Agriculture - Crop Insurance in India

Editorial Board Smt. Pournima Gupte, Member (Actuary), IRDAI Shri P.J. Joseph, Member(Non-Life), IRDAI Shri Nilesh Sathe, Member (Life), IRDAI Dr. T. Narasimha Rao, Managing Director IIRM Shri Sushobhan Sarker, Director, National Insurance Academy Shri P. Venugopal, Secretary General, Insurance Institute of India Shri V. Manickam, Secretary General, Life Insurance Council Shri R. Chandrasekaran, Secretary General, General Insurance Council Dr. Nupur Pavan Bang, Associate Director Indian School of Business Editor K.G.P.L. Rama Devi Published by Dr. Subhash C Khuntia of behalf of Insurance Regulatory and 2010 Insurance Regulatory and Development Development Authority of India Authority of India Printed at: NavaTelangana Printers Pvt. Ltd., Please reproduce with due permission # 21/1, M.H. Bhavan, Near RTC Kalyanamandapam, Azamabad Industrial Area, Musheerabad, Hyderabad The information and views published in this Ph: 91-40 27673787, 27665420 journal are those of the authors of the articles. Disclaimer: Due to administrative reasons, IRDAI could not roll out the previous edition of the Quarterly Journal.8102 enuJ-lirpA lanruoJ IADRI Publisher Page The central theme of the current issue of IRDAI quarterly Journal is Crop Insurance. The articles on crop insurance have been sourced from those having deep knowledge and expertise in the field. We believe that they will be found useful by not only those who underwrite the crop insurance business but also by the general readers.Weather variation and the associated uncertainty of crop yields has been a global phenomenon. Agricultural activity and the incomes therefrom are often influenced by vagaries of nature like droughts, floods, storms etc. These events being beyond the control of the farmers, often result in heavy losses in crop production and the farm incomes. The magnitude insurance schemes and on the very concept of of loss is also increasing due to the growing crop insurance is paramount to achieve this. commercialization of agriculture. As on today, This can be achieved only through a concerted agriculture engages about half of the total effort by all the stake holders of the crop workforce in the Indian economy and contributes insurance sector. Quick settlement of claims is about 17 % to the Gross Domestic Product (GDP). a very important element in encouraging the In such a scenario, the need and the importance spread of crop insurance. Assessment of claims of crop insurance cannot be over emphasized. at the right time and timely disbursement of Crop Insurance is a necessity for a majority of claims will not only help the farmers and their farmers but is faced by problems of design and families to overcome the challenges of economic finance. Moral hazard and adverse selection are distress but encourages other uninsured farmers more pronounced in Crop Insurance as compared to opt for crop insurance. IRDAI, as the to other lines of insurance business. regulator, would constantly endeavor to provide Several Crop Insurance Schemes have been a supportive regulatory environment for the designed and rolled out by the Government of development of this sector. Boosting the Crop India from time to time starting from the Insurance would not only develop the Comprehensive Crop Insurance Scheme (CCIS) agricultural sector but also the general of 1985 to the Pradhan Mantri Fasal Bima Yojana insurance sector. (PMFBY) of 2016. The approach towards these I am pleased that the articles published in this schemes has been one of continuous issue have covered various aspects of Crop improvement based on the recommendations of Insurance in India, discussing the problems and various committees appointed to study the prospects associated with the sector. This would shortcomings and the loopholes of these schemes. encourage further discussion on the issue and The efforts have resulted in a coverage of 30% of will provide inputs and potential solutions to the the gross cropped area during the year 2016-17. various problems and issues being faced However, we still have along way to go to increase currently. The next issue of the journal would the coverage of crop insurance and also the be on the theme of “Reinsurance” number of farmers insured. Improving the Dr. Subhash C Khuntia confidence of the farmers in the various crop Crop Insurance 18102 enuJ-lirpA lanruoJ IADRI Dr. Subhash C Khuntia Crop Insurance 28102 enuJ-lirpA lanruoJ IADRI Inside Inside ISSUE FOCUS 7 Towards improving crop yield estimation in the insurance units of Pradhan Mantri Fasal Bima Yojana - Dr. C.S. Murthy Crop Insurance & Technology 16 Intervention,(Odisha- experience) - Dr.Rajesh Das Agriculture/Crop Insurance 21 in India: Key issues and way forward - Azad Mishra Technology Interventions 24 In Crop Insurance - Ashok K Yadav and Nima W Megeji 28 Agricultural / Crop Insurance In India - Problems And Prospects - Dr. S. Pazhanivelan Pradhan Mantri Fasal BimaYojana 35 (PMFBY) – Issues inhibiting its big success and probable way forward - M K Poddar, A Closer Look at Agriculture 42 Insurance of India - Vivek Lalan, Crop Insurance 38102 enuJ-lirpA lanruoJ IADRI Crop Insurance 48102 enuJ-lirpA lanruoJ IADRI Given the importance of agriculture in India in the historical, economic and cultural context, the need for transferring the risks of farming through insurance,needs no emphasis. The current edition tries to bring out the varied facets of the Crop Insurance,as a long term risk management tool, and also discusses issues and the challenges associated therewith. “The biggest challenge in the crop insurance value chain is assessing crop yield in the insurance units for determining the indemnity payout. Therefore, the effectiveness and sustenance of the insurance scheme largely depends on the yield- loss assessment’’, argues Mr.C.S. Murthy’s team in their article ‘Towards improving crop yield estimation in the insurance of PMFBY’. The article also stresses upon the need for enhancement of transparency quotient in the Crop Cutting Experiment (CCE) processes, ensuring that crop yield estimates are done in an objective manner, minimizing the human induced biases, through use of satellite, mobile and GIS technologies. It also proposes to finally replace CCE in the long run with an alternative mechanism. Terming Indian agriculture as a “gamble in the monsoon”, Dr. Rajesh Das has analyzed the crop insurance experience of the State of Odisha in his article ‘Crop Insurance & Technology intervention. Odisha is one of those states having considerable exposure to drought and floods. After understanding the benefits of crop insurance and taking into consideration the importance of Crop Cutting Experiments(CCE) in settlement of claims, the State pioneered in the usage of technology by streamlining the CCE process across the State through digitalization of data using the mobile applications. The article illustrates how the coordinated efforts of district and state level officials along with other key stakeholders in monitoring the implementation and progress were the key to the success of the scheme. Delay in issuance of notification, lack of awareness about the benefits of insurance, enrolment process, non-existence of land ownership title documents for the tenant/share croppers; lack of adequate man-power for conducting large number of crop cutting experiments have been identified as some of the issues in the spreading of crop insurance by Mr. Azad Mishra in his article ‘Agriculture/Crop Insurance in India: Key issues and way forward’. The recommendations include involvement of all stakeholders for spreading awareness, as well as utilization of technology such as Digital India Land Record Modernization Programme (DILMRP), utilization of remote sensing and drone based technology for smart sampling for timely settlement of claims. In his article ‘A closer look at Agriculture Insurance of India’, Mr. VivekLalan touches upon the various obstacles hindering the smooth functioning of the crop insurance schemes in India. He stressed on the need to conduct large scale insurance awareness campaigns at the grass root level, to expand its outreach by linking of Aadhar number enabling Direct Benefit Transfers and use of technology for faster settlement of claims etc. Utilization of sophisticated technology including Satellite Imagery and Remote sensing based information for assessment of crop yields/ losses is discussed in the article ‘Technology interventions in crop insurance’ by Mr. Ashok K Yadav. Mr. M K Poddar, in his article presents the various operational issues plaguing the Pradhan Mantri Fasal Bima Yojana (PMFBY), from achieving landslide success. Some of the issues identified are skewed distribution of the risk, perceiving the payment of subsidy as financial burden bysome States, poor quality of CCE data etc. He also suggests a few measures that could make the Scheme sustainable and argues that like in many developed and developing countries, a comprehensive legislation on Agricultural Insurance should be put in place. ‘Remote Sensing Applications in Crop Insurance being a success story from Tamil Nadu using Tamil Nadu Agricultural University-Remote sensing-based Information and Insurance for Crops in Emerging economies (TNAU-RIICE) technology’ was presented by Dr.S. Pazhanivelan. The article also shows how remote sensing could be used to assess the impact of floods and droughts on crop conditions along with yield loss assessment. The amount of insured losses from each major natural catastrophe – be it floods or localized calamities have been rising progressively. Reinsurance is an extension of the basic, fundamental concept of pooling and is an integralpart of the entire insurance business cycle. The focus of the next issue will be on ‘Reinsurance’. -K.G.P.L. Rama Devi Crop Insurance 58102 enuJ-lirpA lanruoJ IADRI BEWARE!! IRDAI does not sell Insurance The public are hereby cautioned regarding the following: Some of you must be receiving phone calls from persons claiming to be employees of Insurance Regulatory and Development Authority of India (IRDAI) and trying to sell insurance policies or offering some ‘benefits’. Please note that IRDAI does not sell or promote any company’s insurance product or offer any ‘benefit’. IRDAI regulates the activities of insurance companies to protect the interests of the general public and insurance policyholders. Report to the nearest police station and file FIR if: Any person approaches you claiming to be IRDAI employee for sale of insurance products or offering any ‘benefit’, Any unlicensed intermediaries or unregistered insurers try to sell insurance products. Crop Insurance 68102 enuJ-lirpA lanruoJ IADRI Issue Focus Towards improving crop yield estimation in the insurance units of Pradhan Mantri Fasal Bima Yojana Dr. C.S. Murthy is Head, Agricultural Sciences and Applications, Remote Sensing Applications Area, National Remote Sensing Centre (NRSC-ISRO), Hyderabad. 1. Introduction the crop season. Use of development and implement- technologies viz. remote ation of Mobile technology for India has a long history in the sensing, mobile and data field data collection for design, development and analytics is being increasingly improving crop yield implementation of various attempted for effective estimation and crop loss crop insurance schemes with implementation of the assessment, (c) training to successive improvements scheme in the last two years. the field level personnel of from time to time. The idea is State Departments on mobile to insulate the farming National Remote Sensing based field data collection, (d) community against various Centre (ISRO) has taken collaborative studies with cultivation risks. Government several initiatives in recent Agricultural Insurance of India introduced traditional years to demonstrate the Company of India Limited crop insurance in the year technology capabilities to (AICIL) to improve crop 1972 on a limited scale, meet the information insurance with remote followed by national level requirements of crop sensing and GIS technologies, large scale introduction insurance. These initiatives (e) awareness-cum-training of the Comprehensive Crop include (a) pilot studies in to the industry on technology Insurance Scheme in 1985, different districts, (b) utilisation, (f) development of National Agricultural Crop a Decision Support System for Insurance Scheme in 1999, India has a long crop insurance for Odisha Pilot Weather Based Crop history in the design, state and (g) conducting Insurance Schemes in 2007 development and special studies to support the and Pilot Modified NAIS in implementation of States. 