaries. The crop insurance portal should be regularly uploaded with all

published material information.

3. Evaluation of the Performance of crop insurance schemes

CCIS covered cereals, pulses and oilseeds. The premium rates were administered uniformly

throughout the country. It was kept at 2 percent for rice, wheat and millet crops and 1 percent

for pulses and oilseeds. It was subsidized by 50 percent for small and marginal farmers.

However, high claim to premium ratio, which was 6.72 for an average of 15 Kharif seasons

(1985-99), and 5.75 for an average of 14 Rabi seasons (1985-86 to 1998-99), made the

scheme financially unviable. The sum insured was to be limited to Rs 10,000 per farmer,

irrespective of the size of loan and farm size (Report of the Committee to Review the

Implementation of Crop Insurance Schemes in India, 2014).

This scheme was replaced by NAIS in 1999-2000 which was further modified and renamed

as Modified NAIS during Rabi 2010-11. WBCIS was introduced in 2007.

Some of the limitations of these schemes are as follows:

Low penetration of agricultural insurance

The penetration of agricultural insurance in India was low and stagnant in terms of the area

insured and the number of farmers covered till 2014-15. In the three years period (2013-14 to

2015-16), the average area insured under all the schemes combined was 16.3 million hectares

in the Rabi and 29.7 million hectare in the Kharif. The number of farmers insured was 13

million in the Rabi and 25 million in the Kharif for all the schemes. The primary reason for

low coverage was unaffordable high premium rates and capping of premium and sum assured

under MNAIS. The average premium rate was around 10 per cent for MNAIS and WBCIS.

Premium and sum insured related issues

The sum insured was worked by multiplying the Notional Threshold Yield with MSP/average

farm gate price. However, in MNAIS and WBCIS, premium rates were calculated on

actuarial basis, (which was a departure from the administratively decided premium rate that

prevailed during NAIS) and they were capped in order to reduce total expenditure on

premium subsidy by both Central and state governments. Sum insured per hectare was

reduced to an amount to commensurate with capped premium rates and this led to low sum

insured for most of the crops. As actuarial premium rates under MNAIS were high for most

of the insured crops in many districts, sum insured in certain cases was insufficient to even

cover the cost of cultivation.

Delay in assessment and settlement of claims

The assessment of damage was based on the traditional system of crop cutting experiments

that took 6-12 months. The settlement of claims took unduly long time; at times it extended

beyond the next cropping season.

Area discrepancy

The issue of area discrepancy has been prevalent since early years of crop insurance as in

many cases, area insured was greater as compared to the net sown area as reported by the

government agencies. According to PK Mishra Committee report (2013) this problem was

acute particularly in some districts of Gujarat growing groundnut as major crop. In Kharif

1993, the claim for groundnut alone was Rs 192.96 crore out of a total claim Rs 207.42 crore

for all crops. The problem of area discrepancy continued even after the introduction of NAIS

in Gujarat in Kharif 2000. To solve this problem of fudging of data by state machinery, area

correction factor

was applied by AIC but the states showed unwillingness to apply such

correction factors.

3.1 Rolling out PMFBY: Experience of Kharif 2016 and Rabi 2016-17

With the new and improved features of PMFBY, overall area insured has increased

marginally by 6.5 percent (from 53.7 million ha in 2015-16 to 57.2 million ha in 2016-17).

However, over the same period, the number of farmers insured has increased by 20.4 percent

(from 47.5 million to 57.2 million), the sum insured has increased by 74 percent (from Rs

1,15,432.4 crores to 2,00,618.9 crores), and premium paid has increased by 298 percent (from

Rs 5,491.3 crores to Rs 21,882 crores). India has definitely taken a leap forward and it

appears that a structural breakthrough has been achieved for which GoI deserves

appreciation. But the use of mobile based technology, smart Crop Cutting Experiments

(CCEs), digitisation of land record and linking them to farmers' account for faster

assessment/settlement of claims are some of the steps that are yet to be fully accomplished

for effective implementation of the new crop insurance scheme.

Farmers Insured

The total number of farmers insured has increased by 20.4 percent (from 47.5 million to 57.2

million between 2015-17 and 2016-17. The new crop insurance scheme has provided

coverage to 38.9 million farmers in Kharif 2016 as compared to 25.4 million farmers in

Kharif 2015, an increase of 53.1 percent (Table 1a). In Rabi 2016-17 the number of insured

farmers insured under PMFBY is 16.2 million, an increase of 17.4 percent from Rabi 2015-

16 (Table 1b). The increase in number of farmers insured is significant in Gujarat, Himachal

Pradesh, Karnataka, Uttar Pradesh and West Bengal in Kharif 2016 (Annexure 2). In Rabi

2016-17, total farmers insured (including WBCIS) has increased marginally by 0.6 percent.

The number of insured farmers has declined in a few states like Bihar, Maharashtra and

Rajasthan in Rabi 2016-17 (Annexure 3).

In a communication issued by Public Information Bureau, GoI (dated 7.12.2016)

, the

government has claimed that there has been an increase of more than 6 times in the coverage

of non-loanee farmers from 1.49 million in Kharif 2015 to 10.26 million in Kharif 2016,

which shows that the scheme has been well received by the non-loanee segment. However,

figures received from industry show that the number of non-loanee farmers has increased

from 9.87 million in Kharif 2015 to 10.18 million in Kharif 2016, an increase of merely 2.4

percent. According to our discussion with experts, increase in the number of non-loanee

farmers is mainly in Maharashtra where farmers reported as non-loanee have increased from

nil in Kharif 2015 to 7.2 million in Kharif 2016. This is due to a judgement of Bombay High

Court which ruled that loanee farmers cannot be forced to take insurance. Therefore all the

farmers taking insurance are considered non loanee farmers.

The area-correction factor is arrived at by dividing the area sown by the area insured for a given unit area,

and applied on the claim amount in order to scale it down. As a result, the claims of all the farmers in a unit

area are scaled down uniformly.

http://pib.nic.in/newsite/PrintRelease

According to data from the industry, the PMFBY, like previous schemes, is primarily

covering only loanee farmers as they account for 74 percent of total farmers insured in Kharif

2016 and 79 percent in Rabi 2016-17. However, there is a significant jump in non loanee

farmers in Jharkhand, AP, Karnataka and Tamil Nadu. In Gumla Simdea region of

Jharkhand, the State Co-operative Bank has reported that 62,567 non-loanee farmers took

crop insurance for paddy and 11,789 for maize in Kharif 2017.

Table 1a: Farmers Covered (million) under NAIS, WBCIS, MNAIS and PMFBY

(Kharif 2013 to Kharif 2016)

Season

NAIS

MNAIS

Total

% Increase

WBCIS

Grand Total

% Increase

Kharif 2012

10.7

2.1

12.8

8.1

20.9

Kharif 2013

9.7

2.4

12.1

-5.5

8.9

21.0

0.5

Kharif 2014

9.7

5.9

15.6

28.9

8.2

23.8

13.4

Kharif 2015

20.6

4.8

25.4

62.8

5.4

30.8

29.4

Kharif 2016

(PMFBY)

38.9

53.1

1.5

40.4

31.2

Source: Agricultural Statistics at a Glance and Industry data

Table 1b: Farmers Covered (million) under NAIS, WBCIS, MNAIS and PMFBY

(Rabi 2012-13 to Rabi 2016-17)

Season

NAIS

MNAIS

Total

% Increase

WBCIS

Grand Total

% Increase

Rabi 2012-13

6.1

7.1

5.6

12.7

Rabi 2013-14

-1.4

5.3

12.3

-3.1

Rabi 2014-15

7.1

3.2

10.3

128.9

3.1

13.4

8.9

Rabi 2015-16

10.1

3.7

13.8

34.0

2.9

16.7

24.6

Rabi 2016-17

(PMFBY)

16.2

17.4

0.6

16.8

0.6

Source: Agricultural Statistics at a Glance and Industry data

Area Coverage

The total area insured in kharif and rabi taken together has increased only slightly by 6.5

percent (from 53.7 million ha in 2015-16 to 57.2 million ha in 2016-17). The area under the

new scheme has increased from 27.2 million hectare (MNAIS & NAIS combined) in Kharif

2015 to 36.6 million hectare (PMFBY) in Kharif 2016, an increase of 34.6 percent (Table

2a). States registering significant increase in area coverage in Kharif 2016 included Assam,

Gujarat, West Bengal and Uttarakhand (Annexure 4). In Rabi 2016-17, area insured has

shown a marginal decline as compared to Rabi 2015-16.

Area insured under WBCIS has however fallen from 11.1 million hectare in Kharif 2012 to

1.3 million hectare in Kharif 2016, drop of about 88 percent (Table 2a). Our discussions with

experts in the industry reveal the following main reasons for this drastic fall in area insured:

All data related to Kharif 2016 and Rabi 2016-17 are updated as on December, 2017

High Actuarial Premium Rates

The actuarial rates vary across states. With the removal of capping of premium rates and no

reduction in sum insured, actuarial premium rates have increased in Kharif 2016 compared to

previous years. It increased from 11.6 percent in Kharif 2015 to 12.5 percent in Kharif 2016

(Table 6a). For WBCIS, the actuarial premium rates were as high as 43 percent and 33.5

percent for states like Rajasthan and Maharashtra, respectively, in Kharif 2016 (Annexure 6).

In case of horticulture crops also, actuarial premium rates were at very high levels in Kharif

2016. For example, in case of Maharashtra it varied in the range of 40 percent to 55 percent

for pomegranate and 55 percent to 70 percent for guava.

Faulty Product Design

Our discussions with insurers further revealed that in many cases, there is no correlation

between temperature and other triggers in the weather station and yield calculation.

Whenever there is a temperature trigger, farmers are eligible for compensation even if there is

no reduction in yield. As informed by insurance companies, agriculture departments of states

prepare term sheets but in many cases these are designed in such a manner that it necessarily

triggers a payout. For example, in case of Alwar district in Rajasthan, farmers were eligible

for compensation in case rainfall received was below 300 mm (Annexure 7). Historical data

of this district show that in the past twenty years this amount of rainfall has never been

received. Therefore, insurance companies (aware of almost compulsory payout) quoted high

actuarial rates of 70 percent to recover their losses.

Ethical Issues

The authors were informed by some key stakeholders in the crop insurance chain that there

were cases of unethical practices in some districts by manipulating temperature at the weather

station to cause “trigger”. For example, in Churu district of Rajasthan in 2013 and 2014, there

are allegations that some famers had used ice in the weather station that led to deviation in

actual temperature and they became eligible to receive claims.

Table 2a: Area Insured (million ha.) under NAIS, WBCIS, MNAIS and PMFBY

(Kharif 2012 to Kharif 2016)

Season

NAIS

MNAIS

Total

% Increase

WBCIS

Grand Total

% Increase

Kharif 2012

15.7

2.2

17.9

11.1

Kharif 2013

14.3

2.3

16.6

-7.3

11.2

27.8

-4.1

Kharif 2014

11.6

7.0

18.6

12.0

9.6

28.2

1.4

Kharif 2015

21.7

5.5

27.2

46.2

6.3

33.5

18.8

Kharif 2016

36.6

34.6

1.3

37.9

13.1

Source: Agricultural Statistics at a Glance and Industry data

Table 2b: Area Insured (million ha.) under NAIS, WBCIS, MNAIS and PMFBY

(Rabi 2012-13 to Rabi 2016-17)

Season

NAIS

MNAIS

Total

% Increase

WBCIS

Grand Total

% Increase

Rabi 2012-13

8.7

0.7

9.4

5.9

15.3

Rabi 2013-14

6.5

3.3

9.8

4.3

5.3

15.1

-1.3

Rabi 2014-15

9.3

3.6

12.9

31.6

4.8

17.7

17.2

Rabi 2015-16

11.8

3.5

15.3

18.6

4.9

20.2

14.1

Rabi 2016-17

18.9

23.5

0.4

19.3

-4.5

Source: Agricultural Statistics at a Glance and Industry data

Gross Premium

In MNAIS and WBCIS, the premium rates were capped at 11 per cent and 9 per cent (of sum

insured) for food and oil seeds crops for Kharif and Rabi season respectively. In case of crops

whose premium was higher than the capped level, sum insured was reduced to capped level

whereas actuarial rates continued to apply (Reduction in sum insured was only in case of

MNAIS). It was basically done to reduce the liability of GOI and State Government towards

premium subsidy. In NAIS there was no such restriction on sum insured as the claim itself

was paid by Central and State Government if it exceeded the total premium amount. The

capping resulted in very low sum insured and high premium rate under MNAIS. This issue

has now been resolved in PMFBY. There is no capping on premium rates and sum insured is

now based on the Scale of Finance for the district as decided by district level technical

committee. With the removal of capping on premium rates, sum insured has increased

significantly in few districts. One such example is that of maize crop in Gorakhpur district of

Uttar Pradesh. The actuarial rate for Kharif maize 2015 under MNAIS was 57 percent and the

original sum insured was Rs 8,415/ha. However, as capped premium rate of 11 percent was

applicable, the sum insured was reduced to Rs 1624/ha. With the implementation of the new

scheme, the actuarial rate for maize in the same district in Kharif 2017 went down to 4.22

percent and the sum insured has increased to Rs 12,096/ha.

Under PMFBY, the farmers’ share of premium (as percentage of sum insured) is fixed at 2

percent in Kharif 2016 and farmer's share in gross premium accounts to 17 percent

Difference between actuarial rate and farmers’ premium is being given as premium subsidy

by GoI and State Government. Thus, farmers are receiving premium subsidy to the extent of

83 percent by the Central and the State government

. The government has allocated Rs

13,000 crore in 2018-19 (BE). The expenditure for 2016-17 and 2017-18 (RE) was Rs 11,051

crore and Rs 10,698, respectively. It included the amount required to settle pending claims

under NAIS.

In Kharif 2016, the total value of gross premium for Kharif 2016 under PMFBY is Rs 15,488 crore out of

which Rs 2666 crore is borne by farmers.

The level of subsidy would differ depending on the actuarial premium discovered through bidding.

With the removal of capping on premium rates, there has been a quantum jump in gross

premium (Table 3a and 3b). It has increased by 486.6 percent (for PMFBY) in Kharif 2016

and almost 275.3 percent in Rabi 2016-17.

Table 3a: Gross Premium (Rs crore) under NAIS, WBCIS, MNAIS and PMFBY

(Kharif 2012 to Kharif 2016)

Season

NAIS

MNAIS

Total

Increase

WBCIS

Total

Increase

Kharif 2012

878

564

1442

1294

2736

Kharif 2013

975

639

1614

11.9

1478

3092

13.0

Kharif 2014

844

928

1772

9.8

1565.5

3337.5

7.9

Kharif 2015

1828

812.4

2640.4

49.0

986.9

3627.3

8.7

Kharif 2016

15488.3

486.6

863.2

16351.2

350.8

Source: Agricultural Statistics at a Glance and Industry data

Table 3b: Gross Premium (Rs crore) under NAIS, WBCIS, MNAIS and PMFBY

(Rabi 2012-13 to Rabi 2016-17)

Season

NAIS

MNAIS

Total

% Increase

WBCIS

Total

% Increase

Rabi 2012-13

447.6

189

636.6

923.0

1559.6

Rabi 2013-14

297.5

434.8

732.3

-27.6

923.4

1655.7

6.2

Rabi 2014-15

550.6

501.5

1052.1

43.7

556.4

1608.5

-2.9

Rabi 2015-16

716.7

543.8

1260.5

19.8

603.5

1864.0

15.9

Rabi 2016-17

4731.1

275.3

798.9

5530

196.7

Source: Agricultural Statistics at a Glance and Industry data

Sum Insured

The sum insured for both loanee and non-loanee farmers are equal to the scale of finance as

decided by the District Level Technical Committee (DLTC). For an individual farmer, the

sum insured is equal to the Scale of Finance per hectare multiplied by area of the notified

crop proposed by the farmer for insurance. In NAIS and MNAIS, the sum insured for loanee

farmers was equal to the amount of crop loan sanctioned which was extendable upto the

value of the threshold yield. There are many instances when a State Government fixed sum

insured very low so that the outgo under NAIS for payment of claims was limited and

premium subsidy under MNAIS borne by State Government was not very high.

As compared to Kharif 2015, the total sum insured for all the states has increased from Rs

60,773 crore (MNAIS & NAIS) to Rs 1,24,382 crore (PMFBY) in Kharif 2016, an increase

of about 104.7 percent (Table 4a). The total value of sum insured under PMFBY and

RWBCIS combined has increased by 89.4 percent in Kharif 2016. In Rabi 2016-17 sum

insured has increased by 65.3 percent under PMFBY and 50.3 percent in PMFBY and

RWBCIS (Table 4b).

The sum insured per hectare was Rs 33,984 in Kharif 2016 and Rs 34,847 in Rabi 2016-17

(Table 5a and 5b) under PMFBY. However, even under PMFBY farmers are provided

coverage only to the extent of cost of cultivation as estimated by DLTC for arriving at Scale

of Finance (SoF) and not the loss of their prospective incomes. Therefore even though there

is an increase in sum insured per hectare, this amount may still not be adequate to cover a

farmer’s risk of loss of income due to lower market prices. The maximum claim is limited to

cost of cultivation, not loss of prospective income.

Table 4a: Sum Insured (Rs crore) under NAIS, WBCIS, MNAIS and PMFBY

(Kharif 2013 to Kharif 2016)

Season

NAIS

MNAIS

Total

% Increase

WBCIS

% Increase

Kharif 2012

27199

4897

32096

12871

44967

Kharif 2013

28924

5825

34749

8.3

14623

49372

9.8

Kharif 2014

24389

9481

33870

-2.5

13254

47124

-4.6

Kharif 2015

52508

8265

60773

79.4

8533

69306

47.1

Kharif 2016

124382

104.7

6903

131285

89.4

Source: Agricultural Statistics at a Glance and Industry data

Table 4b: Sum Insured (Rs crore) under NAIS, WBCIS, MNAIS and PMFBY (Rabi

2012-13 to Rabi 2016-17)

Season

NAIS

MNAIS

Total

% Increase

WBCIS

Total

% Increase

Rabi 2012-13

15708

2077

17785

10655.5

28440.5

Rabi 2013-14

12549.5

6406.5

18956

6.6

10901.9

29857.9

Rabi 2014-15

21512.5

9107.8

30620.3

61.5

4400.4

35020.7

17.3

Rabi 2015-16

27809.6

12022.6

39832.2

30.1

6294.2

46126.4

31.7

Rabi 2016-17

65860.8

65.3

3473.1

69333.9

50.3

Source: Agricultural Statistics at a Glance and Industry data

Table 5a: Sum Insured (Rs) per hectare under NAIS, MNAIS and PMFBY (Kharif

2012 to Kharif 2016)

Season

NAIS

MNAIS

WBCIS

Kharif 2012

17324

22259

11595

Kharif 2013

20227

25326

13056

Kharif 2014

21025

13544

13806

Kharif 2015

24197

15027

13544

Kharif 2016

33984

53100

Source: Authors' calculations

Table 5b: Sum Insured (Rs) per hectare under NAIS, MNAIS and PMFBY (Rabi

2012-13 to Rabi 2016-17)

Season

NAIS

MNAIS

WBCIS

Rabi 2012-13

18055

29671

18060

Rabi 2013-14

19307

19414

20570

Rabi 2014-15

23132

25299

9168

Rabi 2015-16

23567

34350

12845

Rabi 2016-17

34847

86828

Source: Authors' calculations

Table 6a: Gross Premium as a Percentage of Sum Insured (Kharif 2012 to Kharif

2016)

Season

NAIS

MNAIS

WBCIS

Kharif 2012

3.2

11.5

10.1

Kharif 2013

3.4

10.1

Kharif 2014

3.5

9.8

11.8

Kharif 2015

3.5

9.8

11.6

Kharif 2016

12.5

12.1

Source: Authors' calculation

Table 6b: Gross Premium as a percentage of Sum insured (Rabi 2012-13 to Rabi

2016-17)

Season

NAIS

MNAIS

WBCIS

Rabi 2012-13

2.8

9.1

8.7

Rabi 2013-14

2.4

6.8

8.5

Rabi 2014-15

2.6

5.5

12.6

Rabi 2015-16

2.6

4.5

9.6

Rabi 2016-17

7.2

22.7

Source: Authors' calculations

3.2 Challenges in the Implementation of PMFBY

Extension of cut off dates

As Kharif 2016 was the first cropping season of the new scheme, various states claimed that

they faced teething problems in bidding process for selection of the insurance companies for

concerned clusters. After the issue of guidelines by GOI in February 2016, several State

Governments invited bids for discovering actuarial rates for various crops in cluster of

districts. As against the original cut-off date of July 31, mentioned in the operational

guidelines of the scheme, some states requested the Centre for an extension of cut-off date.

Thus, due to delay in carrying out the requisite preliminaries the date of tender submission

was extended to 10

August 2016.

However, most of the states that floated their tender on time and completed the tender process

were able to receive low actuarial premium rates. For example states like Andhra Pradesh,

West Bengal and Chhattisgarh completed their bidding process in the months of April and

May, 2016. These states were able to receive actuarial rates between 4-9 percent. However,

other states like Bihar, Gujarat, Rajasthan and Maharashtra were late in opening and

evaluating bids and completed the process only in the months of June and July, 2016 and they

received high actuarial rates of around 20 percent (Annexure 6). Moreover, requests for such

extensions of cut-off dates by State Government in future could lead to the problem of

adverse selection. For example, Bihar encountered excessive rainfall and flood during Kharif

2016. The tender was floated in July, 2016 when the flood situation was already known. As a

result, the companies quoted very high actuarial rate of 17 percent. Moreover, the reinsurance

companies also quoted high reinsurance rates. Similarly, after the demonetisation of Rs 500

and Rs 1000 currency notes was announced by the government, the cut off dates for

enrolment under PMFBY in Rabi 2016-17 was extended to 10th January, 2017 from the

original date of 31st December, 2016.

Actuarial Premium rates and premium subsidy

The gross premium for FY 2016-17 is Rs 21,882 crore (PMFBY and RWBCIS) out of which

famers’ share is Rs 4,373 crore. The remaining premium subsidy is shared by the Central

government and the State government. The share of the Central Government was Rs 6,623

crore in Kharif 2016 and Rs 2,182 crore for Rabi 2016-17. Thus the total amount required for

premium subsidy by the Central government was Rs 8,805 crore in 2016-17. As mentioned

above, there were outstanding bills of NAIS also. Due to increase in premium subsidy in

Kharif 2016, the government revised the amount allocated towards crop insurance to Rs

11,051 crore in FY 2016-17. In 2016-17, the actual expenditure of DACFW on all the

schemes was Rs 36,912 crore out of which Rs 13,397 crore was for interest subvention. Thus

if interest subvention is excluded from department’s budget, premium subsidy on crop

insurance took almost 17 percent of the budget of Department of Agriculture, Cooperation

and Farmers' Welfare

With an increase in area insured it was expected that the actuarial premium rates would go

down. However, gross premium as a share of sum insured increased to 12.5 percent in Kharif,

2016. Although there is an increase in the actuarial premium rates, it must be noted that there

are comparability issues across various insurance schemes. In case of NAIS, premium rates

were administered by the government and in MNAIS they were market determined but

capping on these rates acted as a barrier in real discovery of actuarial premium rates. Under

PMFBY, capping on premium rates was removed and therefore the actuarial rates of 2016-17

can be said to perhaps reflect the risk profile more accurately.

Experts in the industry also informed the authors that high actuarial rates were also caused by

the expansion of reinsurance market. According to them, only 25 percent of risk (as a

percentage of sum insured) is absorbed by the domestic insurance companies. Out of the

remaining 75 percent, 50 percent is absorbed by the domestic reinsurance company (General

Insurance Corporation) and balance 25 percent by foreign reinsurance companies. Some of

the major foreign players include Swiss Re, Munich Re, SCOR, Hannover Re and Berkshire

Hathaway. The risk has shifted from insurance companies to the reinsurance companies and

therefore the actuarial premium rates may not come down anytime soon unless the

administration of scheme at the state level improves substantially. Contrary to this statement,

some other experts have suggested that with an increase in area insured to 100 million

hectare, the actuarial premium rates could come down to as low as 3-4 percent. However, as

it appears today, with rather unpredictable ways of implementation of the scheme, reinsurers

don’t have full confidence and therefore premium rates are likely to remain high unless

concerted efforts are made to strictly follow operational guidelines of the scheme so that

reinsurers get the confidence that Indian crop insurance players, including the state

governments would play by the rules. Payment of premium subsidy by government to

insurance companies in time and adherence to cut-off dates are the minimum pre-conditions

to encourage insurance companies to quote lower rates in future.

Inadequate insurance coverage

Sum insured per hectare has increased to Rs 33,984 in Kharif 2016 and Rs 34,847 in Rabi

2016-17 under PMFBY. As PMFBY is yield based, price risk is still not covered and farmers

remain exposed to volatility in prices of agricultural commodities. So, even the new crop

insurance scheme has not been able to cover loss of prospective income of farmers due to

vagaries of market. Sum insured was to be equal to the SoF for that crop as fixed by District

Level Technical Committee. But the data for Kharif 2016 reveals that sum insured in many

districts was way lower than SoF. For example, in Alwar and Dungarpur district in Rajasthan,

SoF for cotton was Rs 58,500 per hectare and Rs 1,50,000 per hectare, respectively against

sum insured of Rs 15,720 and Rs 18,720 per hectare, respectively. This was possibly done as

the state government may have preferred lower sum assured so as to restrict its share of

premium subsidy.

Insufficient and inefficient CCEs

The total number of CCEs planned by the government for both Kharif and Rabi season in

2016-17 was 9.27 lakh. With the CCEs being brought down to village panchayat level, it is

expected that the number of CCEs will go up to 30 lakhs (20 lakhs in Kharif season and 10

lakhs in Rabi season). In the operational guidelines of PMFBY, the use of mobile based

technology with GPS stamping has been mandated to improve the quality of data and make

faster assessment of claims. However, neither the number of CCEs has increased nor have the

State governments in most of the states procured mobile devices to make smart assessment of

crop yield. Our discussions with experts reveal that there were large scale data manipulations

in some cases while conducting CCEs. For example there is egregious case of, Rajkot district

in Gujarat in Kharif 2016 where it is claimed that the yield of groundnut was largely

underestimated which made the insurance companies liable to pay exaggerated claims to

farmers. As informed by experts in the industry, despite bumper harvest of groundnut, the

yield was reported to be 500 kilograms per hectare against actual estimated yield of about

1200 kilograms per hectare. Due to underestimation of crop yield, insurance companies may

become liable to pay huge claims even if there is no actual reduction in yield of crops. This is

nothing short of a fraud in the name of crop insurance and brings very bad name to the

implementation failure of PMFBY. It needs to be investigated at the highest level,

responsibility must be fixed and stern action may be initiated against unscrupulous elements.

Only then PMFBY can be salvaged and premiums reduced. Else, we are afraid, it may not

serve its intended purpose. It may be emphasized that if GoI wants PMFBY to succeed, it has

to ensure transparency in conduct of CCE and prevention of malpractices.