2010. However, implementa- various crop tion of PMFBY from kharif insurance schemes The biggest challenge in the 2016, is a revolutionary step with successive crop insurance value chain is towards improving agri- improvements from assessing crop yield in the culture insurance system in time to time. The idea insurance units for the country. PMFBY, is to insulate the determining the indemnity primarily an area-yield farming community payout. The effectiveness and insurance contract, has against various sustenance of area-yield many positive features to cultivation risks. insurance scheme is therefore compensate for multiple risks largely dependent on w during the entire life cycle of the objective yield-loss Crop Insurance 78102 enuJ-lirpA lanruoJ IADRI assessment mechanism using the lower side for most of the In India, crop yield reliable, current and historical time, as observed from estimation in the crop yield data, which posed various reports, news items insurance units is done a serious challenge and views of different by conducting Crop stake holders. Such under- Cutting Experiments In India, crop yield estimation (CCE) in the field-plots estimation of crop yields in the insurance units is done selected through a in the insurance units, has by conducting Crop Cutting sampling scheme. cascading effect on the entire Experiments (CCE) in the Subjectivity in the yield system of insurance. Reduced measurements has field-plots selected through a yields attract higher payouts, become a major concern sampling scheme. Subjectivity reflecting higher risk and and it is widely agreed in the yield measurements that the quality of crop higher cost of insurance has become a major concern yield data needs to be (premium rate) in subsequent and it is widely agreed that the improved drastically to years. Another impact of the quality of crop yield data enhance the strength of biased data is that it reduces the crop insurance needs to be improved the threshold / guaranteed contracts for their drastically to enhance the yield of the crop for an sustenance. strength of the crop insurance insurance unit which is based contracts for their sustenance. w on the average of preceeding Technology interventions 3. Strategies for 5-7 years yield in the such as use of satellite data improving crop yield insurance unit. to improve crop yield estimation estimation is largely re- Therefore, the probability of commended and hence experiencing less than the Three broad strategies for attempts are being made to threshold yield (which is improving the crop yield adopt the same since the start already on lower side due to estimation in the insurance of PMFBY in kharif 2016. past series of biased data) units include; (1) measures to This paper examines various gets minimised gradually over enhance transparency and loopholes in the current a period of time. As a result, objectivity in the CCE process, mechanism of yield esti- the insured farmers (2) implement smart mation through CCE and would be either uninde- sampling on the basis of yield suggests the strategies minified or partially proxies to improve the for enhancing technology indemnified despite facing sampling design in terms of utilisation to address both crop losses. Consequently, the reduced sample size and human induced and crop insurance contract will logical distribution of the methodological shortcomings become a financial risk sample plots and (3) replace to improve the yield data. enhancing instrument the CCE with alternate mechanism. The main focus of rather than risk reducing Implications of inaccurate and this paper is on the measures instrument, as the farmers biased yield data on the for immediate implementa- may endup paying the insurance mechanism are first tion and these are mostly premiums without getting the discussed followed by related to the first and second compensation for crop loss in different strategies for strategies mentioned above. return. Thus, biased yield improving the yield The third strategy is the estimation in the insurance assessment. outlook for medium to long units leads to disastrous and terms and not much 2. Implications of biased cascading effect on the crop emphasised here. yield data insurance mechanism in the short run as well as in the long Human induced prejudices/ Bias in the crop yield data of run. choices and methodological different insurance units is on Crop Insurance 88102 enuJ-lirpA lanruoJ IADRI area may be distributed of wheat crop. Human induced under irrigated conditions, Crop mapping was done using prejudices/ choices and rainfed conditions, semi-dry multitemporal data and m e t h o d o l o g i c a l conditions, fertile areas, less decision rules approach. On limitations together fertile areas etc. Further, in the basis of sowing time, three impact the quality of the event of risk occurrence, types of wheat namely – early yield data in the current part of the insurance unit only sown, normal sown and late may be affected. Thus, system of CCE. By sown could be delineated spatial variability of crop infusing technologies using satellite data. Early performance and spatial such as remote sensing, wheat and late wheat variability in the occurrence of mobile, GIS and data produces less yield compared different risks – floods, analytics the effect of to normal class as observed drought, pest, diseases etc, from the field data and these limiting factors within the insurance unit interactions with farmers. can be minimised. would seriously distract the Early wheat completes w homogeneity assumption. flowering before the close of Therefore, random selection winter, where as late wheat limitations together impact of four CCE plots would crop commences flowering in the quality of yield data in the tend to result in skewed the high temperature period. current system of CCE. By representation of field These could be the reasons for infusing technologies such as conditions leading to biased yield reduction in these two remote sensing, mobile, GIS estimate of the average yield. classes. The number of and data analytics the effect Currently, random number of irrigations ranges from 2-6 of these limiting factors can be fields for locating CCE plots based on water availability. minimised. are being identified in the Insurance unit level wheat 3.1 Selection of CCE plots beginning of the crop season. yield variability and its It means, the crop risks that Generally speaking, four CCE association with satellite may occur during the course plots in each insurance unit for indices are also analysed. For of crop season are not duly a given crop and season are this purpose CCE were recognised and to this extent considered for yield conducted in some of the the sampling is non- measurment and average villages based on the sampling representative of the ground yield estimation. These four scheme using satellite data. It level situation. Therefore, plots are identified in the is observed that wheat yield selection of CCE plots should randomly selected fields. The variability within the be guided by yield affecting/ underlying assumption is that insurance units is higher and indicating factors to ensure the insurance unit is hence estimating average optimal spread of these plots. homogeneous with respect to yield using four CCE plots in crop performance and hence In order to overcome the each village (insurance unit) the average yield of any four above stated sampling issues may not produce the plots represents the and towards improving the representative yield for the insurance unit’s average. The distribution of CCE plots, the unit. validity of this assumption is scope for using moderate Satellite derived wheat NDVI the key to the success of this resolution satellite data has profiles, wheat crop map and randomisation process. been investigated in detail for NDVI based crop condition Unless and otherwise, it is wheat crop in Ujjain district. establsished with real data, it Sentinel data of 10m spatial zones are shown in Figs. 1-3. remains as a theoretical reolution and 5-6 days repeat Index derived from temporal assumption which may not has been procured for NDVI i.e., Season’s Max. match with ground situation. analysis. There are 21 NDVI has shown high number of satellite images correlation with wheat yield In an insurance unit, the crop covering complete phenology as shown in Table1. Crop Insurance 98102 enuJ-lirpA lanruoJ IADRI Fig.1 NDVI profiles of CCE plots using 21 Sentinel-2 temporal Scenes Fig.2 Satellite derived wheat crop map Fig.3. Wheat crop condition variability within village, Ujjain district, Rabi 2017-18 the village, Ujjain district, Rabi 2017-18 S. No. Halka Correlation between (Insurance Unit) wheat yield and NDVI 1 Ajawada 0.78 2 Bichrod Istamurar 0.71 3 Mungawada 0.83 4 Ninora 0.81 5 Rudaheda 0.82 6 JawasiyaSolanki 0.88 7 Jhalara 0.79 Table 1: Correlation coefficient between Wheat yield and Season Maximum NDVI at Insurance units of Ujjain district (2017-18) Crop Insurance 108102 enuJ-lirpA lanruoJ IADRI 3.2 Notification of fields CCE data is linked to map base The CCE plots are for CCE plots and satellite data for GIS identified with the help analysis, it is evident that The survey numbers of the of survey numbers of the many of the CCE plots are fields selected for CCE are corresponding fields. wrongly located in non- communicated to the field The location of these agriculture areas, neigh- functionaries in the first one fields is not represented bouring villages etc. In some or two months of the crop in any digital map base cases, there are more than 10 season. This information or by coordinates. As a CCE located in a Gram eventually reaches the result, there is scope for Panchayat, as a result of farmers of the village and replacing the actual CCE wrongly recorded latitude creates opportunities for field with nearby or a and longitude. This is typically moral hazard activities in the convenient field in the a problem of data collection. CCE fields. There are some same village. The field level person using incidents in recent years w the Mobile app, has to wait for where there was deliberate a few minutes, for getting the mismanagement of crop in the that, only when the field best lattitude/longitude by fields notified for CCE. person reaches close to the using the signals of more This is typically a governance selected survey number, number of GPS satellites. related issue and can be within the predefined buffer Therefore, till the location addressed through manage- zone of about 10m radius, the error becomes less than 10- ment interventions. data fields of the app are 15m, the App should not 3.3 Locating CCE fields activated enabling the data enable the data fields for on the ground entry. Thus, by using map inputing the information. If base and by modifying the the mobile app based CCE The CCE plots are identified mobile app, the identification data is within the acceptable with the help of survey of CCE plots on the ground location error limits, such numbers of the corresponding becomes foolproof. data is useful for further fields. The location of these analysis such as linking with fields is not represented in any 3.4 Recording with satellite data, weather data for digital map base or by Mobile App the purpose of analytics and coordinates. As a result, there value addition. Mobile Applications are being is scope for replacing the used extensively by many of actual CCE field with nearby 3.5 Post CCE verification the states for recording CCE or a convenient field in the of yield data yield data, from 2017 kharif same village. Identification of season.Thus the intention to Insurance unit average yields random numbers and field establish transperancy in the computed from the CCE data plots should go completely in process of CCE data