Assessment and payment of claims

The state government is responsible for providing yield data of CCEs to insurance companies

and claims are to be settled within three weeks from the date of data receipt. But companies

have not yet paid their claims to farmers and they have cited delay in receiving premium

subsidy from the state government as the main reason for delay settlement on claims. For

instance insurance companies have partially received premium subsidies from states like

Bihar, Assam, Madhya Pradesh and Karnataka (Annexure 8). Another reason for delay in this

process is late submission of yield data by states to companies, which extends way beyond

the required date of notification. This is true in case of states like Gujarat and Tamil Nadu in

Kharif 2016 and Rajasthan and Tamil Nadu in Rabi 2016-17 that have partially submitted

yield data to insurance companies.

There have been allegations in media made that insurance companies have made large profits

at the cost of farmers and government as gross premium collected is far greater than claims

paid to farmers. It must be noted that Kharif 2016 was a year of normal monsoon with only 3

percent shortfall at all India level and drought prone regions in Central India received 6

percent above normal rains. In normal rainfall years, it will be common that claim payouts

are likely to remain lower than premiums collected, while in bad years with drought/floods,

etc the claims may even exceed premiums collected. The nature of insurance business has to

be seen over a cycle of about 5 years, which includes good, normal, and bad years to see how

far the premiums collected match with payments made as compensation. The total amount of

claims paid is 2016-17 (PMFBY and RWBCIS) is Rs 12,117 crore against claims reported

worth Rs 13,692 crore till December 2017. However, during drought/flood years, claims paid

would surpass gross premium collected depending on the intensity of weather calamity. Thus,

the effectiveness of PMFBY cannot be judged on the basis of data of one year. .

The case of Tamil Nadu is worth highlighting as the state experienced one of the worst

droughts in 2016-17. The total claim paid to farmers in Rabi 2016-17 is Rs 2,414 crore

against gross premium of Rs 1,232 crore and the premium to claim ratio of 1.96 (196

percent). The yield data on CCE for Rabi 2016-17 was furnished to insurance companies in

time (by 1/05/2017) and the state government also paid its share of premium subsidy to the

insurance companies. As a result, most of the farmers in Tamil Nadu received claims for their

crop damage caused by drought in that season. This stands as an outstanding example that

could be emulated by other states to provide yield data and premium subsidy on time to

insurance companies. The case of Karnataka is also worth mentioning as they have made a

portal dedicated to crop insurance and all the information relating to this scheme is made

available on this website (Box 1). Thus insurance companies could disburse claims within

one week of receipt of yield data.

BOX 1: CASE STUDY OF KARNATAKA

Karnataka Government has made Samarakshane portal which has been operational for

about 20 months. It handles all facets of PMFBY right from issue of notification till the

payment of the compensation, including updation of such compensation details. Number of

crop cutting experiment required for CCE is 4 for major crop and notified at Gram

Panchayat level and 10 for minor crop notified at Hobli (sub taluka) level. The number of

experiments under NAIS during Kharif 2015 was 74,242 and this has increased to 85,166

in Kharif 2016 and further to 88,434 in Kharif 2017. For crop cutting experiments, mobile

phones have been made mandatory. Mobile phones were introduced under Kharif 2016 and

as induction of mobile phones was delayed, 32,447 experiments out of 85,166 experiments

were conducted by mobile phones. In Rabi & Summer 2016-17 all 52,208 experiments

were conducted using mobile phones. They are used to capture images of CCE increasing

transparency and accuracy of the data.

It is not just the Crop Cutting Experiment data that is given to the insurance company. It

includes other information such as claim statements, farmer-wise including farmer’s

Aadhaar number and account number. The insurance company can make the payment soon

after the sheet is given.. However, compensation is delayed by some insurance companies

as they raise objection on the CCE data provided by the government. To address this

issue, since Kharif 2017, insurance companies are made to participate in CCE and can raise

objections on the mobile phone platform itself. Thereafter, they would not be allowed to

raise any objections at a later point of time. This will enhance transparency in the data

received for CCE so that claims could be disbursed to farmers on time.

Thus, other states should design similar portal like Karnataka and provide complete

information of CCE, use of technology, updation of pictures from CCE and provide timely

information to insurance companies and also involve them in CCE. State portals should be

linked to crop insurance portal of GoI so that there is no mismatch in data. State portals

should be linked to crop insurance portal of GoI so that there is no mismatch in data.

The litmus test of any crop insurance scheme depends on quick assessment of crop damage

and payment of claim into farmers’ bank account. The infrastructure to make this scheme

fully operational is still inadequate. Timely submission of yield data by State government to

the insurance companies is necessary so that they can finalise the claims expeditiously and

pay the claims to farmers. .

A comparative statement of these three schemes, NAIS, MNAIS, and WBCIS, on various

parameters is given below in Table-7.

Table 7: Comparison between NAIS, MNAIS and PMFBYs

Details

NAIS

MNAIS

PMFBY (2016-17)

Highlights of PMFBY

1. Penetration of these schemes

in terms of farmers covered

(average between 2013-14 and

2015-16 for NAIS and MNAIS)

Covered 20.3 million

farmers (7 million farmers

in Rabi season and 13.3

million farmers in Kharif

season)

Covered 7.7 million farmers

(3.3 million farmers in Rabi

season and 4.4 million

farmers in Kharif season)

Covered 55.1 million farmers

(16.2 million farmers in Rabi 2016-

17 and 38.9 million farmers in

Kharif 2016)

Increase in farmers

covered by 96 percent

2. Coverage in terms of area

insured (average between 2013-

14 and 2015-16 for NAIS and

MNAIS)

Covered 25 million

hectares (9.2 million

hectares in Rabi season and

15.8 million hectares in

Kharif season)

Covered 8.3 million hectares

(3.4 million hectares in Rabi

season and 4.9 million

hectares in Kharif season)

Covered 55.4 million

hectares(18.9 million hectares in

Rabi 2016-17 and 36.6 million

hectares in the Kharif 2016)

Increase in area

coverage by 66 percent

3. Use of Crop Cutting

Experiments (CCE)

Based on block level:

Panchayat provision was

present

Based on Panchayat level.

Increase in the number

of CCE

4. Level of Indemnity

Three levels of indemnity—

90 per cent, 80 per cent and

60 per cent, corresponding

to low-risk, medium-risk

and high-risk areas

Two levels of indemnity-90

percent and 80 percent

Three levels

of indemnity—90 per cent, 80 per

cent and 70 per cent

Three level of

indemnity

5. Threshold Yield

Based on moving average

yield, of past three years, in

case of rice and wheat, and

five years’ yield in case of

other crops, multiplied by the

level of indemnity

Based on average of seven

years [excluding a maximum

of two years in which a

calamity such as drought]

Based on average of seven years

[excluding a maximum of two

years in which a calamity such as

drought]

6. Sum Insured

Extendable up to 150% value

of average yield of the

insured crop

Calculated by multiplying

Notional Threshold Yield

(Average yield of 7 years)

with MSP/farm gate price

which is reduced to capped

level of premium rates

Scale of Finance (equal to the cost

of cultivation) as decided by

District Level Technical

Committee

Based on Scale of

Finance

7. Sum Insured Covered

No reduction in sum insured

Reduction in sum insured if

actuarial premium rates

exceeds capped premium rates

No reduction in sum insured

Increase in sum insured

by 89 percent (between

2015-16 and 2016-17)

8. Settlement of Claims

No provision of ‘on account’

settlement of claims

‘On account’ payment of up

to 25 percent as immediate

relief to farmers

On account payment of up to 25

percent for prevented

sowing/crop damage reported

more than 50 percent

9. Premium Rates

Based on administered

premium rates

Based on actuarial premium

rates

Based on actuarial premium rates

9. Premium subsidy

It started with 50 percent

subsidy, which was to be

reduced to present 10 percent

every year only to small and

marginal farmers.

Calculation of subsidy is

based on premium slabs.

Heavily subsidised-about 83

percent by Central and State

Government

Heavy increase in

premium subsidy-83

percent

10. Funds allotted

An open ended scheme

where the state was allotted

funds by the centre in case of

any damage making it

lucrative for the states

There was no such provision

and the states were reluctant

to switch to MNAIS.

Both Centre and State

government to provide subsidies

to insurance companies

4. Learning from International Best Practices

The previous section discussed the existing system of agricultural insurance in India

highlighting its limitations in terms of area insured, premium rate, and government support in

the form of subsidy. It becomes pertinent to have an understanding of the practice of crop

insurance followed in other countries, particularly China which also has small agricultural

landholdings like India.

4.1 Crop Insurance in USA

In 1938, Congress formed the Federal Crop Insurance Corporation (FCIC) to protect the

income of the farmer from falling prices and crop failure. The insurance coverage was limited

to only wheat and cotton and this programme suffered from heavy losses and low

participation rates. Till 1980, this programme was mainly run by the government. With the

passage of the Federal Crop Insurance Act of 1980, there is increased involvement of private

players that has laid the foundation of its success.

The Federal Crop Insurance Reform Act of 1994 was passed to address the ad hoc disaster

compensations that were released from time to time by the government.

The participation of

farmers in crop insurance programme was made compulsory to be eligible for deficiency

payments under price support programmes. As participation in this programme was

compulsory, catastrophic (CAT) coverage was created where premium was subsidised. In

1996, the Risk Management Agency (RMA) was created to administer FCIC programmes

and other non-insurance related risk management and education programmes that support US

agriculture. The RMA of the U.S. Department of Agriculture sets the rates that can be

charged and determines which crops can be insured in different parts of the country. Private

companies are obligated to sell insurance to every eligible farmer who requests for it. Efforts

made by the government led to a substantial increase in area insured and by 1998, more than

180 million acres (73 million hectares) of farmland was insured, covering around 52 percent

of cropland, which is almost twice the area insured in 1993. The increase in premium

subsidies has made the insurance products more attractive and affordable to farmers.

There are two types of crop insurances available to farmers in the USA: multi-peril crop

insurance (MPCI) and crop hail policy.

While the crop hail policy is not a part of the FCIP, they are directly provided to farmers by

private insurers. The farmers purchase this policy in areas where crops are affected by

frequent hailstorms. They can be purchased at any time in the agricultural season.

On the other hand, MPCI is overseen and regulated by RMA. This is a public-private

partnership programme and 19 private companies are currently authorised by USDA RMA to

write MPCI policies. These policies cover loss in yield due to extreme weather conditions

A major drought in 1988 led to the ad hoc disaster assistance programme to provide relief to farmers. This

was followed by a series of disaster bills in 1989, 1992 and 1993.

and price risk to protect framers against potential loss in income. The crop insurance products

include individual plans as well as area plans.

The government plays an important role not only in subsidising the insurance premium of

farmers but also in reimbursing the operating and administrative expenses incurred by private

insurers. The subsidy provided by the government accounts for approximately 70 per cent of

the total premium amount (including operating and administrative expenses). The insured

area has increased to 120 million hectares in 2015. Thus, area insured has increased from 52

percent of cropland in 1998 to 89 percent

in 2015.

Table 8: Area Insured and Premiums paid by the Government (USA)

Year

Insured Hectares

(million)

Premium (million

USD)

Share of Premium paid by

Government (%)

2004

4,186

59.17

2005

100

3,949

59.36

2006

4,580

58.56

2007

110

6,562

58.26

2008

110

9,851

57.77

2009

107

8,951

60.63

2010

104

7,595

62.04

2011

108

11,971

62.33

2012

115

11,114

62.78

2013

120

11,788

61.80

2014

119

10,042

61.69

2015

121

9,747

62.34

Source: United States Department of Agriculture, Risk Management Agency, 2016

Revenue insurance protects farmers against fluctuations in price and yield and it has become

the most popular insurance product in the USA. Although, revenue insurance was tried by

several countries including Canada, Europe and Spain, USA is the only country in the world

that has been successful in running revenue insurance scheme. At present, revenue premium

accounts for nearly 85 per cent of total premium. Different insurance plans have various level

of coverage. For example, in the case of actual production history, insurance coverage varies

from 50 per cent to 85 per cent of yield and 55 per cent to 100 per cent of price (USDA, Risk

Management Agency, 2011).

4.1.1 Farm bill 2014

The 2014 farm bill has repealed direct payments, Countercyclical Payments, Average Crop

Revenue Election and Supplemental Revenue Assistance. It brought about changes in the

support given to farmers by introducing a few new crop insurance programmes, namely price

loss coverage (PLC) and Agriculture Risk Coverage (ARC). Farmers have to make a one-

time choice between ALC and PLC. PLC makes payment to producers when the market price

Insurance coverage of USA is the ratio of area insured and total cropland used for crops obtained from

Agricultural Statistics, USDA (2016)

of the crop is below a fixed reference price which is fixed in the Agricultural Act of 2014.

ARC makes payment when either the farm’s revenue from all crops or the county’s revenue

for a crop (the farmer may choose the alternative) is below 86 per cent of a predetermined or

benchmark level of revenue (5-year Olympic average county yield times 5-year Olympic

average of national price or the reference price—whichever is higher for each year).

Stacked income protection plan (STAX) is available for upland cotton acreage as they are not

eligible for the PLC or ARC plans. It provides coverage for a portion of the expected revenue

of the area. Subsidy level of 80 per cent of STAX premium in available to the producers.

Besides this, administrative and operating expenses are reimbursed to the insurance agency.

Supplemental coverage option (SCO) provides all crop producers with the option to purchase

area coverage in combination with an underlying individual policy. Indemnities are paid to

farmers when there is a fall in either the average yield or the average revenue per acre to

below 86 per cent level.

4.2 Crop Insurance in China

China is one of the few countries in the world which is at risk for a large variety of highly

destructive natural disasters. The country is affected by weather calamities such as drought,

floods, and hailstorm. According to a report by AIR Worldwide, drought and flood affects 52

per cent and 28 per cent of crop value in China, respectively.

Crop insurance is not new to China as the Peoples Crop Insurance Company of China (PICC)

had introduced livestock insurance in the 1950s. Based on the State Council Report submitted

by the People's Bank of China (1982), PICC implemented a pilot programme and received a

positive response. There was a steep rise in the annual premium from 1982 to 1993 and it

covered 29 provinces of China’s 34 provinces (including autonomous region and provincial

level municipalities). However, the average annual loss ratio

in this period was 105 per cent.

From 1993 until 2006, the insurance sector in agriculture witnessed a steep fall as the

premium amount fell from around 1000 million Yuan in 1993 to around 200 million Yuan in

2006. One of the primary reasons behind this decline was the high loss ratio coupled with the

strong market oriented focus of PICC. In 2006, a policy document of the State Council

recommended the exploration of a new model on agriculture insurance based on subsidies

from both the central and local governments. It also recommended establishing an

agricultural reinsurance system with fiscal support from both the central and the local

governments. In 2007, the government approved 1 billion Yuan (USD 130 million) towards

an agricultural insurance subsidy. This marked the beginning of a new phase of insurance in

the agricultural sector in China. Total premium rose from 0.8 billion Yuan (USD 104 million)

in 2006 to 5.3 billion Yuan (USD 690 million) in 2007. Since 2007, there has been a steep

rise in premium amount and it crossed 30 billion Yuan (USD 4.8 billion) in 2013. In the same

period, the total area insured has increased from 15.3 million hectares in 2007 to 73 million

hectares in 2013 and 115 million hectare in 2016. Thus, the penetration level has increased

Loss ratio is the ratio between the claims settled by the insurance company and premium paid to the

company.

from 10 per cent of the total sown area in 2007 to 45 per cent of the total sown area in 2013

and 69 percent in 2016 In terms of the number of farmers insured, the sale of agricultural

policy has increased from 51.8 million in 2007 to 150 million in 2010. The graph below

(Figure 2) shows the total premium and claim amounts for the period 2007 to 2013.

Figure 2: Total Premium Paid and Claims Received from Agriculture Insurance (2001-

2013)

Source: China Statistical Yearbook, various years

The loss ratio varied between 0.56 and 0.71 indicating the financial viability of the new

insurance model. China's crop insurance covers major crops like paddy, corn, wheat and

soybean. The programme covers seven natural disasters such as rainstorms, flood, water

logging, windstorms, hail, ice storms and droughts. This programme also covers some types

of livestock like pig breeding. In 2007, around 14.7 million breeding pigs were insured under

the programme and, in 2009, this increased to 52.7 million pigs covering, more than 75 per

cent of all breeding-pigs (Wang et al, 2011).

Under the government subsidy programme, the main form of agriculture insurance is yield-

based MPCI and the risk assessment is based on district or county yields. The threshold is set

at the township's average yield and the sum insured represents 30-40 per cent of production

costs for most crops (Swiss Re, 2009).

Weather insurance products are also available and they are modelled as an index of

parameters measured on officially recognised weather parameters. The claims are given when

there is a deviation measured at officially recognised weather stations.

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

Amount (billion yuan)

Premium paid Claim and Payment

4.2.1 Government Support in Agriculture Insurance

The Chinese Government intervenes to support agriculture in China through policy reforms

and subsidies. In the case of agricultural insurance, government support includes premium

subsidies and government reinsurance acts as the last resort. The table below gives the details

of premium subsidy provided by the Central and provincial government from the year 2007

till 2013.

Table 9: Premium Subsidy given by the Central and Provincial Government

Details

Central

Government (%)

Provincial

Government (%)

Total

Subsidies (%)

2007

Maize, rice, wheat, soya, cotton

2008

Maize, rice, wheat, soya, cotton,

peanut & rapeseed

2009

Maize, rice, wheat, soya, cotton,

peanut & rapeseed

2010

Maize, rice, wheat, soya, cotton,

peanut & rapeseed

2011

2012

2013

Note: Data from 2007 to 2010 are taken from FAO report, data for 2011 is taken from Caixin Online

(interview of Xiang Junbo, head of CIRC) and data for 2013 is taken from Global and China

Agricultural Insurance Industry Report, 2013-2014.

4.2.2 Agriculture Reinsurance

The government acts as a major reinsurer when the damage to crops is beyond a certain

threshold limit. As insurance is growing at a rapid pace since 2007, the reinsurance system

was introduced in a few provinces such as Beijing, Henan, Zhejiang and Jiangsu. In Beijing,

the municipal government acts as the last insurer and undertakes the remaining liability if the

loss ratio exceeds 160 per cent in any year in which catastrophe occurs. In addition, the

municipal government purchases reinsurance in private reinsurance market to cover

indemnity risk between 160 per cent and 300 per cent; they have established a reserve fund

for risk over 300 per cent. In this way, the government caps the indemnity of agriculture

insurance policy and introduces a risk sharing policy (World Bank, 2007).

Until 2002, China Re enjoyed the monopoly of being the only reinsurer company in China.

After 2002, Swiss Re and Munich Re were given business licences to operate in the Chinese

reinsurance market.

4.2.3 AIR Worldwide

Recently, China has tied up with AIR Worldwide and they have developed a cat-loss model.

They provide a probabilistic approach to determine the likelihood of losses to the country’s

major crops: corn, cotton, rapeseed, rice, soybeans and wheat. The model captures the

severity, frequency and location of adverse weather events, taking into account weather

variables (such as rainfall and temperature), soil conditions and crop-specific parameters

(AIR Worldwide, 2011).

4.3 Crop Insurance in Kenya- Kilimo Salama

Agriculture is the main occupation in the Kenyan economy. Around 70 per cent of the

workforce still depends on agriculture for their livelihood. Although traditional indemnity-

based insurance products are available to farmers in this region, it has several limitations such

as the long time lag in payout of claims, high premium rates and lack of faith in insurance

products.

Kilimo Salama (Safe Agriculture) is a weather-index based insurance product developed in

2009 by the Syngenta Foundation for Sustainable Agriculture (SFSA). This was launched in

partnership with Safaricom (the largest mobile network operator in Kenya) and UAP (a large

insurance company based in Kenya). It insures farm inputs such as seeds and provides

complete crop cycle cover for drought and excessive rain. Rainfall is measured using solar

powered weather stations and, in case of deviation from normal rainfall, claim payouts are

made to farmers. These weather stations are located at a radius of about 15 square kilometres.

It monitors rainfall and several other weather parameters such as wind speed, sunlight and

temperature and sends data to the central location every 15 minutes using GPRS technology.

Since 2012, SFSA has partnered with Columbia University’s Earth Institute to ground proof

and scale satellite index insurance products.

The foundation has entered into a partnership with Safaricom, which is the largest mobile

network operator in Kenya with 80 per cent market share. They developed an application that

uses Safaricom mobile technology, M-pesa, to transfer money for claims payout and

premiums. Agricultural stockists act as a medium of distribution of insurance products. The

farmers are registered with the agro-dealers using barcode which is linked to Cloud-based

system. Farmers who purchase insurance embedded seed bags send an SMS to short code

with details of unique code, upon which the farmer is automatically registered for insurance.

The confirmation message is immediately sent to farmers and they are automatically

connected to automated weather stations. Whenever there is a deviation in rainfall, leading to

germination failure, the claim amount automatically gets transferred into the accounts of

insured farmers. This process does not take more than 4 days and the farmers can use the

money for replanting crops. The premium rates vary from 4-13 per cent and this is shared

between the farmers and seed companies. The government plays no role in subsidising

premium payments.

Figure 3: Representation of Replanting Guarantee

Source: Syngenta Foundation for Sustainable Agriculture (SFSA)

4.3.1 Progress of the scheme

Since its inception in March 2009, the number of insured farmers has gone up from merely

185 to 1.87 lakh in 2013. The amount of premium collected has gone up from USD$543 to

USD$1,174,399 in the same period (about USD 1.2 million).

It must be noted that there is almost zero transaction cost in either issuing the policy or in

disbursement of claims. This system of claim disbursement via mobile technology is efficient

because of timely payout of claims and transparency in claims assessment.

4.4 Lessons for India

India can draw some lessons from some of the best international practices followed by

countries such as China, Kenya and the USA. The heavy premium subsidy programme

started by the Government of China in 2007 led to an expansion of insured farm area from 15

million hectares in 2007 to 115 million hectares in 2016, covering 69 percent of total sown

area. The premium subsidy payable by the government increased from 50 per cent to 80 per

cent of the total premium amount. The Kenyan experience is significant due to its efficiency

in settlement of claims within 2-4 days. Kilimo Salama (Safe Agriculture) is a weather index

based insurance product developed by Syngenta Foundation for Sustainable Agriculture

(SFSA) in 2009. They developed an application that uses Safaricom mobile technology, M-

pesa, to transfer money for claim payouts. Whenever there is a deviation from normal rainfall

resulting in germination failure, the claim amount automatically gets transferred into the

accounts of insured farmers. In the USA, the insured area is 300 million acres (about 122

million hectares) in 2015 has increased from 52 percent of cropland in 1998 to 89 percent in

2015. The government subsidy accounts for approximately 70 per cent (including operating

and administrative expenses) of the total premium amount. Revenue insurance, which

protects farmers against fluctuations in price and yield, has become the most popular

insurance product accounting for 85 per cent of total premium. In USA, crop insurance is

sold as a retail product and claims are analysed on the basis of data or productivity of

individual plots. It is possible to do it as the average size of landholding is about 174 hectare.

India could set up an agency under (Insurance Regulatory and Development Authority) IRDA

like RMA in USA to determine premium rates and crops to be insured in different parts of the

country.

5. Role of Technology in Crop Insurance

In India, under area based scheme (NAIS, MNAIS and PMFBY) damage is assessed on the

basis of CCEs. They are conducted for all notified crops in the notified insurance units to

assess the crop yield. Under PMFBY, the minimum sample size of CCEs is fixed at 24 at

district level, 16 at the block level, and 4 for major crops and 8 for other crops at the Village

Panchayat level. The issues relating to sample size and other technical matters are decided by

a technical advisory committee consisting of representatives from Indian Agricultural

Statistical Research Institute (IASRI), National Sample Survey Organisation (NSSO) and the

Ministry of Agriculture & Farmers’ Welfare.

However, there have been problems relating to CCEs from the early years of crop insurance.

In the previous insurance schemes such as CCIS and NAIS, there were a number of

operational and administrative problems such as the reliability and availability of data on real

time basis. Sometimes there is strong local pressure to underestimate the yield of crop as it

results in the area becoming eligible for crop insurance claims. The time gap between

conducting CCE and settlement of claims is usually very long and the farmers suffer in this

process as they may not have surplus money to invest in inputs required in the following

season.

Therefore, it is important that remote sensing technology is used that could reduce the time

between assessment of crop damage and payment of claim amount. The application of drones

and remote sensing satellites at fine resolution can prove to be effective in taking images that

could be used by agronomists to assess crop damage in the fields.

5.1 Application of Satellites in Agriculture

Satellite images are increasingly being used to map crop types, estimate crop yield, assess

damages for crop insurance and identify locations to conduct CCEs. The images are used in

combination with regression/crop models for acreage in many parts of the world.

At present, the major satellite of India, which is used for agricultural applications, is

Resourcesat 2. A repeat satellite Resourcesat 2A has recently been launched. Resourcesat has

three cameras. They are as follows:

(i) Advanced Wide Field Sensor (AWiFS): This camera provides pictures with a resolution of

56 metres and frequency of about five days.

(ii) LISS III: This camera provides pictures with a resolution of 23.5 metres and frequency of

24 days.

(iii) LISS IV: This provides images with a resolution of 5.8 metres and frequency of 48 days.

Besides these Indian satellites, data from Sentinel (European satellite) and Landsat (American

satellite) are also used for crop estimation.

One major challenge in the usage of satellite images is frequent obstruction by clouds in

Kharif season in the optical type of sensors (the sensors which operate in visible and infrared

region). These are overcome by the use of microwave satellites as they are able to capture

images even on cloudy days. However, the use of this technology has been successful for

assessment of paddy and jute crops.

5.2 Application of Drones in Agriculture

Drones or UAVs (Unmanned Aerial Vehicles) have been in use by the armed force since the

19th century. However, now their use has been extended to other sectors such as customer

product delivery, oil and gas, search and rescue, agriculture and crop management, and media

and entertainment. With advancement in technology and its vast usage, it is expected that the

projected annual sales of these unmanned vehicles will increase from around 40,000 in 2015

to 1,25,000 in 2020 (Cognizant Report on Drones, 2014). These are light-weight units,

typically ranging from two to fifteen pounds, and have flight duration ranging from 40 to 200

minutes. They are operated manually, have high resolution cameras, and they can capture

pictures and video and share information on a real time basis. According to an article in USA

Today, the Association for Unmanned Vehicle Systems International, the trade group that

represents producers and users of drones and other robotic equipment, predicts that 80 per

cent of the commercial market for drones would eventually be for agricultural uses.

Drones will have a significant role in agriculture and they will help increase efficiency in a

number of ways. The practice of identifying problems based on specific areas is called

precision farming, which has become popular through the use of drones. This enables farmers

to use resources such as pesticides and fertilisers more efficiently, and apply it only to

focused areas. In Japan, around 2400 unmanned helicopters are being used to spray fertilisers

and pesticides to 40 per cent of its rice fields. They are also being used in farms of wheat and,

soybeans and pine trees in South Korea (Jack Nicas, Wall Street Journal, 2014).