collection are quite often disputed by digital mode with map is made clear. stake holders. In many cases outputs using the digital these data sets are suspected cadastral layer of the village. The element of concern in this to be biased thereby causing Latitude and longitude details process is the location errors abnormal delay in the decision of the selected fields along of CCE plots measured making on claims settlement. with survey numbers are to through GPS system of the Technologies play an be advised to the field Mobile. Location errors of the important role in the personnel. Mobile App may CCE plots are ranging from 10 verification of yield data. be modified in such a way mt. to 1000 mt. When the Satellite derived crop Crop Insurance 118102 enuJ-lirpA lanruoJ IADRI condition indices are available is justified or not. There is sets, shape files of in 10-60 metres spatial scope for developing semi- administrative units – resolution once in five days. automated procedures for villages, Gram Panchayats, These indices are useful to quick verification of yield insurance units etc. It has detect the crop condition data. Localised risks and the been observed that in many anomalies in insurance units risks that happen just before cases, all the insurance units to corroborate with estimated harvest may go un-noticed in (villages) could not be yield. By comparing the yield such verification process, identified in the available data and crop condition data which needs to be shape files. A significant of the current year with supplemented with ground number of Gram Panchayats previous normal years, one truth information. (insurance units) in Odisha, can get an idea remain unidentified in the whether the crop yield shape file. Similarly, about reduction in the 25% of villages could not current year, if be located in the shape reported, is justified files of some of the districts or not. For example, in Maharashtra state. the insurance unit (Gram Panchayat) Therefore, the most level average yields of important and immediate paddy and season’s requirement for techno- maximum NDVI of logy application in crop AWiFS sensor for all insurance is availability of the units are plotted in uniform and standard Fig. 4. Season’s max shape files of villages/ Fig.4 Paddy yield versus NDVI of paddy is associated blocks/districts. Without AWiFS NDVI among the with paddy yield showing identifying all the insurance insurance units (GPs), positive correlation. This units in map base, kharif 2016-17, Odisha maximum NDVI corresponds undertaking any scientific state (Data source: to heading/flowering phase of analysis in respect of yield Department of paddy crop. This association verification, smart sampling Agriculture and Farmers between NDVI and yield of etc.is not possible Empowerment, paddy may be exploited to correct the wrongly reported Government of Odisha) 3.7 Trained man power CCE data, when there are no for conducting CCE abnormal weather conditions The number of CCEs required 3.6 Digital data or pest resurgence in the post to support PMFBY is very availability heading phase of crop. huge, accounting to about 35- Similarly, weather data sets of Adoption of remote sensing, 40 lakhs per year. Conducting different years can be mobile apps and GPS and CCE in such a large number compared. Using multiple implementation of the needs logistic support, trained parameters – satellite techniques of spatial analysis personnel and budget derived NDVI, NDWI/LSWI, for improving crop insurance support. To generate quality rainfall, rainy days, dry spells needs GIS data base which yield data, CCEs need to be etc, decision rules can be consists of satellite images, conducted in a systematic developed to infer whether mobile app collected CCE data way and hence it requires the reported yield reduction and field data, weather data trained manpower. The Crop Insurance 128102 enuJ-lirpA lanruoJ IADRI persons having some Sampling scheme is applied at knowledge on field data Yield estimation in aggregated level say district collection in agriculture are the insurance units or taluk level, and the suitable for this purpose. Lack based on smart estimates are generated at of trained man power is one sampling data disaggregated level. Smart of the most critical involves empirical sampling will be efficient if a impediments faced by many procedures and strong yield proxy is states and insurance hence prone to developed preferably at a companies. Coordination errors. Therefore, later part of the crop growing quantifi-cation of between the agencies season capturing the most error and assigning involved such as department systemic and idiosyncratic error limits to the of agriculture, revenue and risks that the crop has faced final estimate need statistics is an important and using the same in due diligence. requirement for successful sampling design. Yield proxy w completion of the CCE. State is useful to arrive at Agriculture Universities with homogeneous zones and to their network of research Another approach reported is locate the fields for CCE. stations may be roped in to giving weightages to CCE Considering the limitations of the CCE task, to overcome the yield, rainfall and NDVI. the satellite based indices and man power shortage on one Arriving at optimal weights weather datasets and other hand and to avail their for different crops and data, it is desirable to develop expertise for supervision and locations is a challenge. To a blended index as yield quality improvement on the sum up, correcting the biased proxy. other. yield data with empirical or Yield estimation in the semi empirical or rule based 3.8 Correction factor for insurance units is based on procedures is still an the biased yield data smart sampling data involves unaddressed problem. empirical procedures and Development of a correction Machine learning algorithms hence is prone to errors. factor for biased yield data is may be promising for Therefore, quantification of a real challenge and needs to developing such correction error and assigning error be addressed. Some of the factors. This is an important limits to the final estimate studies have reported R&D element in crop needs due diligence. Another regression approach – insurance and there is a lot of important point of attention between yield and NDVI, scope for initiating pilot while adopting smart between yield and rainfall etc studies. sampling is that it is more for correcting the yield data. 3.9 Smart sampling for likely that in some of the These empirical methods may insurance units there will not reducing the number of not produce consistent results CCE be any CCE plots and hence from place to place and time no yield measurement. to time. Uncertainty is high Smart sampling or intelligent Therefore, the average yield and may not be good for sampling aims at two benefits data of insurance units that operational use. By forcing (a) reducing the number of result from smart sampling the data through regression CCE plots and (b) improving techniques is ‘estimate’ and techniques, another form of the distribution of CCE plots, not ‘measured’. subjectivity would be without compromising the introduced in the yield data. error limits of final estimates. Crop Insurance 138102 enuJ-lirpA lanruoJ IADRI In the event of implementing physical variables derived agricultural production. the smart sampling techniqes from satellite data to develop Quantifying the frequent and in the near future i.e., next 1- index based crop insurance localized phenomena that 2 years, the compatibility schemes. Local weather affect the crop production is a between the smart sampling conditions, crop management main challenge in the area- derived yield estimates for practices, soil, variety/ yield crop insurance. Machine the implementing year and hybrid, water related learning algorithms may be the corresponding yield parameters, etc. are promising to develop yield derived from the CCE based important yield determinants estimation techniques. Thus, measured yields of previous but their effect is not alternative methods for crop years will pose a problem completely manifest in any yield estimation are still in which may be overcome to single index. Therefore, semi development phase and some extent by empirically empirical techniques are hence replacement of CCE transforming the CCE based being developed involving with other mechanism is yet yields of historic years by spectral indices, weather data to be realised. using the concurrent datasets and local crop growing 4. Conclusion of one year. conditions. Adopting crop simulation models call for Crop yield data is the most 3.10 Dispensing with CCE very intensive field crucial data for the area-yield system data on different variables, insurance contracts. Crop calibrations etc limiting its Considering the complexities yield estimation in insurance scalability. associated with the current units continues to be the mechanism of CCE as subject of greater concern Technology based innovations mentioned in the above with ever increasing disputes need to be blended with local sections, the most preferred on the quality of yield data. contexts, i.e., local crop choice is to replace the system growing conditions such as Although, technology infusion with alternative mechanism cultivation practices, soil, to improve yield measure- that is less prone to errors. weather elements etc. that ment has been started, by Development of an frequently influence the way of using satellite images alternative scientific method and mobiles, since the launch of yield estimate in the of PMFBY in kharif 2016, insurance units is the biggest Considering the there are still several factors research challenge. Generally, complexities associated plaguing the quality of yield crop yield estimation methods with the current data. Loopholes or short are of three categories – mechanism of CCE as comings in the current empirical, semi-empirical and mentioned in the above system of crop yield simulation models. Remote estimation in the insurance sensing derived NDVI which sections, the most units and the means to represents crop vigour has preferred choice is to improve the system through been correlated with yield to replace the system with technology interventions in a investigate the possibility of alternative mechanism more strategic way are developing crop yield index that is less prone to highlighted in this paper. for crop insurance. errors These interventions would Considering the limitations of w certainly fix the methodology NDVI, some studies have related factors and also recommended the use of bio- Crop Insurance 148102 enuJ-lirpA lanruoJ IADRI minimise the human induced Ibarra, H., and Skees J., Feasibility, scalability and biases in order to make the 2007, Innovation in risk sustainability. Rep. to the yield measurements more transfer for natural hazards Gates Foundation, 40 pp. objective. Biased yield data impacting agriculture, [Available online at leads to disastrous and Environmental Hazards 7, cascading effect on the crop 62–69 http://agecon.ucdavis.edu/ insurance mechanism in the research/seminars/files/ Leblois, A., and Quirion, P., short run as well as in the long vsmithindex- insurance.pdf. 2013, Agricultural insurances run. Technology interven- based on meteorological Turvey, C.G., and Mclaurin tions should be undertaken in indices: Realizations, methods M.K., 2012, Applicability of a big way across the nation to and research challenges. the Normalized Difference overcome this menace of yield Meteor. Appl., 20: 1–9, Vegetation Index (NDVI) in data quality and to sustain doi:10.1002/met.303. Index-Based Crop Insurance the crop insurance system Design. Weather, Climate and with wider acceptability.. Mishra, P.K. 1996, Society 4, 271-284, DOI: Agricultural Risk, Insurance References 10.1175/WCAS-D-11-00059. and Income.Arabury, Anonymous, 2014, Report of Vermont: Ashgate Publishing Views expressed in this the Committee to review the Company. paper are author’s implementation of crop personal only and not of Smith, V., and M. Watts, insurance schemes in India, the affiliating 2009, Index based Department of Agriculture organisations agricultural insurance in and Cooperation, Govern- developing countries: ment of India, available at www.agricoop.nic.in. Crop Insurance 158102 enuJ-lirpA lanruoJ IADRI Crop Insurance & Technology Intervention,(Odisha-experience) Dr.Rajesh Das Nodal Officer (PMFBY) Directorate of Agriculture Government of Odisha When I entered the College of 50% of cultivated area and use of fertilizers & pesticides Agriculture for a Bachelor Government is seriously coupled with climate change course in Agriculture in early attempting to bring more area made the emergence of new eighties, the Agronomy under irrigation, proper pests affecting agricultural teacher welcomed us with the availability of water for production. sentence “Indian Agriculture irrigation is still a question. In The long exposure to coast is a gamble in the Monsoon”. fact water availability in line (about 480 Km) makes Even after 35 years, I still feel irrigated commands is solely Odisha more prone to cyclone that in spite of all our scientific dependent on distribution and floods. An analysis of developments in the field of and quantum of rainfall during occurrence of drought and agriculture, we still are rainy season. Without having flood in past 50 years reveal grappling with various a proper ground water that in 42 years, the uncertainties and the recharge plan, the unjudicious agricultural production in the sentence has not lost its use of ground water may State has been affected by relevance. In those days, the further complicate the matter either drought or flood and best way to make agriculture in future. The indiscriminate even both in the same year secure was to bring the (Table-I).In this back drop, cultivated area under the need for providing a irrigation, use more fertilizers, The long exposure to protective cover to farmers better varieties of seeds and coast line (about 480 through “Crop Insurance” has prophylactic sprays to Km) makes Odisha more become a pressing necessity safeguard against imminent prone to cyclone and than ever before. pest attack. Odisha as a state floods. An analysis of that had not harnessed much occurrence of drought The history of Crop Insurance benefit out of the first “Green and flood in past 50 years in Odisha dates back to 1999 Revolution” continued with reveal that in 42 years, when the National these strategies. the agricultural produ- Agricultural Insurance ction in the State has Scheme (NAIS) was The climate change has made been affected by either introduced as a flagship the arrival of monsoon, drought or flood and programme. The State distribution of rain and even both in the same successfully implemented the departure uncertain. year scheme till Rabi 2015-16. In Although irrigation potential w the interim period schemes has already been created for like MNAIS, WBCIS, NCIP Crop Insurance 168102 enuJ-lirpA lanruoJ IADRI etc, were implemented on always been minimal. (Table- pilot basis. II). It was then decided that the CCE process In 2011 a major intervention The turn around to the shall be digitized and in the NAIS scheme was Insurance programme came all pre-selected CCE made by lowering down the in the year 2015-16 (Scheme points will be geo- Insurance unit of Paddy to NAIS) when the estimated tagged. The Gram Panchayat Level from claim level for Kharif ’15 experiences of Block level. It is pertinent to reached about 2000 crore. capturing CCE data mention here that paddy is The state never had that kind using Mobile App the major crop of the state of claim payment history. under “FASAL” project and accounts for about 95% of This was an eye opener for all of Mahalnobis the insurance. Thus the State at the administrative level National Crop was able to extend the benefit and a serious relook was given Forecast Centre of the programme to a large to Crop Cutting Experiment (MNCFC) came in chunk of farmers. (CCE) process, the main way really handy. of claim assessment. It is revealed from the NAIS w implementation data that in It was then decided with properly calibrated tools, Kharif season about 16-18 that the CCE process shall be precautions and protocols. lakh farmers were covered digitized and all pre-selected under the programme and CCE points will be geo-tagged. While all these arrangements similarly during Rabi season The experiences of capturing were being made, the about 60,000—80,000 CCE data using Mobile App “Pradhan Mantri Fasal Bima farmers were covered. The under “FASAL” project of Yojana (PMFBY)” was average areas covered for Mahalnobis National Crop launched. The mainstay of the Kharif & Rabi season were 13 Forecast Centre (MNCFC) scheme is “Use of lakh ha and 0.75 lakh ha came in really handy. The Technology” and this boosted respectively .The Rabi District level officials were the State’s initiative to coverage under insurance has identified as “Master stream line the CCE process. Trainers” to train the As a first step in this regard, The turn around to the Primary Workers regarding all Primary Workers were Insurance programme capturing of CCE data provided with an incentive of came in the year 2015-16 through mobile App using Rs. 2500/- for downloading (Scheme NAIS) when the Smart Phone. All the primary the “CCE Agri-App” and estimated claim level for Kharif ’15 reached about workers were provided with registering in portal with a 2000 crore. The state a complete set of CCE kit condition that they will be never had that kind of comprising of weighing capturing and uploading CCE- claim payment history. balance, measuring tape, iron data for three years. This was an eye opener pegs, rope, cloth bag, Additional Incentive of Rs. for all at the administrative level and tarpaulin, cap etc. It is 100/- per CCE was provided a serious relook was pertinent to mention that for capturing & uploading the given to Crop Cutting here the conduct of crop CCE data. Training Camps Experiment (CCE) cutting is treated as an were organized for “Primary process, the main way of experiment and for an Workers” as well as “District claim assessment. experiment to yield desired level Approvers”. In fact the w results, it has to be performed concept of “District Level Crop Insurance 178102 enuJ-lirpA lanruoJ IADRI holders and also District has also been decided to go for Magistrates was also created smart sampling techniques Specific “Mobile App” for sharing of ideas and based on crop phonological are being developed for monitoring the programme. parameter for selection of loss assessment in case Two new collaborative ideal plots for conduct of CCE of Localized Calamity & projects “Crop Insurance and use of satellite imageries Post Harvest Losses. Decision Support System (coupled with ground Efforts are also being (Technical Partner-NRSC, trothing) to assess sown area made to notify more Hyderabad)” and Science under various crops in an crops under the Based Crop Insurance Insurance Unit. The state is programme. A major (Technical partner- also contemplating to learning from the International Rice Research implement a novel concept programme Institute,Manila,Phillipines)” “Picture Based Insurance” on implementation is that technological were launched for a pilot basis starting from intervention is the only augmenting PMFBY Kharif ’18. way for taking the implementation. Besides the techno- scheme further. Outcomes- This changed logical innovations and w the entire scenario of interventions, the State programme implementation. Government has formulated Approvers” as a check & Odisha became the pioneer a scheme for systematic balance measure in CCE data state in the country with publicity campaign especially approval process was regards to use of technology to bring in more non-loanee introduced at the behest of in Crop Insurance. This farmers into the ambit of the Odisha. helped in quicker claim crop insurance and capacity For effective programme settlement (by end of June ’17 building of the State officials execution “What’s App” i.e one of the earliest in the in loss assessment in case of groups were created in each country) bringing in various risk scenarios. district through which CCE transparency to the CCE- Specific “Mobile App” are schedules were shared. process-the core area of being developed for loss Guidelines for multi level controversy and instilling assessment in case of physical CCE verification was confidence among the Localized Calamity & Post formulated and district empanelled insurance Harvest Losses. Efforts are administration was instructed companies. As a result, while also being made to notify to scrupulously monitor the the actuarial premium rates more crops under the process and progress. An were going high in other programme. A major learning Officer in the rank of Addl. states, Odisha got much from the programme Dist. Magistrate was declared better rates for Kharif ’17 & implementation is that as “Nodal Officer” to Rabi 2017-18. The experience technological intervention is coordinate the CCE process. of four seasons are presented the only way for taking the Periodic video-conferences below in Table-3. scheme further. between State and District The journey, did not This way it is expected officials were held to keep a end there. The CCE results of that coordinated effort and tab on the progress of conduct Kharif ’16 and Kharif ’17 were use of technology shall make of CCE and its approval. A analyzed by MNCFC and the the State an example for “State level What’s App findings are being used to plug other states to emulate. group” involving all key stake in the gaps in the system. It Crop Insurance 188102 enuJ-lirpA lanruoJ IADRI TABLE-1 Sl.No. Year Normal Actual Kharif Rice Remarks Rainfall rainfall Production mms mms (In lakh MTs.) 1 2 3 4 5 6 1. 1961 1502.5 1262.8 36.99 2. 1962 1502.5 1169.9 36.32 3. 1963 1502.5 1467.0 42.47 4. 1964 1502.5 1414.1 43.59 5. 1965 1502.5 997.1 31.89 Severe drought 6. 1966 1502.5 1134.9 35.37 Drought 7. 1967 1502.5 1326.7 34.43 Cyclone & Flood 8. 1968 1502.5 1296.1 38.48 Cyclone & Flood 9. 1969 1502.5 1802.1 38.39 Flood 10 1970 1502.5 1660.2 39.13 Flood 11. 1971 1502.5 1791.5 33.76 Flood, Severe Cyclone 12. 1972 1502.5 1177.1 37.35 Drought, flood 13. 1973 1502.5 1360.1 41.91 Flood 14. 1974 1502.5 951.2 29.67 Flood, severe drought 15. 1975 1502.5 1325.6 42.74 Flood 16. 1976 1502.5 1012.5 29.58 Severe drought 17. 1977 1502.5 1326.9 40.50 Flood 18. 1978 1502.5 1261.3 41.89 Tornados, hail storm 19. 1979 1502.5 950.7 27.34 Severe drought 20. 1980 1502.5 1321.7 40.31 Flood, drought 21. 1981 1502.5 1187.4 36.63 Flood, drought, Tornado 22. 1982 1502.5 1179.9 27.07 High flood, drought, cyclone 23. 1983 1502.5 1374.1 47.63 24. 1984 1502.5 1302.8 38.50 Drought 25. 1985 1502.5 1606.8 48.80 Flood 26. 1986 1502.5 1566.1 44.56 27. 1987 1502.5 1040.8 31.03 Severe drought 28. 1988 1502.5 1270.5 48.96 29. 1989 1502.5 1283.9 58.40 30. 1990 1502.5 1865.8 48.42 Flood 31. 1991 1502.5 1465.7 60.30 32. 1992 1502.5 1344.1 49.76 Flood, drought 33. 1993 1502.5 1421.6 61.02 34. 1994 1502.5 1700.2 58.31 35. 1995 1502.5 1588.0 56.48 36. 1996 1502.5 990.1 38.27 Severe drought 37. 1997 1502.5 1493.0 57.51 Crop Insurance 198102 enuJ-lirpA lanruoJ IADRI 38. 1998 1502.5 1277.5 48.85 Severe drought 39. 1999 1502.5 1435.7 42.75 Severe Cyclone 40. 2000 1502.5 1035.1 41.72 Drought & Flood 41. 2001 1482.2 1616.2 65.71 Flood 42. 2002 1482.2 1007.8 28.26 Severe drought 43. 2003 1482.2 1663.5 61.99 Flood 44. 2004 1482.2 1273.6 58.84 Moisture stress 45. 2005 1451.2 1519.5 62.49 Moisture stress 46. 2006 1451.2 1682.8 61.96 Moisture stress/Flood 47. 2007 1451.2 1591.5 68.26 Flood 48. 2008 1451.2 1523.6 60.92 Flood , Moisture Stress 49. 2009 1451.2 1362.6 62.93 Flood/ Moisture stress/ Pest attack. 50. 2010 1451.2 1293.0 60.51 Drought/ Un-seasonal rain 51. 2011 1451.2 1327.8 51.27 Drought & Flood 52. 2012 1451.2 1391.3 86.29 Drought in Balasore, Bhadrak, Mayurbhanj&Nowapara districts. 53. 2013 1451.2 1627.0 65.85 Flood& Cyclone in 18 dists due to Phailin. 54. 