With the help of technological advances, tiny MEMS sensors (accelerometers, gyros,

magnetometers, and often pressure sensors), small GPS modules, incredibly powerful

processors, and a range of digital radios are available at low a cost due to their wide usage in

smart phones and consequent economies of scale. Drones are helpful to farmers in providing

a bird’s eye view of their land. They help in viewing a crop from the air and can reveal

various patterns like irrigation problems, soil variations, fertiliser requirements, etc.; they can

even track down lost livestock. With the help of airborne cameras and through images taken

by them, it is possible to identify the difference between a healthy and distressed plant which

is not visible to the naked eye. It is also possible to create time series data within several time

periods to help in better crop management.

UAVs could be used to assess crop damage and enable faster settlement of insurance claims

and payouts. It can take images of crop damaged by hail, wind, rainfall, etc. As they fly at

low heights, problems such as cloud obstruction that occur in the case of remote sensing

satellite images can be avoided to a large extent. They could be proactively positioned in

areas prone to damage. As soon as there is information on damage in a particular area, they

could be deployed to assess the damage on site so that accurate information is captured on

time.

Several countries such as Canada, Australia, Japan and Brazil have already been using drones

in agriculture. As early as in 1983, the Ministry of Agriculture, Forestry and Fishery of Japan

had put forward a request for the development of an unmanned helicopter for agriculture. The

Yamaha R-50 with its payload of 20 kg was the first practical, unmanned helicopter for crop

dusting. The government announced a formal policy promoting the use of unmanned

helicopters in 1991 for rice farming. In 2001, the total area of farmland being sprayed

by RMAX unmanned helicopters reached 310,000 hectares and they are being used in

agriculture areas for tasks including planting, weed management, fertilising and pest control

(Yamaha RMX,).

In USA, the Federal Aviation Administration (FAA) has recently given the approval for

usage of drones in agriculture. Following this, the world’s largest corn processor Archer-

Daniels-Midland Co. received approval to use UAV to assess crop damage. This will enable

to expedite the process of crop insurance. Other insurance companies such as AIG, USAA

and Erie Insurance Group have also obtained approval from the FAA to operate drones to

assist in their claims, risk assessment and underwriting practices.

In 2014, Skymet along with the Agriculture Insurance Company of India Limited (AIC) and

the Gujarat government conducted a pilot project using satellites and drones for the

groundnut crop. They were able to capture images a few centimetres away from the farmland,

which was not possible with the use of satellites. As the landholdings are small and there is

multiple cropping, such technology may be able to help in improving the estimation of the

yield of crops. States with digitised land records could benefit from this technology. These

UAVs can take images that can be superimposed on digital maps of states and help identify

farms and crops sown.

5.3 Low Earth Orbits (LEO)

LEO satellites are micro satellites weighing less than 500 kilogram at a height of 200

kilometres to 1200 kilometres above the earth surface. They travel at a speed of 28,000 km

per hour and are capable of completing a rotation around the earth in 90 minutes. China has

launched 100 satellites in 2014 and they have launched another 150 satellites in 2015. With

LEO satellites, images of vegetation could be captured to enable monitoring of crop growth

around the world. India could launch also such micro satellites that could be used for

assessing crop damage.

5.3.1 Planet Labs

Planet Labs is an organisation based in San Francisco, engaged in space and information

technology. They design, build and operate satellites that are called "doves". In 2014, they

delivered Flock 1, the world's largest constellation of earth-imaging satellites made up of 28

doves. They subsequently launched more satellites totalling 71 doves that take images of the

entire earth, every day. In February 2017, Planet Labs launched 88 satellites on an Indian

PSLV rocket and they hope to complete the constellation of tiny birds that will let it image

the whole planet daily with high resolution. They fly on low orbit and collect data from any

place on earth and this is significant in solving commercial, environmental and humanitarian

challenges. They make contact with the ground station, receive images and migrate to clouds.

The resolution is 3-5 m and each image is processed through their automated data pipeline

and delivered to customers via web tools. At present, they are used in areas such as

monitoring of crops, urbanisation, natural resources, asset management, logistics and site

development. As the satellites go into orbit, they take 90 minutes to complete a full circuit.

5.4 Government of India’s Programmes of use of Satellite Data for Agriculture

Government of India, Ministry of Agriculture & Farmers’ Welfare has launched many

programmes to use satellite data for agricultural applications. Under the FASAL (Forecasting

Agricultural output using Space, Agro-meteorology and Land based observations) project

pre-harvest production district/state/national forecasts are given for 8 major crops using

optical and microwave (for rice and jute) remote sensing data. Agricultural drought

assessment is carried at district/sub-district level using data of multiple satellites and other

collateral parameters under NADAMS (National Agricultural Drought Assessment and

Monitoring System) project. Production assessment of 7 major horticultural crops is carried

out using satellite data under the CHAMAN (Coordinated Horticulture Assessment and

Management using geo-informatics) project. The efficacy of use of satellite data for

improvement in yield estimation towards crop insurance is being explored through pilot

studies under the KISAN (C [K] Crop Insurance using Space technology and geo-

informatics) project.

5.5 Remote sensing-based Information and Insurance for Crops in Emerging

Economies (RIICE)

RIICE is a public-private organisation that aims to reduce the vulnerability of small rice

farmer in low-income countries in Asia and beyond. The countries that have a large area of

land under cultivation and a large per capita consumption of rice in Asia are selected by the

organisation. These countries include Bangladesh, Cambodia, India, Indonesia, Philippines,

Thailand and Vietnam.

RIICE has partnered with the European Space Agency (ESA) and other providers to scan the

earth surface using radar-based sensing technology. The radar based-remote sensing

data,

used in RIICE, have a high spatial resolution and high temporal resolution. It can detect the

growth of rice at a resolution of 3 by 3 metres. It is capable of taking data from the same spot

every few days as it circles around the earth. The data is stored in a map format and in

numerical tables, with the administrative unit at village level. The growth of rice is mapped in

three stages that include the sowing, growing and flowering stage. With this technology, it is

easy to identify the extent of damage of crops caused by droughts and floods. They are

particularly useful to government and policymakers in making decisions regarding trade

related issues and insurance company in calculating risks of yield losses. The insurance

products are delivered with the help of rural banks and co-operative institutions.

6. Conclusions and Policy Recommendations

Crop insurance has been in the country since 1972, yet it is beset with several problems such

as lack of transparency and non-payment/delayed payment to farmers. Until recently (till

March 2016), there were three crop insurance schemes operating in India – NAIS, MNAIS

and WBCIS. However, it met with limited success due to high premium rates of 8-10 per cent

under MNAIS and WBCIS, delay in settlement of claims, which took around 6 to 12 months,

inadequate sum insured and their capping under MNAIS and inadequate government support

in the form of premium subsidies, had left a vast majority of farmers without any significant

insurance coverage. Realizing the limitations existing system of crop insurance, the

government launched a new crop insurance scheme, Pradhan Mantri Fasal Bima Yojana

(PMFBY) in Kharif 2016. In this paper, we have made an attempt to evaluate the

performance of the scheme for the year-2016-17. The broad conclusions and policy

recommendations that emerge from the analysis of Section 2, 3, 4 and 5 are as follows:

Low penetration of agricultural insurance

The penetration of agricultural insurance in India is still low in terms of the area insured and

the number of farmers covered. In three year period (2013-14 to 2015-16), the average area

insured under all the schemes was around 47 million hectare and the number of farmers

insured was 39 million. With the implementation of PMFBY both area insured (in hectare)

and farmers covered have increased to 57 million. In total, area covered in 2016-17 accounts

for about 30 percent coverage to gross cropped area in the country, less than half of what

USA (89 percent coverage) and China have achieved (69 percentage coverage) (Figure 4).

The first year target of 30 percent coverage in terms of area insured has been achieved and

the government aims to cover 40 percent in 2017-18. In this context, we would like to

highlight that 27 percent coverage was already achieved in 2015-16 even without the

implementation of the scheme. So, there is a long way to go if India has to scale up its crop

insurance sector like USA or China. To achieve this, it is critical to ‘fix the system’ for

greater transparency, accuracy, and timeliness, as it is scaled up.

Radar-based sensors have an advantage over optical observation as the obstructions of clouds in mapping

and monitoring of earth can be avoided.

Figure 4: Area under Crop Insurance in India, China and USA

Source: Agricultural Statistics at a Glance (various years), Industry data, USDA and

Krychevska (2017) for China

Premium administration related issues

Crop insurance is one of the largest items of expenditure in the central budget. Out of total

expenditure of Department of Agriculture Cooperation and Farmers’ welfare, of Rs 36,912

crore in 2016-17, the expenditure on crop insurance as premium subsidy was Rs 11,051

crore. Thus it has taken away almost one-third of allocation. Crop insurance is the third

largest segment in non-life insurance sector after motor and health insurance. The scheme is

administered by credit and insurance division under a joint secretary in the Department of

Agriculture, Cooperation and Farmers’ Welfare, GoI, who is assisted by two directors. The

joint secretary and directors are transferrable to other ministries or state government. The

only person who has continued in insurance section in the department is an Assistant

Director. Even in the Agriculture Insurance Company of India, there was no regular Chief

Managing Director from March, 2016 to May, 2017 when PMFBY was being implemented

in its very first year. Such a large and important scheme deserves a dedicated team of

professionals, both at the centre and in the states, which can collate and analyse the data

collected from the states and insurance companies. This team should also hand hold the

officers in State Government in documentation of crop insurance scheme. Since GoI bears 50

percent of premium subsidy, it is necessary that the actuarial rates received in tenders are

compared with rates of similar clusters and appropriate lessons are learnt and shared with

Years 2012, 2013, 2014, 2015, 2016 refers to 2012-13, 2013-14, 2014-15, 2015-16 and 2016-17,

respectively, for India.

100

120

140

2012 2013 2014 2015 2016

Area insured (million ha)

Area insured under NAIS & MNAIS/PMFBY (India)

Area insured under WBCIS (India)

Total Area Insured (India)

Total Area Insured (USA)

Total Area Insured (China)

states. On the basis of historical loss cost, attempt should be made by this professional team

in GoI to estimate actuarial premium for each cluster. This professional team should also

regularly update the insurance portal so that insurance companies, farmers, researchers and

NGOs can have access to the progress of scheme and its usefulness to farmers. Similarly the

State government also need to engage two or three professionals in their crop insurance cells

to analyse data generated at various levels. This can help in reducing premium rates in future.

Costing of Insurance Scheme-Premium subsidy

Premium on crop insurance is highly subsidised under PMFBY by the Central and State

government which is in line with international experience. Learning from the international

experience, it is clear that penetration of agricultural insurance increased only after the

introduction of heavy subsidy (80 percent) by the Government of China and 70 percent

(including administrative expenses) in USA. In this context, the Indian government must be

complimented for taking a bold step and reforming crop insurance.

The gross premium stands at Rs 21,882 crore in 2016-17 (increased by 298 percent since

2015-16) and about 83 percent of this amount is subsidised by the State and Central

government. As agriculture is increasingly becoming risky for farmers, the actuarial premium

rates under PMFBY in Kharif 2016 are very high and the government will have to continue

subsidising premiums paid by farmers. Expanding area insured should lead to a substantial

decrease in premium rates (at least upto 8 per cent) provided strict discipline is adhered to

and the scheme is implemented as per its operational guidelines.

Even assuming that the premium falls from 12.5 per cent to 8 per cent, as the scale of insured

area increases to 100 million hectares, and that the sum insured is, say, Rs.40,000/ha, the total

premium subsidy required by the Central Government will be Rs.12,000 crore for 100 million

hectares covered (Annexure 9).

The premium rates are calculated on actuarial basis under MNAIS and WBCIS which was a

departure from the administrative premium rates, fixed by the GoI that prevailed during

NAIS. Under MNAIS, the premium rates were capped in certain cases and the sum insured

per hectare was reduced to an amount commensurate with capped premium rates. Due to high

premium rates, the sum insured was very low in many districts. Under PMFBY, there is no

capping on premium rates and sum is insured it to be fixed based on the Scale of Finance.

With the removal of capping on premium rates, sum insured almost doubled in 2016-17

(Figure 5). But even under the new scheme, sum insured is based on scale of finance as

assessed by DLTC which covers only cost of cultivation.

Figure 5: Gross Premium and Sum Insured (all schemes combined) under Crop

Insurance

Source: Agricultural Statistics at a Glance (various years) and Industry data

Implementation issues related to PMFBY- cut off dates, yield data submission and

premium subsidy

The scheme can fly high only if operational guidelines are strictly followed and cut off dates

are not extended frequently.. One of the reasons for high actuarial premium rates quoted by

the reinsurance companies in Kharif 2016 was the extension of cut off dates. Such extensions

leads to the problem of adverse selection and companies quote high premium rates to cover

their losses. For example, Bihar encountered excessive rainfall and flood during Kharif 2016.

The tender was floated when the flood situation was already known. Thus, both insurance and

reinsurance companies quoted very high actuarial rate of 17 percent. There should never be

any extension of cut off dates under any circumstances.

Timely submission of yield data on CCEs to insurance companies by the state governments is

critical to the success of PMFBY so that they can make faster settlement of claims to farmers

in case of reduction in crop yield. Also insurance companies face delays in receiving

premium subsidy from State government and Government of India. This should be done

efficiently so that insurance companies could make timely settlement of claims. This may

also result in insurance companies quoting lower actuarial premium rates in future.