2014 1451.2 1457.4 85.78 Flood & Cyclone in 8 dists due to Hud-Hud 55 2015 1451.2 1144.3 88.37 Late Season Drought TABLE-2 TABLE-3 Views expressed in this paper are author’s personal only and not of the affiliating organisations Crop Insurance 208102 enuJ-lirpA lanruoJ IADRI Agriculture/Crop Insurance in India: Key issues and way forward Azad Mishra Vice President , HDFC ERGO General Insurance Company Ltd. 1st Crop Insurance acts as find it difficult to afford crop forms and shapes in recent financial security to farmers insurance. In order to make years. Government of India by mitigating the risks the crop insurance affordable launched Pradhan Mantri associated with agriculture. to farmers, most of the Fasal Bima Yojana (PMFBY) Crop Insurance provides countries have developed during 2016-17 with a goal of compensation to the insured crop insurance schemes minimum premium and farmers in the event of crop wherein subsidies are maximum insurance for losses due to various factors provided on the premium farmer welfare. Premium such as deficit rainfall, excess amount to be collected from rates for all insured crops rainfall, high temperature, the farmers. India also has its were kept at lowest as low temperature etc. Crop own crop insurance compared to all previously insurance compensation programme which provides implemented crop insurance during adverse climatic subsidy to farmers. schemes. Crop insurance conditions not only covers the under PMFBY has gained Crop Insurance in India farm losses but also significant outreach whereby formally started way back in encourages investment on the coverage of famers under 1972 and has taken different farming for next season. the scheme increased by 18% as compared to 2015-16 and Under crop insurance, sum penetration on Gross Cropped insured for the policy is Crop Insurance provides Area (GCA) reached 30% equivalent to scale of finance compensation to the during 2016-17. Sum Insured decided for notified crop in insured farmers in the per hectare was changed from notified district. Farmers have event of crop losses due value of threshold yield to to pay a premium amount to various factors such as scale of finance under PMFBY which is charged by insurance deficit rainfall, excess rainfall, high which resulted in increase in company to insure the crop in temperature, low overall sum insured by more specified location for defined temperature etc. Crop than 70%. Also new crop risks and policy periods insurance compensation insurance scheme came up during the season. But the during adverse climatic with more comprehensive frequency and quantum of conditions not only covers the farm losses coverage wherein add on crop losses may be very high but also encourage cover such as prevented and wide spread for some investment on farming sowing, post harvest losses, crops and geographies. This for next season. mid season payments, may result into high actuarial localized risks are added with premium rates. So farmers w existing standing crop cover. Crop Insurance 218102 enuJ-lirpA lanruoJ IADRI Key issues to ponder over advertisements, brochures, and way forward posters, banners, leaflets etc. Timelines, process Also regular farmer’s 1. Time window available and mode of meetings and workshops for coverage enrolment needs to be needs to be conducted to Issues clearly briefed increase the awareness about Time window available for through various the scheme. Timelines, coverage of farmers under modes of process and mode of crop insurance is inadequate communication. This enrolment needs to be clearly due to delay in issuance of will bring in more briefed through various notification in many states. confidence among the modes of communication. This During PMFBY farmers for enrolment will bring in more confidence implementation in 2016-17, under scheme. A among the farmers for coverage time window in some Nationwide marketing enrolment under scheme. A states was as short as 10-15 plan needs to be Nationwide marketing plan days, which resulted into launched involving all needs to be launched lower coverage of farmers. stakeholders involving all stakeholders something in line with something in line with Jan Way Forward Jan Dhan Yojana. Dhan Yojana. Targets can be In order to provide ample allocated at block level for w time window for creating coverage of non loanee awareness and ensuring farmers and reward covered under the scheme maximum enrolment under programme may be initiated due to lack of awareness about the scheme, State in line with Rural Housing the scheme features, benefits, Government should issue mission. process of enrolment and notification for PMFBY at process of claim settlement. 3. Documentation for least 3 months before the cut Even for the block level coverage of farmers off date which will provide administration, scheme insurance companies and Issues awareness is low due to lack district administration ample It has been observed that of adequate training time to increase coverage by land documents are yet to be programmes. putting in well coordinated digitized in some states and efforts. Way Forward even if digitized, the recent 2. Awareness about the Awareness of the insurance changes in the crop sown are scheme scheme and its operational not updated. Further to this guidelines needs to spread tenant farmers (especially Issues uniformly wherein State oral lessee) and share After the launch of PMFBY, Government, District croppers in many locations large scale marketing administration and insurance are not able to get covered activities have been organised companies should make under the scheme due to lack by Central and State collaborative efforts to of proper documentation. Governments which resulted communicate the scheme Way Forward in increased non loanee features and process of coverage of 24% of total enrolment via different media Early adoption of model coverage as compared to 7% like television advertise leasing act in addition to during 2015-16. however still ments, press release, press separate guidelines for many farmers are not yet advertisements, radio coverage of tenants (especially oral lessee) and Crop Insurance 228102 enuJ-lirpA lanruoJ IADRI landless farmers need to be CCEs for estimating yield at submission and claims framed and implemented. notified unit level. Due to lack computation. However Digitization of land records of adequate manpower to seasonality discipline has not needs to be given priority and conduct CCEs in a short time been followed properly in all the land records needs to window (usually 20 to 40 many states wherein there be generated in soft form in a days), the quality of CCEs is had been delay in receipt of state or central portal. This affected. In addition to that, final coverage detail, also needs to be properly manual capturing and premium subsidy payment updated before the start of consolidation of CCEs yield by States to insurance season (with owner details data further delays the companies, submission of final and crop sown). Digital India process. yield report which in turn Land Record Modernization resulted in the delayed Way Forward Programme (DILMRP) was payment of claims to farmers. Considering lack of initiated to usher in new Way Forward infrastructure to conduct system of updated land large number of CCEs all In order to ensure claim records, automated mutation, across India , guidelines settlement to farmers as per integration of textual and should be framed for usage of the defined time lines, spatial records. The progress remote sensing and drone seasonality discipline should of digitization of land records based technology for smart be properly followed by all under this programme needs sampling which will reduce stakeholders. to be monitored properly and the expected number of all digital land records should In order to achieve the CCEs. CCEs should be be linked to Aadhaar card ambitious goal of reaching mandatorily conducted on the (which in turn can be linked penetration up to 50% under mobile app which will reduce to bank account). This will PM Flagship scheme, all the timeline for collating yield help in smooth quality check stakeholders needs to be data which consequently lead of land documents and will working together within the to reduced claim settlement reduce over insurance which framework of operational time with added benefits of will further reduce subsidy guidelines with strict bringing transparency and outlay. adherence to seasonality improving quality of CCEs. discipline which will bring in 4. Lack of adequate 5.Adherence to seasona- transparency in the system infrastructures to lity discipline and claim settlement with conduct Crop Cutting defined timelines will prove Experiments (CCEs) Issues as confidence booster for Issues As per the PMFBY farmers even during distress operational guidelines, After the launch of PMFBY, situations. seasonality discipline had notified units have gone down Views expressed in this been clearly mentioned with to Gram Panchayat for paper are author’s cut off date for submission of majority of crops and personal only and not of final coverage details, subsidy locations which resulted in the affiliating payment, CCE yield data larger number of targeted organisations Crop Insurance 238102 enuJ-lirpA lanruoJ IADRI TECHNOLOGY INTERVENTIONS IN CROP INSURANCE Ashok K Yadav, Manager and Nima W Megeji, Deputy Manager Agriculture Insurance Company of India Ltd. India is a vast country with during pre-independence era. 1972 to 1978. Thereafter, the varied agro climatic The concept of rainfall emphasis shifted to yield conditions comprising of insurance had been mooted by index based on area approach. more than 14 million farmers J S Chakravarty as early After some initial pilots, a full- with an average landholding as 1920. Soon after fledged area yield index based of 2 -3 acres growing a independence, a committee scheme was launched for the number of crops in a season had been constituted to entire country in 1985 which mostly for self-sustenance explore the possibility of crop ran successfully for fourteen and approximately 60% of insurance. We have been years. The experience gained the area doesn’t have assured experimenting with various through these schemes gave irrigation. The agricultural forms of crop insurance in way to the formation of a production is therefore, India.Crop insurance for H4 broader yield index based greatly dependent on rains, cotton based on Individual scheme launched in 1999 i.e. particularly south west assessment was provided by National Agricultural monsoons that provide rains fertilizer companies from Insurance scheme (NAIS) from June to September. which covered all food crops Even a slight deviation of and annual commercial crops. these rains in time and Even a slight deviation In this scheme, an element of of these rains in time quantity causes great losses individual assessment was and quantity causes in yields of various crops in kept, though on a limited great losses in yields of one or the other part of scale, to gain experience and various crops in one or country every season. Given it was used very scarcely. the other part of this uncertainty of weather, While this scheme was being country every season. crop insurance is very implemented, AIC also tried Given this uncertainty important and relevant for revenue based insurance in of weather, crop the country. the form of Farm Income insurance is very Insurance scheme (FIIS) in Crop insurance in India important and relevant 2003-04 with little success in –an overview for the country. terms of coverage and claims. w In India, there have been Weather aspect was proposals for crop insurance introduced from 2003 and Crop Insurance 248102 enuJ-lirpA lanruoJ IADRI pilot Weather Based Crop meteorological model. Insurance Scheme (WBCIS) There are perpetual was introduced from Kharif shortcomings like over Studies have been carried 2007. insurance or mis-match out to develop and test a of area insured viz a viz sampling methodology using This long experience of area sown, yield data the co-witnessed CCEs and implementing crop insurance reported not being in remote sensing to estimate schemes had raised the sync with the overall crop Gram Panchayat (GP) level expectations of farmers and condition or weather crop yields from block-level now they expect insurance to conditions that crop yields. Terrestrial provide compensation on prevailed during the Observ ation and Prediction their individual experience season. AIC has put System (TOPS) Technology rather than the ‘area technology to practical with empirical/mechanistic approach’, in other words the use to counter some of farmers want the ‘basis risk’ these issues and has models has been used to to be minimized or eliminated demonstrated that it can monitor and predict crop be effectively used in growth profiles, crop stress altogether. Besides this, the crop insurance. and yields. losses need to be assessed in Mobile phones were used to a more transparent way and w geo tag the experimental claims are to be paid soon plots and record the real time after the harvesting is over. all these technologies used in relay of the whole process. These make the insurers’ job crop insurance were available The experience so gained more complex and require readily which probably gave helped in improving and huge manpower. the confidence to incorporate calibrating the technology and advocate the usage of The ultimate solution to all and provided the much technology for various these expectations and needed confidence that crop activities in PMFBY. complexities lies in the usage insurance products can be of technology. Remote Sensing Technology further improved with the (RST) has been used for Crop incorporation of technology . Research & Development acreage estimation, crop There are perpetual All along, while implementing health /stress assessment, shortcomings like over crop insurance, use of and development of models insurance or mis-match of technology in various modes, for yield estimation. RST has area insured viz a viz area albeit on experimental basis, also been used for Crop sown, yield data reported not had been tried and tested by mapping and assessment of being in sync with the overall AIC in collaboration and crop condition based on crop condition or weather partnership with various Normalized Difference conditions that prevailed national and international Vegetation Index (NDVI) during the season. AIC has institutes, World Bank, state analysis. In addition to food put technology to practical agricultural universities etc. crops, it has been successfully use to counter some of these So, when the present scheme used to map tea acreage, tea issues and has demonstrated Pradhan Mantri Fasal Bima yield estimation and that it can be effectively used Yojana (PMFBY) was prediction using vegetation in crop insurance. conceptualized, the results of indices and agro Crop Insurance 258102 enuJ-lirpA lanruoJ IADRI (NDVI)” is a measure of 2.Area discrepancy: Technology should be biomass or crop vigour in the Rajasthan used on a larger scale plant derived through Remote There was a huge difference for the implementation Sensing Technology. It in the area insured and the of PMFBY. Although the normally ranges between 0 area sown of gram crop, as per Scheme lays emphasis and 1, but, can be scaled government records, during on the use of technology between 0 and 250. The Rabi 2013 in Churu district of for acreage estimation, scaled values were adopted Rajasthan. Therefore, area crop monitoring, mid- for the purpose of insurance. season loss assessment, Temperatures above certain sown under gram crop was assessment of losses due estimated through the images degree, particularly during to localized calamities obtained from satellite and the month of March are likely like hails, inundation, compared with the area to reduce wheat yield landslide and post- recorded by the state considerably. Therefore, harvest losses, its use department. The claims were temperature was used as a has not picked up ultimately paid on the basis of second parameter to trigger because there is no area sown under gram crop claim payout under this protocol for the usage of assessed through satellite insurance. technology. imagery. w The claims were payable 3. Remote Sensing- against the likelihood of Based Information and diminished Wheat output/ Insurance for Crops in Experiences on use of yield resulting from a) lower Emerging Economies Technology by AIC crop vigour (biomass) as (RIICE): Tamil Nadu measured using satellite 1.Wheat Insurance: imagery in terms of NDVI Haryana and Punjab An international project, within the specified taluka / Remote Sensing-Based The first practical technology block during the month of Information and Insurance for centered insurance product February (preferably during Crops in Emerging Economies was in the form of NDVI the 2nd / 3rd week (RIICE) in partnership with based insurance for wheat corresponding to peak crop GIZ, IRRI, SARMAP, TNAU, crop introduced by AIC in vigour) and / or b) high Allianz Re and AIC as the some pockets of Punjab and temperature (in degree insurance partner in India was Haryana. As a precursor to centigrade) consecutively for implemented to generate crop development of this product, specified number of days yields and crop monitoring correlations between Agro- above specified levels in the using satellite imagery and meteorological parameters 1st and / or 2nd fortnight of crop modeling from 2012 and NDVI values for past March as measured at onwards. After four years of seasons were established to Reference Weather Station testing in Cuddalore, enable current season yield (RWS). The uptake of the Shivgangai, Thanjavur, estimation. The final yield is insurance product was low as Nagapattinam and Trichy a reflection of the biomass/ the farmers were not sure districts of Tamil Nadu, the crop vigour. “Normalized about the efficacy of this State Government found it Difference Vegetative Index technology. reliable and ultimately agreed Crop Insurance 268102 enuJ-lirpA lanruoJ IADRI to use the data generated Technology played a for the use of technology for from this technology for significant role in establishing various purposes which will assessing the area sown under that the crop was not as bad go a long way in adoption of paddy crop to arrive at the as being presented by the technology in crop insurance. ‘sowing failure / crop failure’ yield data of the state References: under PMFBY during Rabi department and thus a 2016. It is being extended to formula was agreed to arrive 1. Mishra, P. K. (1995), other areas of the State and at the loss assessment, ‘Is Rainfall Insurance a other States are also thereby reducing the claims. New Idea: Pioneering assessing the idea of using it. Scheme Revisited’, Way forward Economic and Political 4. Yield data Weekly, vol. XXX, no. Technology should be used on discrepancy: Gujarat 25, pp. A84–88. a larger scale for the In spite of Kharif 2016 season implementation of PMFBY. 2. Rao, K. N. (Ed.). 2013. being good and no adverse Although the Scheme lays Agriculture Insurance reports on crop production, emphasis on the use of (IC-71). Insurance the yield data submitted by technology for acreage Institute of India. the State department for estimation, crop monitoring, 3. Remote Sensing- ground nut crop in Gujarat mid-season loss assessment, Based Information showed losses in some specific assessment of losses due to and Insurance for districts. This was contested localized calamities like hails, Crops in Emerging with scientific results i.e. inundation, landslide and E c o n o m i e s NDVI derived from satellite post-harvest losses, its use (RIICE).http:// images and Unmanned Aerial has not picked up because www.riice.org/about- Vehicle (UAV) images there is no protocol for the riice/ initially. The matter was then usage of technology. Views expressed in this referred to GoI and the MahalanobisNational Crop paper are author’s Technical Advisory Forecasting Centre (MNCFC) personal only and not of the affiliating Committee. is developing some protocols organisations Crop Insurance 278102 enuJ-lirpA lanruoJ IADRI Issue Focus AGRICULTURAL / CROP INSURANCE IN INDIA - PROBLEMS AND PROSPECTS Remote Sensing Applications in Crop Insurance – A success story from Tamilnadu using TNAU-RIICE technology Dr. S. Pazhanivelan Monitoring the security. Fluctuations in production of field crops is production of field crops, important for ensuring food influenced by extreme security in India. Accurate weather events viz., and consistent information on variations in onset, progress monitoring. Synthetic the area under production is and withdrawals of monsoons, Aperture Radar (SAR) necessary for national and floods caused by torrential imagery is a promising option state planning but to overcome the issue of rainfall and uneven conventional statistical cloud cover. Recent and distribution, necessiates a methods cannot always meet planned launches of SAR proper crop monitoring the requirements. This sensors viz., RISAT (India), mechanism on a spatial scale. information is vital to the Cosmoskymed (Italy), Terra Further Climate change poses policy decisions related to SAR-X (Germany) and threat to agricultural crops imports, exports and prices, Sentinel 1A (ESA) coupled through extreme weather which directly influence food with state-of-the art events. To ensure resilience automated processing among the resource poor provide sustainable solutions marginal farmers, disaster Remote sensing has the risk reduction in terms of crop to these challenges. scope for cost effective insurance is needed. With latest advances in precise estimates of remote sensing and crop yield Remote sensing has crop area. But the modeling, it is now possible to technical challenges viz. the scope for cost effective provide accurate information cloud cover during precise estimates of crop on crop acreage, crop health, cropping season, wide area. However, the technical yields, crop damages and loss range of environments, challenges viz. cloud cover during floods and drought. small land holdings and during cropping season, wide Early estimation of the end of diverse and mixed range of environments, small the season yield can help cropping systems limits land holdings and diverse and the use of remote mixed cropping systems insurers to envisage pay-outs sensing as a tool for limits the use of remote and early claim settlements crop monitoring. sensing as a tool for crop without waiting for the CCE w Crop Insurance 288102 enuJ-lirpA lanruoJ IADRI generate information like rice technology in the year 2016. Pradhan Mantri area statistics, mid-season The ensuing cropping season Fasal Bima Yojana rice yield forecasts and end- i.e., Rabi 2016-17 saw the (PMFBY) is a flagship of season yield estimates worst drought in Tamil Nadu scheme of the down to the village level. This in last 140 years. RIICE Government of India to helps government decision measured the rice area lost to provide insurance makers, insurers, and relief be about 1 million ha. of the coverage and financial organizations in better sown area covering close to 1 support to farmers in the managing domestic rice million farmers. event of failure of any of production during normal the notified crops, Pradhan Mantri Fasal growing conditions and during unsown area and damage Bima Yojana (PMFBY) is a the compensations after to harvest produce as a flagship scheme of the natural catastrophes strike. result of natural Government of India to calamities, pests and Initiated in 2012 in the provide insurance coverage diseases to stabilize the state of Tamil Nadu, India, and financial support to income of farmers, and to the project with Tamil Nadu farmers in the event of failure encourage them to adopt Agricultural University as its of any of the notified crops, modern agricultural lead implementation partner unsown area and damage to practices. has been actively harvest produce as a result of collaborating with the state natural calamities, pests and w Government and the diseases to stabilize the data. TNAU has insurance industry towards income of farmers, and to demonstrated the efficacy of establishing a successful encourage them to adopt SAR based rice crop model of technology leading modern agricultural practices. monitoring and information to sustainable delivery of The scheme is a considerable system in Tamil Nadu products and services. This improvement over all through the RIICE comes at the backdrop of previous insurance schemes in Programme ‘Remote sustained engagement with India which aims to cover 50 sensing-based Information the Government and creating percent of the farming and Insurance for Crops in a policy environment