The data on initiation and finalisation of tender date, sum insured for each crops, actuarial

premium rates, payment of premium subsidy by GoI and state governments, submission of

yield data by state government, claim accepted and paid by the insurance companies should

be released by the government every month. This will enable to increase transparency and

improve efficiency of crop insurance program. The recently launched insurance portal is an

50000

100000

150000

200000

250000

2012-13 2013-14 2014-15 2015-16 2016-17

Amount (Rs crore)

Gross Premium Sum Insured

appreciable initiative as it will help in analysis of insurance data by government officers,

researchers and insurance companies.

In this context, we would like to highlight the case of Tamil Nadu which experienced one of

the worst droughts in 2016-17, resulting in substantial crop damage. As the yield data on

CCE for Rabi 2016-17 was furnished to insurance companies on time and premium subsidy

was paid by the State government, farmers received timely claims for their crop damage.

Being a drought season, premium to claim ratio was high at 1.96 in Rabi 2016-17. Thus,

Tamil Nadu stands as an outstanding example that can be emulated by other states to show

them that it is possible to make claim settlements on time.

Procedure for making assessment of crop damage

Optimizing CCEs

In the operational guidelines of PMFBY, the use of mobile based technology with GPS

stamping has been mandated to improve the quality of data and make faster assessment of

claims. Unfortunately, even after almost two years of the implementation of the scheme,

mobile devices have not been procured to make smart assessment of crop yield. Our

discussions with experts reveal that there were allegations of data manipulations in some

cases while conducting CCEs. For example, it is claimed that the yield of groundnut of

Rajkot district in Gujarat in Kharif 2016 was largely underestimated that made the insurance

companies liable to pay exaggerated claims from farmers.

We recommend conducting, monitoring and evaluating a small number of high quality CCEs.

This should be supervised and monitored by independent experts from state agricultural

universities and Krishi Vigyan Kendras. The use of satellites to identify farmland for

conducting CCEs is recommended to promote transparency and minimise the CCEs. Satellite

images could be used to determine broad location of CCEs, determination of area sown to

validate area insured and it may be possible to conduct CCE in areas which are prone to

higher losses. Use of handheld devices and mobile phones to capture multiple images in case

of heterogeneity of field conditions in a village could be beneficial in assessment of damage.

Karnataka has gone ahead as they have made Samarakshane portal. Not only does this portal

provide information related to CCE but other information such as claim statements, farmer-

wise including farmer’s Aadhaar number and account number. Mobile phones have been

made mandatory for CCE. Other states should design similar portal like Karnataka and

provide complete information of CCE, use of technology, updating of pictures from CCE and

provide timely information to insurance companies and also involve them in CCE.

Satellite/Drone Images

Use of drone/satellites could be a potential break through and 'realistic crop insurance' could

be made possible by leveraging on technology and having minimal reliance on human

intervention. Satellites and drones provide imagery data for assessing agriculture damage.

Collaboration with ISRO and satellites from other countries could play a significant role in

increasing the frequency of images captured for assessment of damage. Some experts in this

area have suggested that there is requirement of a constellation of five satellites both using

optical and microwaves technology, dedicated to crop insurance which would enable a

resolution of 5-10 metre within a frequency of 5-10 days. The average cost estimated for of

these satellite is Rs 400 crore (approximately) and it would require approximately Rs 2000

crore to set up this constellation.

Drones could also be used for providing images for assessing crop damage. As they fly at low

heights, the data could be captured with greater accuracy and the problem of cloud

obstruction can be avoided. They could be used to make quick assessment of localised

hailstorms, flood, etc. However, flying of drones in the country needs many official

clearances, which is time consuming. A single window clearance mechanism must be made

available to make wide scale usage of drones for agriculture in India.

To achieve greater accuracy in assessment of damage, large scale pilot studies need to be

carried out with the support of ISRO and Mahalanobis National Crop Forecasting Centre of

Ministry of Agriculture & Farmers’ Welfare. Funding and human resources support is

essential for carrying out these pilot studies.

Scaling up WBCIS-Expanding Automatic Weather Stations and Rainfall Data Loggers

Under WBCIS, there has been a substantial decline in area insured to 1.3 million hectare in

Kharif 2016 as compared to 6.3 million hectare in Kharif 2015. The primary reason for this

decline was the faulty product design where there is no correlation between temperature and

other triggers in the weather station and yield calculation, manipulating temperature at the

weather station causing “trigger” and prompting claim payments. If these issues are corrected

and India could follow Kenya's model of settling insurance claims within 2-4 days from the

occurrence of the event, WBCIS could be a success.

In order to get accurate data of rainfall, we have to plan to set up automatic weather stations

(AWS) and rainfall data loggers at the block level. In order to cover the entire country, one

block would require five automatic weather stations (AWS) and under each AWS, there

should be five rainfall data loggers

. The total cost is estimated be Rs.332 crore-1420 crore

to cover the entire country with weather stations.

There is need to have a proper quality check for both the instruments and the sighting of the

instruments. Additionally, there is requirement of regular maintenance of the weather

stations. However, Weather based crop insurance scheme can be successful only if term

The cost of one AWS is Rs 50,000 and the cost of a rainfall data logger is Rs 10,000, therefore one set of

AWS and rainfall data loggers for one block will cost Rs. 5 lakh.

For 6500 blocks, we would require 33000 AWS and approximately 170,000 rainfall data loggers. We

already have 9000 AWS in the country; hence, we would require approximately 25000 AWS and 170,000

rainfall data loggers. According to the specifications regarding weather parameters required by WBCIS, the

cost of each AWS is Rs. 65,000. However, as per MAHAWEDH project launched by the Government of

Maharashtra, the cost of AWS is estimated to be around Rs 5,00,000. Costs of 25000 AWS is Rs.162 crore-

Rs 1250 crore. Cost of 170,000 rainfall data loggers is Rs.170 crore

sheets are prepared by professionals under the supervision of agricultural universities without

any undue influence of local politicians. The term sheets should reflect the local weather

conditions. An independent agency such as State Agriculture Universities and ICAR

institutes is required to be deeply involved for this purpose. Weather based insurance

products can provide quick claim to farmers if term sheets are unbiased and weather stations

are well managed.

Procedure for settlement of claims to farmers under WBCIS

Based on the Kenyan model of agricultural insurance, primarily for weather insurance, the

stockist (seed and fertiliser shops) could act as distribution channels for selling insurance

products. The farmers could purchase an insurance cover by paying the premium amount.

This can be made available in the form of scratch cards. Crop specific scratch cards

(premium) could be made available in the market. These cards can be in different acreage

denominations (up to 1 ha; 2 ha; etc.). The farmers could then send an SMS using the number

mentioned on them.

In case of adverse weather conditions, farmers would receive compensation and the amount

could be directly credited into their bank accounts. This amount could be used to replant and

harvest their crops in the same season. This kind of technology ensures transparency, timely

payment of claims and satisfaction among farmers.

Digitised Land Records – Linking with bank accounts, Aadhaar UID and mobile numbers

The land record of the farmers should be digitised and linked to their bank account. The

claim amounts could be transferred to farmers' bank accounts linked with Aadhaar along with

their mobile numbers. This system can enable faster settlement of claims within two weeks of

crop damage due to certain reasons like hail where assessment is possible without CCEs.

Role of private players

The private sector has an important role to play in enlarging crop insurance programme in the

country. Private sector participation could lead to greater efficiency in the system through

faster settlement of claims and less distortion in allocation of government subsidy. As

envisaged in the operational guidelines companies could be allocated states/districts based on

tender proceedings for a period up to 3 years. It will induce competitiveness in this sector and

this could significantly lower the cost of providing insurance coverage to farmers.

Raising awareness and satisfaction among farmers

It is generally said that the farmers lack faith in the current insurance system. They have little

knowledge on sum insured, premium rates, etc. The time period taken for assessment of

claims make the product unattractive to them. There is need to create awareness among

farmers through government agencies, insurance companies and banks. Farmers should

receive an SMS as soon as they purchase the insurance product so that they are well informed

about compulsory deduction of premium, the amount of sum insured and procedure of claim

settlement. There is need to create excitement around the scheme as was done in the cases of

the PM Suraksha BimaYojana and PM Jan DhanYojana.

In sum, GoI has taken a bold step to revamp its crop insurance schemes and move towards

PMFBY. It is a step in the right direction, but for it to be beneficial to farmers and at low cost

to the government exchequer, it has to overcome several implementation glitches. It has to

build trust through greater transparency, by applying high technology, regularly sharing

relevant data on insurance portal, adhering to timelines for cut-off dates for registration as

well as conducting CCEs within stipulated time mentioned in operational guidelines, and

video recording those. Only then premiums will come down, and so will the subsidy burden

to government, and timely benefit of settling claims of farmers.

References

Agricultural Finance Corporation Ltd. (2011), "Report on Impact Evaluation of Pilot

Weather Based Crop Insurance Study"

Aijaz, R. (2013), "Monsoon Variability and Agricultural Drought Management in India",

Retrieved from http://orfonline.org/cms/export/orfonline/modules/issuebrief/attach

ments/Issuebrief-51_1370080087866.pdf (Last accessed on April 7, 2015)

Balzer., N., Hess, U. "Climate change and weather risk management: evidence from index-

based insurance schemes in China and Ethiopia", Retrieved from

http://home.wfp.org/stellent/groups/public/documents/newsroom/wfp225958.pdf

(Last accessed on April 5, 2017)

Belete. N., Mahul. O., (2007), " China: Innovations in Agricultural Insurance", The World

Bank, 68607 v1

Cognizant. (2014), " Drones: The Insurance Industry’s Next Game-Changer?", Retrieved

from http://www.cognizant.com/InsightsWhitepapers/drones-the-insurance-industry's-

next-game-changer-codex1019.pdf (Last accessed on May 20, 2017)

Singh, G. (2010), "Crop Insurance in India", IIM Ahmedabad, Working Paper. No. 2010-06-

Hazell, P., Pomareda, C., and Valdes, A. (1986), "Crop Insurance for Agricultural

Development", John Hopkins University Press, Baltimore, USA

Harms, S.C, "History of Crop Insurance in the United States", Retrieved from

http://www.rainhail.com/pdf_files/rainhailcom/uscihistory.pdf (Last accessed on May

14, 2015)

Jain, R.C.A. (11th Plan) "Report of the Working Group on Risk Management in

Agriculture", Planning Commission of India

Kabuta. F. (2014),"Agriculture Disaster Compensation System in Japan", FFTC Agriculture

Policy Platform, Food and Fertilizer Technology Center for the Asia and Pacific

Region (Last accessed on April 15, 2015)

Kerer, J. (2013), "Situation of Agricultural Insurance in Kenya with Reference to

International Best Practices", Adaptation to Climate Change and Insurance

Krychevska, L., Shynkarenko, I., Shynkarenko R., (2017), “Agricultural Insurance in

China: History, Development and Success Factors. www.agroinsurance.com

Mishra, P.K. (2014), "Report of the Committee to Review the Implementation of Crop

Insurance Schemes in India", Department of Agriculture & Cooperation, Ministry of

Agriculture

Munich Re, "Crop Insurance for the wealthy", Retrieved from http://www.munichre.com/site

(Last accessed on May 16, 2015)

Stutley, C. (2011), "Agriculture Insurance in Asia and the Pacific Region" Food and

Agriculture Organization

Swiss Re. (2009), "An Insurance Recipe for the Chinese Food and Agricultural Industry,

Technical publishing", Swiss Re

Wang, M., Shi, P., Ye, T., Liu, M ., and Zhou, M. (2011), " Agriculture Insurance in

China: History, Experience, and Lessons Learned", International Journal of Disaster

Risk Science, Vol. 2, No. 2

Xu, W., Odening, M., Ji, C. (2010), " Systemic Weather Risk and Crop Insurance: The Case

of China", SFB 649 Discussion Paper 2010-053

Websites Accessed

Pro Ag, http://www.proag.com/basics-of-crop-insurance [Accessed May 25, 2017]

Risk Management Agency, http://www.rma.usda.gov/policies/ [Accessed December 28,

2017]

AIR Worldwide (2011, October), http://www.air-worldwide.com/Press-Releases/AIR-

Worldwide-Launches-Multiple-Peril-Crop-Insurance-Model-for-China/ [Accessed

May 17, 2015]

US Farm Bill (2014), http://www.cropinsuranceinamerica.org/ [Accessed May 15, 2017]

MIT Technology Review http://www.technologyreview.com/featuredstory/

526491/agricultural-drones/ [Accessed May 20, 2017]

Investing In Low Earth Orbit Micro-Satellite Telemetry in China (2015, May)

http://www.china-briefing.com/news/ [Accessed May 20, 2017]

Planet Labs, https://www.planet.com/[Accessed May 20, 2017]

The Wall Street Journal (2014, Dec 10), http://blogs.wsj.com/cio/2014/12/10/drone-on-the-

farm/ [Accessed May 22, 2017]

Yamaha RMX, http://rmax.yamaha-motor.com.au/agricultural-use [Accessed June 2, 2017]

The Economic Times (2015, February 19) http://articles.economictimes.indiatimes.com/

2015-02-19/news/59305367_1_groundnut-crop-gujarat-farmers-agriculture-

insurance-company [Accessed June 3, 2017]

RIICE, (official website), http://www.riice.org/ [Accessed May 25, 2017]

Annexures

Annexure 1:

National Agriculture Insurance (NAIS)

NAIS was introduced in Rabi 1990-2000, leading to the discontinuation of CCIS. Like CCIS,

this was primarily based on area approach. It was available in all states and union territories

and covered all farmers and sharecroppers and tenant farmers growing notified crops in

notified areas. The crops covered in the scheme included food crops, oilseeds, annual

commercial/horticulture crops.