which households within next 3 Emerging economies’ in allows the project based years. The scheme envisages collaboration with deliverables to be used by the use of technologies viz., International Rice Research both public and private Remote sensing, Drones and Institute (IRRI), GIZ and insurers in portfolio mobile applications. PMFBY Sarmap, Switzerland. monitoring and claim has provision for administration in case of compensation under different TNAU RIICE aims at imminent losses. Due to the clauses viz., Prevented, Failed reducing the vulnerability of outreach efforts, the sowing and total crop failure smallholder farmers engaged Government of Tamil Nadu due to extreme weather in rice production by crop gave official approval for events. insurance. RIICE technology piloting TNAU-RIICE makes use of satellite data to Crop Insurance 298102 enuJ-lirpA lanruoJ IADRI Application as per Description as per PMFBY Use of RIICE technology PMFBY Guidelines Prevented sowing risk can be RIICE satellite technology can be used Prevented Sowing/ defined as the risk of farmers to verify the occurrence of the following Failed SowingRisk not being able to plan/sow the perils:Flood, drought, inundation as well notified crops in the insured as their impact on village level. It will area due to adverse seasonal only take 10 days post event (in conditions. A pay-out of up to exceptional cases 12 days) to verify the 25% of the sum insured is loss. foreseen. The insurance policy will be voided thereafter. On Account PMFBY specifies “on account Prior to the mid-season RIICE can payment” of up to 25% of the report on loss areas as in the above case. Payment sum insured in case the In addition, as from the middle of the following two conditions are season onwards, RIICE can also predict met: a) the expected final the impact of a certain natural calamity season yield is below 50% of on the expected final yield at the end of threshold yield and b) All the season. perils are covered and the payout is at revenue village level. Smart sampling of PMFBY outlines the role Before the end of the season RIICE can CCEs remote sensing technology prepare a list of vunerable areas can play in the smart sampling showing symptoms of crop stress due to of CCEs and can be adverse seasonal conditions. This will successfully used to target the lead to prioritisation of Insurance Units CCEs within the Insurance (IUs) across a homogenous region Unit (IU) where more number of CCEs are required. Acreage Estimation It has been observed in some In order to present an accurate overview instances that the area notified and to avoid over- or underinsurance, a for insurance exceeds the map will be generated to show the actual planted area in a given location of rice in the monitored season, insurance unit. Fair crop demarcating rice growing areas from insurance should ensure the non-rice growing areas. This can be correct insurance areas and done at village level, delivering the rice the PMFBY has provision to growing area in ha. on village level. This address this anomaly thereby product can be delivered at mid-season avoiding area discrepancy. at the earliest but during the upcoming season it will be delivered two weeks after the end of the season at the latest. Use of proxy indicators. This The remote sensing yield data is End of the Season provides the opportunity to generated immediately after the end of Yield Estimates use remote sensing based yield season thereby providing sufficient time indices to provide an alternate to identify areas where expected final source of yield data apart from yield will be lower. This will provide the the official CCEs. areas where the official CCE data from claims point of view is critical. The other way is to use the remote sensing based yield data for actual claim settlement. Crop Insurance 308102 enuJ-lirpA lanruoJ IADRI Methodology Satellite used : Sentinel 1A (ESA) The basic idea behind Spatial resolution : 20m the generation of rice acreage using radar data is the Temporal resolution : 12 days analysis of changes in the Data acquisition : 19th Sep 2016 - 17th Jan acquired data over time. 2017 Measurement of temporal No. of acquisitions : 11 changes of SAR response due to the rice plants phenological any of these administrative estimation and the salient status lead to the units can be produced. features of the technology are identification of the areas Rice yield prediction is · High resolution Synthetic subject to transplanting. The performed by combining Aperture Radar (SAR) rice acreage statistics are remote sensing, in situ, imageries were used to stored in map format showing climatic data and an Agro map and monitor Paddy the rice extent and, in form Meteorological Model. crop area coverage. of numerical tables, Production (I), finally, is · Application of MAPscape- quantifying the dimension of simply calculated by Rice software with the area at the smallest combining yield estimation (t/ automated processing administrative level - ha) and the acreage (ha) chain. typically village unit- derived from the radar data. · Integrating Crop Growth cultivated by rice. These T N A U - R I I C E simulation model ORYZA products are linked to technology resulted in higher and RiceYES interface for district, region, state and accuracies of 89-93% for rice yield estimation. country, so that statistics on area and 87-90% for rice yield SAR based Remote Sensing Products used in Crop Insurance Product Frequency Description Rice area maps Once per season A detailed map of the rice growing area detected monitored from the analysis of Sentinel 1A data acquired every 12 days through the monitored season. Date of start of Once per season The time series of images used to estimate, the start season map monitored date of the growing season for each pixel. This is a critical input to the crop model that estimates yield. It is also critical for estimating the area that has been planted at a given date. Production loss Once event If the event occurs in a season that is being estimates occurred monitored, the imagery can be interpreted to estimate the area affected. The yield estimates are used to estimate the expected production loss from this damage per mapping unit. End of Season End of season Yield model incorporates weather and SAR data to Modeled Yields produce a yield value for each calibrated spatial unit Crop Insurance 318102 enuJ-lirpA lanruoJ IADRI Fig. Utility of TNAU-RIICE technology in PMFBY Department of Remote Sensing and GIS, Tamilnadu Agricultural University assessed the impact of recent drought during 2016 on crop condition using Sentinel 1A satellite data acquired between September 2016 and January 2017 at 12 days interval. The annual rice area map, seasonality maps and Assessing Rice crop with Rice start of the Season map statistics, crop signature and Sentinel 1A Satellite and progression of planting yield information were used to meet the requirements of Normal area sown figures for the notified villages were different features of PMFBY compared with the village wise area generated using SAR data crop insurance scheme. and the villages were identified for invoking prevented sowing wherever the area sown was less than 25 % with the reduction 1.Remote sensing for caused by delayed onset of monsoon or water release from Prevented and Failed canal preventing the farmers from sowing or planting. sowing A detailed map of the rice growing area detected from the analysis of Sentinel 1A data acquired during the monitored season was used to generate rice area statistics Total Crop Failure Failed sowing every 12 days at village level. Crop Insurance 328102 enuJ-lirpA lanruoJ IADRI Backscattering signature for crop field were generated using the dB stack derived from 11 date SAR images and the date of crop failure was assessed and the villages were identified for failed sowing (within thirty days after sowing) or total crop failure (beyond 30 days). District Villages Prevented / Total Checked Failed Sowing Crop Failure Pudukottai 193 27 160 Ramnad 51 38 13 Nagapattinam 155 34 112 Tiruvarur 378 26 18 Cuddalore 183 4 179 Ariyalur 31 22 9 Tiruchirapalli 502 210 - Erode 365 127 - Tiruvannamalai 1 - 1 Kancheepuram 30 - 30 Tiruvallur 16 - 16 Virudhunagar 318 - 31 Sivaganga 293 41 252 Total 2516 529 821 2. Assessing the impact devastating floods based on a subsequent flooding in many of Flood and drought timely assessment report districts of Tamil Nadu using Remote sensing containing flood maps and resulting in severe damage to statistics provided by agricultural land and i. Flood maps from SAR TNAU.The deadly depression property. In response to the data crossing over the Tamil Nadu catastrophe, the RIICE’s The StateGovernment coast in early November flood assessment report was of Tamil Nadu, India initiated 2015(as shown in the left delivered as part of the relief several policy level measures image captured by a and flood rehabilitation efforts in alleviating the losses in the meteorological satellite), to the Government of Tamil aftermath of the 2015 caused heavy rains and Nadu . Crop Insurance 338102 enuJ-lirpA lanruoJ IADRI ii. Impact of Drought on crop condition The impact of recent drought during 2016 on crop condition was monitored by retrieving time series Leaf area Index using Sentinel 1A SAR satellite data and composite NDVI derived from MODIS. The area under the classes of moderate and severe drought was assessed and shared with Insurance companies for possible loss and anticipated claim assessments. LAI Map of Rice area NDVI Map of Rice area failure in 821 villages. In total 3. Yield loss assessment 8,80,179 farmers were benefitted from the crop Rice yields and hence insurance and the payouts production at district, block were to the tune of Rs. and village level are assessed 2,769.15 crores. The satellite by integrating remote technology has helped in sensing products viz., Rice getting quicker payouts and area, Start of the Season and also to maximize the dB Stack into the crop compensation which was due for the farmers ensuring the growth simulation model prevented/failed sowing in social protection. ORYZA. Yield loss if any 529 villages and total crop were estimated by Insurance payouts through TNAU-RIICE Technology comparing satellite derived Crop Insurance No. of Farmers Claim Amount rice yields with threshold feature benefitted (Rs. In Crores) yields for the villages as Prevented sowing 47,513 60.46 notified. Through RIICE Technology Samba rice (Paddy-II) Yield loss claims - RIICE 2,56,190 933.61 growing villages in Yield loss claims - DES 5,76,179 1,775.08 Tamilnadu were monitored Total 8,80,179 2,769.15 for crop loss assessment and the remote sensing Views expressed in this technology helped in paper are author’s identifying or invoking personal only and not of the affiliating organisations Crop Insurance 348102 enuJ-lirpA lanruoJ IADRI Issue Focus Pradhan Mantri Fasal BimaYojana (PMFBY) – Issues inhibiting its big success and probable way forward M K Poddar, GM, Agriculture Insurance Company of India Ltd Given the constraints of the sown seed or seedlings, agrarian landscape of India, the seeds do not germinate as a crop insurance solution, or fail to survive due to PMFBYis a good blend of adverse weather conditions, PMFBY is a yield index insurance the farmers are eligible for combination that takes working on “Unit Area” compensation. Whereas the care of systemic or (village panchayat / block / first case is known as covariate risk mandal/ patwari halka/ prevented sowing, the associated with revenue circle etc.) and second one is called failed widespread calamities traditional named peril sowing/ planting.Further in as well as idiosyncratic insurance(landslide, case of mid-season adverse losses arising from hailstorm and inundation) weather conditions, viz. localised calamities aiming individual farm- prolonged dry spell after viz hailstorm, based damage assessment. good initiation of crop, which landslide and Farm based damage may lead to yield losses, ad- inundation.Farmers assessment is also prescribed hoc or on-account payments are also indemnified in for post-harvest-on-field are prescribed so that case they are not able losses due to unseasonal