Area Approach and Level of Indemnity

The scheme was based on an area approach, i.e., a defined area

for each notified crop and

on an individual basis for localised calamities such as hailstorm, landslide, cyclone and flood.

Three levels of indemnity, viz. 90 per cent, 80 per cent and 60 per cent, corresponding to low,

medium and high-risk were available for all crops based on the coefficient of variation in

yield data over the previous 10 years.

Farmers Covered and Sum Insured

The scheme compulsorily covered loanee farmers, whereas it was voluntary for non-loanee

farmers. The minimum sum insured in case of loanee farmers was the amount of crop loan

availed from the bank. There was an option to extend sum insured up to the value of

threshold yield

of the crop insured. Where the value of the threshold yield was lower than

the loan amount per unit area, the higher of the two was the sum insured. If loanee farmers

wished to opt for a higher sum insured (upto 150% value of average yield in the notified area)

they had to pay premium at actuarial rate as notified by the State Government.

For non-loanee farmers, the sum insured was up to the value of threshold yield of the insured

crop. For sum insured up to 100 per cent of threshold/average yield of the notified area, the

farmers paid normal subsidised premium but sum insured above 100 per cent and up to 150

per cent of the value of average yield was without any premium subsidy from the Govt.

Premium Rate and Premium Subsidy

The premium rates for farmers were 3.5 per cent for oilseeds and bajra, 2.5 per cent for

cereals, millets and pulses during Kharif and 1.5 per cent for wheat, 2 per cent for other food

crops and oilseeds during Rabi. The actuarial rates applied for annual

commercial/horticulture crops. In case the actuarial rates were less than the premium rates,

then the former was taken into consideration for calculating the premium.

Defined area may be a gram panchayat, mandal, hobli, circle, phirka, block, taluka, etc. to be decided by the

state/UT government.

The value of threshold yield is based on moving average yield of the past three years in the case of rice and

wheat, and five years’ yield in case of other crops, multiplied by the level of indemnity.

Premium for small/marginal farmers were subsidised to the extent of 50 per cent, to be shared

equally between the centre and the states. The subsidy on premium was to be phased out over

a period of five years, which would be reduced by 10 per cent each year. However, 10 per

cent subsidy continued to be given till the end.

Sharing of Risk

The risk was shared by implementing agency

(IA) and the government in the following

proportion: The risk of IA in meeting claims was restricted to 100 per cent in case of food

and oil seeds. Thereafter, all normal claims up to 150 per cent of the premium were to be met

by government and claims beyond that were to be paid out of a corpus fund.

In case of

commercial/horticulture crops the IA will bear all normal losses, i.e., up to 150 per cent of the

premium and beyond this limit, claims will be paid out of the corpus fund.

Calculation of claims

An insured crop in a notified area that recorded actual yield lower than the threshold yield

(i.e., guaranteed yield), automatically became eligible for compensation or claims. All the

insured farmers growing that crop in the defined area were deemed to have suffered shortfall

in their yield. Indemnity was calculated using the formula:

(Shortfall in Yield/Threshold Yield)* Sum Insured for the farmer

Shortfall in Yield=Threshold Yield - Actual Yield for the defined area

In case of localised risk, where settlement of claims was based on individual basis,

compensation was calculated by the IA in co-ordination with state/UT government.

Due to the shortcomings of NAIS, the government decided to launch a new scheme called the

National Crop Insurance Programme (NCIP) in 2013, which consisted of three components.

(i) Modified National Agricultural Insurance Scheme (MNAIS)

(ii) Weather Based Crop Insurance Scheme (WBCIS) &

(iii) Coconut Palm Insurance Scheme (CPIS)

This scheme was to start with some improvements for full-fledged implementation from Rabi

2013-14 seasons. However, on representations from several states, NAIS was continued

during 2013-14 and 2014-15.

Agriculture Insurance Company of India (AIC) Ltd. was the Implementing Agency for NAIS.

To meet catastrophic losses, a corpus fund was created with contributions from the Government of India

and the states/UT on 50:50 basis managed by IA.

Modified National Agriculture Insurance Scheme (MNAIS)

The Government of India implemented MNAIS on a pilot basis in 50 districts from the Rabi

2010-11 season onwards, with some additional features that were lacking in NAIS. It

included a reduction of the insurance unit to the panchayat level, calculation of premium

based on actuarial rates, raising the minimum level of indemnity to 80 per cent from 60 per

cent, a more refined basis for calculation of threshold yield, etc. These improved features are

discussed in detail below. From Rabi 2013-14, MNAIS became a part of NCIP and NAIS

was to be discontinued but several state governments represented to GoI for continuation of

NAIS. The GoI allowed NAIS to continue in several states including Gujarat, MP, Odisha,

Chhatisgarh, Jharkhand, Maharashtra and TN. A few states, including UP and Rajasthan

however switched to MNAIS from 2013-14.

Insurance Unit

The insurance unit was village/village panchayat or any other equivalent unit for major crops,

and for other crops, it could be a unit between the village panchayat and taluka. The unit will

be decided by the state/UT government.

Level of Indemnity and Sum Insured

Two levels of indemnity, viz., 90 per cent and 80 per cent, were available for all crops. The

threshold limit in an insurance unit was the average of seven years [excluding a maximum of

two years in which a calamity, such as drought etc., was declared by the concerned authority

of the government].

For loanee farmers, the sum insured was equal to the amount of crop loan

sanctioned/advanced, which could be extended up to the value of threshold yield at the option

of the farmer. The value of sum insured was arrived at by multiplying the notional threshold

yield with the MSP. The farm gate price, established by the marketing department /board was

to be adopted for crops for which MSP was not declared.

Farmers who had voluntarily opted for crop insurance, the sum insured was up to the value of

threshold yield of the insured crop.

Premium Rates and Subsidy

Under MNAIS, the premium rates were determined on actuarial basis instead of the

administered rates in the earlier scheme. However, in order to save Govt expenditure on

subsidising the premium, the same were capped at 11 per cent and 9 per cent (of sum insured)

for food and oil seeds crops for Kharif and Rabi season respectively. And for annual

commercial/horticulture crops, it was capped at 13 per cent. In case of crops whose premium

were higher than the cap level, sum insured was reduced to cap level whereas the actuarial

rates continued to apply. The actuarial premium was subsidised by the government in the

following manner:

Premium Subsidy given by the Government under MNAIS

S No.

Premium Slab

Subsidy by Central and State Government on 50:50

basis and premium payable by farmers

Up to 2 %

Nil

>2% - 5%

40% subject to a minimum net premium of 2%

>5% - 10%

50% subject to a minimum net premium of 3%

>10% - 15%

60% subject to a minimum net premium of 5%

>15%

75% subject to a minimum net premium of 6%

The above table shows that premium subsidy up to 75% was available to all farmers. The

difference between the actuarial (gross) premium and the premium payable by the farmer, i.e.

premium subsidy, was shared equally between the Government of India and state

government.

The calculation of indemnity was to be done in the same manner as NAIS.

Apart from these features, on account settlement of claims up to 25 per cent of likely claims

was to be released in advance in case of adverse seasonal conditions. There was an option

now available for claims under prevented sowing/planting category if farmers in one

insurance unit are not in a position to either sow or transplant or grow a crop (failed at an

early stage). When this incidence is widespread, i.e., a major portion of the area in one

insurance unit remains unsown or sowing fails, (say, more than 75 per cent of normal area) or

as decided for various crops by SLCCCI at the time of notification, then the insurance

company based on weather/rainfall position in the insurance unit as issued by concerned

office of IMD during the season, and acreage sown details received from the state

government, shall decide the extent of claims to be paid. Post-harvest losses are available for

those crops that are allowed to dry in the field up to a maximum period of two weeks from

harvesting.

Reinsurance Cover

The implementing agency was required to make efforts to obtain appropriate reinsurance

cover in the national/international reinsurance market. The government provided protection

in case of failure to obtain reinsurance cover as well as in cases where the premium to claim

ratio exceeded 1:5. A ‘Catastrophic fund’ to which the centre and state governments would

contribute equally was to be created.

Weather Based Crop Insurance Scheme (WBCIS)

This scheme started its operations on a pilot basis in 2007-08 based on ''area approach''. It

aimed to protect farmers against crop losses on account of adverse weather conditions such as

deficit and excess rainfall, frost, heat, relative humidity, etc. Amongst the various weather

parameters like rain, temperature, wind, sunshine, etc., rainfall is the most important

parameter in the context of Indian agriculture, particularly in the Kharif season. This scheme

compensated anticipated loss in crop yield resulting from adverse rainfall incidence such as

deficit rainfall or excess rainfall.

Area Approach

For the purpose of compensation, a ‘reference unit area’ (RUA) is deemed to be a

homogenous unit of insurance. Each RUA is linked to a reference weather station (RWS), on

the basis of which current weather data and the claims are processed. Such RUA was

restricted to a 10-km area around the RWS in the case of rainfall and wind parameters, and

100 km in the case of other weather parameters such as frost, heat and relative humidity.

Claims arose due to certain deviation in weather conditions. When the 'actual temperature'

within the time period was more or less as compared to the specified “temperature trigger”,

then all insured farmers in the specified RUA were deemed to have suffered the loss. This

scheme was compulsory for loanee farmers and voluntary for non-loanee farmers.

Sum Insured

Sum Insured was broadly equivalent to the 'cost of cultivation' and pre-declared by the

insurers. Here, the sum insured for an individual cultivator would be the product of sum

insured per hectare and the area under cultivation as declared by the cultivator.

Premium Rate and Premium Subsidy

The premium rate was calculated on the basis of actuarial rates. However, premium rates

were capped at 10 per cent for Kharif and 8 per cent for Rabi food crops and oilseeds and 12

per cent on commercial/horticulture crops. In case of the crops whose premium was higher

than the cap level, their sum insured would be reduced to cap level where the actuarial rates

continued to apply.

The premium subsidy available to the farmers, shared equally between the central and state

government are as follows:

Premium Subsidy given by the Government under WBCIS

SL.

Premium Slab

Subsidy to Farmers

Up to 2%

No Subsidy

>2 – 5%

25%, subject to a minimum net premium of 2.00%

>5 – 8%

40%, subject to a minimum net premium of 3.75%

>8%

50%, subject to a minimum net premium of 4.80%

and maximum net premium of 6%

Payout of Claims

Payout would arise only in adverse weather incidents when there was deviation between

trigger weather

and actual weather data recorded at reference weather stations. It would be

responsibility of the insurance companies to make payments for losses arising out of adverse

weather conditions.

Trigger weather is a predefined weather parameter applicable to a notified crop in a notified reference unit

area.

Annexure 2:

State

No of Farmers Insured (in 000s)

Kharif

2012

Kharif

2013

Kharif

2014

Kharif

2015

Kharif

2016

Andaman &

Nicobar

0.6

1.0

0.7

0.6

0.0

Andhra Pradesh

2618.5

2228.0

261.0

1519.2

1618.5

Assam

34.8

34.1

24.7

31.5

51.7

Bihar

1615.8

1861.8

2298.3

1655.2

1485.4

Chhattisgarh

1182.2

650.6

974.2

1203.9

1399.2

Goa

0.3

0.2

0.1

0.7

Gujarat

1143.9

1005.1

658.9

502.2

1842.3

Haryana

108.1

122.2

0.0

738.8

Himachal Pradesh

17.7

11.8

17.4

28.8

134.6

Jammu & Kashmir

3.8

4.5

1.4

0.0

Jharkhand

497.8

334.5

193.9

536.1

828.4

Karnataka

859.6

600.5

1076.0

872.1

1725.7

Kerala

17.7

34.0

24.4

26.5

31.5

Madhya Pradesh

2032.5

2411.0

2522.5

2959.5

4083

Maharashtra

1334.5

1496.5

5770.2

8938.5

10997

Manipur

5.0

3.5

7.5

8.4

Meghalaya

1.6

2.3

1.2

0.5

0.1

Mizoram

0.0

Odisha

1477.7

1604.7

1800.9

2152.5

1766.4

Puducherry

1.8

0.5

0.3

0.4

0.0

Punjab

0.0

Rajasthan

6058.9

6570.3

5866.0

6409.8

6228.2

Sikkim

0.0

Tamil Nadu

269.3

213.4

44.8

137.7

15.9

Telengana

0.0

335.6

898.7

711.3

Tripura

0.9

0.0

0.9

1.9

Uttar Pradesh

814.5

1122.5

738.6

1688.7

3591.1

Uttarakhand

59.6

67.1

57.9

85.9

175.2

West Bengal

563.0

580.5

1081.8

1024.7

3056.7

Grand Total

20719.9

20962.2

23754.1

30681.5

40492.2

Annexure 3:

State

No of Farmers Insured (in 000s)

Rabi 2012-

Rabi 2013-

Rabi 2014-

Rabi 2015-

Rabi 2016-

Andaman &

Nicobar

Andhra Pradesh

406

326

194

182

153

Assam

Bihar

1637

2153

1579

1434

1228

Chhattisgarh

105

150

Goa

Gujarat

133

Haryana

113

141

597

Himachal

Pradesh

114

111

244

Jammu &

Kashmir

Jharkhand

Karnataka

128

326

1391

Kerala

Madhya Pradesh

2028

2442

2637

1966

2815

Maharashtra

1074

310

1249

3577

1014

Manipur

Meghalaya

Mizoram

Odisha

118

106

Puducherry

Punjab

Rajasthan

3979

4285

4021

4554

3056

Sikkim

Tamil Nadu

1005

594

663

937

1430

Telangana

710

376

265

Tripura

Uttar Pradesh

1123

762

1034

1917

2984

Uttarakhand

West Bengal

747

813

753

981

1078

Grand Total

12682

12273

13344

16720

16803

Annexure 4:

State

Area Insured (000 ha)

Kharif

2012

Kharif

2013

Kharif

2014

Kharif

2015

Kharif

2016

Andaman &

Nicobar

1.1

1.5

1.1

0.8

Andhra Pradesh

3694.4

2975.5

353.8

1982.9

1387.4

Assam

27.6

23.9

14.0

19.9

36.7

Bihar

1720.7

1975.1

2207.0

1504.1

1312.2

Chhattisgarh

2238.0

1209.9

1688.8

2160.6

2200.2

Goa

0.3

0.5

0.2

0.1

0.5

Gujarat

2472.9

2136.5

1384.3

1027.4

2566.7

Haryana

170.5

224.2

0.0

1188

Himachal Pradesh

15.9

9.1

13.0

13.6

39.5

Jammu & Kashmir

5.5

5.8

0.8

0.0

Jharkhand

433.7

362.8

187.2

373.6

352.7

Karnataka

1098.6

828.9

1387.8

1236.7

1400.2

Kerala

17.4

20.7

20.9

24.6

21.4

Madhya Pradesh

4706.8

5274.8

5504.5

6519.0

6434

Maharashtra

1053.7

1343.8

3954.3

5692.8

6579.3

Manipur

7.5

9.8

6.5

16.8

9.1

Meghalaya

1.3

2.1

0.7

0.3

0.02

Odisha

1304.5

1374.5

1566.8

1945.4

1257.9

Puducherry

2.2

0.5

0.3

Punjab

0.0

Rajasthan

8411.5

8082.2

7744.0

7299.9

7490.3

Tamil Nadu

300.1

231.1

48.7

142.8

30.6

Telangana

0.0

364.6

1062.8

594.7

Tripura

0.9

0.0

0.7

0.8

Uttar Pradesh

1056.7

1263.5

1032.7

1980.5

3158.9

Uttarakhand

32.4

39.4

34.6

59.3

101.1

West Bengal

283.4

282.8

667.2

445.8

1502.2

Grand Total

29057.8

27678.6

28183.7

33510.6

37828.2

Annexure 5:

State

Area Insured (000 ha)

Rabi 2012-

Rabi 2013-

Rabi 2014-

Rabi 2015-

Rabi 2016-

Andaman &

Nicobar

0.0

0.2

0.1

0.6

0.3

Andhra Pradesh

592.9

460.2

284.8

300.5

165.2

Assam

15.8

14.8

16.2

8.5

4.3

Bihar

1685.7

2005.5

1526.2

1297.1

1153

Chhattisgarh

194.6

169.2

205.9

181.9

232.1

Gujarat

71.2

61.7

4.4

4.1

274.7

Haryana

166.6

260.3

0.1

0.0

869.7

Himachal

Pradesh

604.9

15.9

1624.3

1636.5

89.8

Jammu &

Kashmir

10.3

0.0

Jharkhand

44.1

79.3

62.7

43.3

Karnataka

192.0

86.1

51.6

502.3

3148.9

Kerala

33.9

27.4

27.6

39.8

31.7

Madhya Pradesh

4373.9

5051.2

5298.2

3862.3

5137.2

Maharashtra

961.4

261.1

908.2

2608.5

715.8

Manipur

2.0

0.0

Meghalaya

1.0

0.8

0.7

0.01

Mizoram

0.1

0.0

Odisha

104.7

44.7

118.4

102.7

63.5

Puducherry

2.1

1.9

1.4

1.7

Punjab

0.0

Rajasthan

4125.5

4530.1

4268.2

4565.3

2702.8

Sikkim

0.0

0.1

0.2

Tamil Nadu

1130.2

708.5

820.0

1087.8

1306.4

Telengana

0.0

999.2

489.6

270.7

Tripura

0.1

0.0

0.4

0.3

5.8

Uttar Pradesh

1259.3

936.2

1022.5

1884.9

2496.8

Uttarakhand

102.6

8.6

16.7

425.6

31.2

West Bengal

323.4

340.2

329.5

427.5

532.4

Grand Total

15998.2

15065.5

17587.0

20371.3

19285.2

Annexure 6: Bid submission and actuarial premium rates in Kharif 2016

State/UT

Scheme

Date of Bid

Submission

Actuarial Premium Rates (%)

Andhra Pradesh

PMFBY

18.04.2016

8.8

WBCIS

17.05.2016

8.6

Assam

WBCIS

18.05.2016

3.4

Bihar

PMFBY

05.07.2016

17.9

Chhattisgarh

PMFBY

06.04.2016

4.1

Goa

PMFBY

10.06.2016

1.3

Gujarat

PMFBY

06.05.2016

11.07.2016

20.5

Haryana

PMFBY

08.06.2016

3.6

Himachal Pradesh

PMFBY

20.04.2016

1.2

WBCIS

20.04.2016

7.7

Jharkhand

PMFBY

04.04.2016

14.0

Karnataka

PMFBY

23.05.2016

14.2

WBCIS

23.05.2016

11.6

Kerala

WBCIS

08.06.2016

7.8

Madhya Pradesh

PMFBY

29.04.2016

14.6

WBCIS

28.05.2016

11.1

Maharashtra

PMFBY

08.06.2016

11.07.2016

18.7

WBCIS

08.06.2016

33.5

Manipur

PMFBY

01.07.2016

9.7

Odisha

PMFBY

10.06.2016

7.7

Rajasthan

PMFBY

08.07.2016

19.8

WBCIS

13.07.2016

Tamil Nadu

PMFBY

05.08.2016

4.7

Telangana

PMFBY

23.04.2016

5.5

WBCIS

23.04.2016

11.5

Tripura

PMFBY

16.05.2016

1.4

Uttar Pradesh

PMFBY

18.05.2016

4.7

WBCIS

18.05.2016

18.0

Uttarakhand

PMFBY

21.03.2016

1.1

WBCIS

26.04.2016

11.0

West Bengal

PMFBY

04.04.2016

3.3

WBCIS

02.05.2016

8.0

All India

PMFBY

12.5

WBCIS

12.5

Annexure 7: Term sheet of Onion (Kharif 2016) in Rajasthan

Total Maximum Payout shall not exceed Rs 1,29,000.

Annexure 8:

Yield Data

State Subsidy Status (in

Lakhs)

State

Season

Scheme

Company

Yield data

submission date as

per notification

Date of Yield

data Received

Data Received

/pending

If pending

mention

crops

Total

Pending

Paid

Estima

ted

Claims

(in

Lakhs)

Claims

Payable

(in

Lakhs)

Claims

Paid

(in

Lakhs)

Bihar

Kharif 16

PMFBY

AIC

28.02.2017

16.02.2017 &

28.02.2017

Received

12800

6400

6323

Bihar

Kharif 16

PMFBY

BAJAJ

30.11.2017

20.01.2017

Received

15530

16420

16419

Bihar

Kharif 16

PMFBY

CHOLA

31.01.2017

15.02.2017

Received

3872

1936

3164

Bihar

Kharif 16

PMFBY

SBI

22.05.2017

Received

10385

5192

5193

360

Bihar

Kharif 16

PMFBY

Tata

15.01.2016

15.02.2017 &

28.02.2017

Received

7006

3502

3503

2671

Bihar

Kharif 16

TOTAL

49593

24796

28938

28937

28727

Bihar

Rabi 16-17

PMFBY

NIC

Wheat ,Gram,

Mustard, Maize,

Potato- 31.05.2017

Sugarcane-

30.06.2018

Wheat , Gram ,

Mustard , Potato -

01.06.2017

Sugarcane &

Maize- 30.06.2017

soft copies of CCE

data is pending ,

CCE data from

GOI portal is not

accessible

5762

3779

Bihar

Rabi 16-17

PMFBY

UNITED

INDIA

30.05.2017 &

30.06.2017

5494

7475

Bihar

Rabi 16-17

TOTAL

11257

7475

Haryana

Kharif 16

PMFBY

BAJAJ

30.11.2016

11.03.2017

Received

4447

11485

10924

Haryana

Kharif 16

PMFBY

ICICI

Post harvesting, One

month

23.01.2017

1397

1333

5411

5253

Haryana

Kharif 16

PMFBY

Reliance

16.01.2017

Received

2475

6436

6536

Haryana

Kharif 16

TOTAL

8320

6821

23482

23280

23172

Haryana

Rabi 16-17

PMFBY

BAJAJ

15.04.2017

30.06.2017

Received

1263

1870

1528

Haryana

Rabi 16-17

PMFBY

ICICI

Within one month

from final harvest

Received

1622

614

Haryana

Rabi 16-17

PMFBY

Reliance

24.07.2017

Received

524

2245

Haryana

Rabi 16-17

TOTAL

1882

5778

3749

Assam

Kharif 16

RWBCIS

HDFC

319

Assam

Kharif 16

RWBCIS

Reliance

Received

183

Assam

Kharif 16

TOTAL

184

140

502

Assam

Rabi 16-17

PMFBY

NIC

Within one month

from final harvest

Not yet received

Pending for all

crops

Wheat,

Summer

Paddy,

Rape and

Mustard,

Potato,

Sugarcane

Assam

Rabi 16-17

TOTAL

Assam

184

139

502

Chhattisgarh

Kharif 16

PMFBY

IFFCO

Within one month

from final harvest

29.01.2017

Received

N/A

5288

5215

5951

5834

Chhattisgarh

Kharif 16

PMFBY

Reliance

23.01.2017

Received

1926

6820

Chhattisgarh

Kharif 16

TOTAL

7215

7140

12652

Chhattisgarh

Rabi 16-17

PMFBY

AIC

1. Lathyrus:-

20.04.2017 2. Wheat

Un-irrigated 3.

Linseed, 4. Rapeseed

& Mustard and 5.

Potato :- 30.04.2017,

6. Bengal Gram

(Chana):- 15.05.2017,

7. Wheat Irrigated:-

31.05.2017, 7. Wheat

Irrigated:- 31.05.2017

09.05.2017;

31.05.2017;

08.06.2017;

30.06.2017 &

05.07.2017

Incomplete.

Pending data

Lathyrus,

Wheat Un-

irrigated,

Linseed,

Rapeseed

& Mustard

and Potato,

Bengal

Gram

(Chana),

Wheat

Irrigated

948

664

536

Chhattisgarh

Rabi 16-17

PMFBY

BAJAJ

15.04.2017

25.05.2017

Partially Received

All Crops

1190

1583

1199

Chhattisgarh

Rabi 16-17

RWBCIS

BAJAJ

20.04.2017

30.06.2017

201

Chhattisgarh

Rabi 16-17

2342

2682

2640

TOTAL

Chhattisgarh

TOTAL

9555

9480

15454

15494

13295

Kerala

Kharif 16

RWBCIS

AIC

Risk period was upto

30.11.2016 for

Autumn Paddy crop,

upto 28.02.17 for

Ginger &Turmeric,

upto 31.03.17 for

Banana, upto

30.04.17 for Black

Pepper and 31.05.17

for remaining five

crops viz. Arecanut,

Cardamom,Pineapple,

Nutmeg & Sugarcane

Pending for Five

crops out of 10

crops notified

Arecanut,

Cardamom

,Pineapple,

Nutmeg &

Sugarcane

273

272

1795

1715

1702

Kerala

Kharif 16

TOTAL

273

272

1719

1715

1589

Kerala

Rabi 16-17

PMFBY

AIC

31.07.2017(Winter

Paddy),

15.09.2017(Summer-

Paddy),

31.12.2017(Banana,

Winter Tapioca),

30.04.2018(Plantain&

Summer-Tapioca)

Yield data for

Winter Paddy was

received from

DES on

27.04.2017.Howev

er RO has sought

certain

clarifications on

the data received

vide our letter

dated 8.5.17 which

is still awaited

Pending

Clarification for

Winter Paddy data

received on

31.05.2017 and

the claim for

Winter paddy

under

process.Yield data

as such Pending

for remaining 5

out of 6 crops

notified as the

COD is not yet

over

Banana &

Winter

Tapioca

for Rabi-I

season and

Summer

Paddy,Pla

ntain and

Summer

Tapioca

for Rabi-II

season

112

343

Kerala

Rabi 16-17

RWBCIS

UNITED

INDIA

913

1380

Kerala

Rabi 16-17

TOTAL

1024

343

Kerala

1297

1024

272

3519

2058

1702

Annexure 9:

Total Cost to the Government (including both State and Central) at various premium rates with 75 percent subsidy (Centre and state

share being 50:50)

Sum Insured

Actuarial Premium @ 12.0% (Rs.

Crore)

Actuarial Premium

@10.0% (Rs.crore)

Actuarial Premium @ 8.0%

(Rs.crore)

Rs.50,000/ha

45,000

37,500

30,000

Rs.40,000/ha

36,000

30,000

24,000

Cost to the Central Government at various premium rates with 75 percent subsidy

Sum Insured

Actuarial Premium @ 12.0% (Rs.

Crore)

Actuarial Premium @10.0%

(Rs.crore)

Actuarial Premium @ 8.0% (Rs.crore)

Rs.50,000/ha

22,500

18,750

15,000

Rs.40,000/ha

18,000

15,000

12,000

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About ICRIER

Established in August 1981, ICRIER is an autonomous, policy-oriented, not-for-profit,

economic policy think tank. ICRIER's main focus is to enhance the knowledge content of

policy making by undertaking analytical research that is targeted at informing India's policy

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ICRIER's Board of Governors includes leading academicians, policymakers, and

representatives from the private sector. Dr. Isher Ahluwalia is ICRIER's chairperson.

Dr. Rajat Kathuria is Director and Chief Executive.

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 Macroeconomic Management Financial Liberalisation and Regulation

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