or farmers get some ad-hoc to sow, plant or cyclonic rains that damage compensation in the interim transplant the crop crops kept on the field for to let him look for alternative due to early-season drying. Therefore, PMFBY operations. adverse weather is a combination that takes No scheme previously has conditions viz. delayed care of systemic or covariate offered such a compre- arrival of monsoon etc. risk associated with hensive protection. widespread calamities as w However farmers probably well as idiosyncratic losses also look for compensation arising from localised against widespread disease Given that PMFBY being calamities viz hailstorm, and pest attacks that impact the most feasible landslide and inundation. many farms simultaneously insurance product Farmers are also indemni- which sometimes cannot be which purportedly suits fied in case they are not able anticipated or contained. the majority stake- to sow, plant or transplant PMFBY as on date do not holders and that too at the crop due to early-season compensate such losses until the cheapest price for adverse weather conditions and unless they are reflected the farmers, what is viz. delayed arrival of in the yield estimates of the stopping it to be a monsoon etc. Even in case of Insurance Unit Area. landslide success? The Crop Insurance 358102 enuJ-lirpA lanruoJ IADRI issues are many and Crop Insurance in India has when the States of multifarious illustrated always remained a multi- Karnataka and Tamil Nadu as under. agency program wherein saw the non-loanee roles of various agencies like, participation soaring 1. The critical challenge is in Banks/ PACS (Primary exceptionally high, followed distribution, that is, mostly Agriculture Cooperative by almost 300%loss ratio the “bad risks” are getting Societies), State (the ratio of indemnity paid, insured. Areas or crops Governments and insurance and premium collected). In prone to losses due to lack of Companies though well- fact, from the insurers’ point irrigation facility or the crops defined are yet poorly of view, this is a glaring which are too susceptible to executed as there is no example of adverse selection adverse weather conditions accountability for not in a draught-like situation, a are usually covered, leaving performing the assigned typical moral hazard that got majority of the “good duties. For example, for established during NAIS risks” out of the insurance loanee farmers, the scheme (National Agricultural basket. It goes without is compulsory, but a Insurance Scheme) regime. saying that predominantly- substantial part of the Insurance Companies raised bad-risk-insurance portfolio eligible loans is left un- doubts about (i) areas being will attract a high premium insured on some pretext or insured without any crops rate which in turn will put a other. Non-compliance of attempted by the farmers strain on State compulsory insurance, and (ii) extensive recording Government’sbudget. This particularly from the good of zero yields without skewed distribution of risk is risk areas is making the conducting Crop Cutting basically due to scheme costlier for the Experiments (CCEs) by the administrative slackness. Government, as for the State Government. This is farmers the premium rate is an issue that plagued crop capped. insurance system in India for Huge financial burden a long time and is still posing on States for running 2. The second issue as far as a problem in putting the PMFBY is one of the sustainability is concerned is PMFBY on a transparent critical limiting factors that, even if the good risks and sustainable footing. The for sustainability. are brought in, and only solution is Given an option, most compulsory provision is advancing the cut-off of the States would like complied fully, the premium date for enrolment of to quit PMFBY and subsidy liability of the State farmers to a point of perhaps would like to Governments will go up in time when the farmers come back to NAIS for absolute terms at least in the are not aware about the the primary reason short run. Therefore, State impending losses. being that under Governmentsmust allocate PMFBY the money more budget for PMFBY Huge financial burden on (premium subsidy) has which most of the time is States for running PMFBY is to be paid upfront to seen as an expenditure one of the critical limiting the insurance wasted. Only exception, in factors for sustainability. companies without recent times when States Given an option, most of the knowing the return. could see value in insurance States would like to quit is during Rabi 2016-17, PMFBY and perhaps would w like to come back to NAIS Crop Insurance 368102 enuJ-lirpA lanruoJ IADRI for the primary reason being 4. The fourth issue is about that under PMFBY the Yield data of the past yield estimation or loss money (premium subsidy) years and for the estimation. Yield data of the has to be paid upfront to the current insured season past years and for the insurance companies is perhaps the single current insured season is without knowing the return. most important perhaps the single most States perceive the element around which important element around payment of subsidy is an the entire mathematics which the entire instant loss and not as a of Indian crop mathematics of Indian crop cost for transfer of risk insurance program insurance program to the insurer. This view revolves. Irony is that revolves. Irony is that CCEs of seeing insurance premium CCEs through which through which the yield data as instant loss rather than the yield data is is arrived at, is an ill- cost of risk transfer is all arrived at, is an ill- managed activity of the pervading and prevalent managed activity of the Indian Crop Insurance across all lines of general Indian Crop Insurance program which needs insurance business. We are program which needs revamping. In fact, in the basically an insurance revamping. sixties when CCE averse society worried about methodology and implemen short term losses rather w tation was conceptualised, than long term risk crop insurance was not management solutions. expenses) in a good year like there. It was conceptualised 2016-17 is unsustainable as only for generating basic 3. Thirdly, as mentioned in a widespread drought agricultural statistics at a above, huge outgo as situation like that of 2015-16 district or at sub-district advance premium subsidy where losses could go up to level to assist planning and seen as a costly affair for Rs 50000 crore against an policy making. The basic certain cash strapped insured liability of Rs statistical data compiled States, raising the questions 200000 crore (2016-17 sum were area, production and on the sustainability of insured). Therefore, from yield (APY) in respect of a PMFBY from the political either side there are issues particular crop in a district. view point. Adding salt to the of sustainability looming The survey through which injury, the data collected large. To give comfort to the this estimate is generated is from insurance industry States’ finances, premium called GCES (General Crop shows that all the companies rates need to come down as Estimation Survey). When combined made a gross profit quickly as possible and this crop insurance started at of Rs.7000 crore will be possible, if only the country level in 1985, the approximately out of first legitimate claims are paid. same GCES data was used year of operation i.e., during For area yield index for calculating guaranteed 2016-17, which is roughly insurance like yield (threshold yield) and 32% of the national premium PMFBY,season-end yield also for estimating actual volume. Insurance losses overwhelmingly yield in the insured season. industry’s view point is constitute the total claims. This means that the GCES diametrically opposite Therefore, yield estimation data collated for APY though. Industry feels that through CCEs assumes a purposes would also be used having a 32% margin great importance. for insurance purposes for (excluding operating calculating compensation for Crop Insurance 378102 enuJ-lirpA lanruoJ IADRI Scheme that only single Only solution to the Only solution to the series of estimate i.e., GCES everlasting CCEs issue is, everlasting CCEs issue is, estimate data would be used perhaps, the use of perhaps, the use of remote sensing. Nowa for both insurance and for remote sensing. Nowa days satellite imagery and APY statistics to stop days satellite imagery remote sensing technology producing a separate data and remote sensing have improved to an extent series for crop insurance. technology have which can provide crop-area However, NAIS was having improved to an extent estimation with 85% to 90% another mandate of lowering which can provide crop- accuracy at village/ village the size of insurance unit to area estimation with panchayat level. As far as village panchayat level for 85% to 90% accuracy at yield estimation is major crops which village/ village concerned, accuracy varies necessitated an increased panchayat level. As far as from crop to crop, but it yield estimation is number of CCEs at district would be safe to say that the concerned, accuracy level. The States could not latest technology supported varies from crop to crop, develop their infrastructure by adequate number of but it would be safe to say to conduct the increased ground truthing (field data that the latest number of CCEs and collection) and collection of technology supported by gradually the quality other related data like adequate number of declined to an alarming level. weather data etc.has the ground truthing (field potential to produce a good PMFBY, as such, requires as data collection) and indicative yield. Over the many as 30 to 35 lakhs of collection of other last couple of decades CCEs to be conducted during related data like remote sensing scientists Kharif and Rabi seasons weather data etc.has the working in the field of which appears to be an potential to produce a agriculture have developed insurmountable task for the good indicative yield. many indices based on States to handle. It may not satellite imagery viz. w be possible ever for the Normalised Difference States to conduct so many Vegetation Index (NDVI), yield losses. However, CCEs in such a short time NDWI (Normalised unfortunately within two window ensuring quality. To Difference Wetness Index), years of implementation of meet the demand many Standard Precipitation CCIS (Comprehensive Crop states are going for Index (SPI), Vegetation Insurance Scheme 1985) outsourcing without any Health Index (VHI), Leaf some of the States started capacity building resulting in Area index (LAI) and so on. altering the CCE process poor quality of data. All these indices attempt to which otherwise has sound Certainly conducting so produce a yield forecast or statistical basis. The states many CCEs through modelled yield at a started producing two series outsourcing or otherwise is reasonably acceptable level. of yield estimates one for not a sustainable proposition The European Space crop insurance and other one and this practice will Agency’s (www.esa.int) for APY statistics. When eventually lead to large scale Copernicus Satellite NAIS was introduced in disputes involving the Program has come up with a 1999 replacing CCIS, it was insurance companies and dozen earth observation clearly mandated in the farmers. Crop Insurance 388102 enuJ-lirpA lanruoJ IADRI satellites named as Sentinel agriculture) which has a Insurance doesn’t Series with primary dedicated Agricultural reduce the chances of emphasis on studying the Division that does all kind of drought or flood impact of climate change and necessary remote sensing happening, what it does how to mitigate the same to and capacity building is, spreading the ensure civil security. The activities thatcan usher in adverse impact over the technology intervention best part of ESA’s program space and time so that in PMFBY in a time bound is that the sentinel data is of the affected farmers do manner. very high resolution, good not get a rude financial frequency and swath and shock and their Problem with Weather available free of cost for use livelihood is reasonably Based Insurance:The by a registered user. sustained. Therefore, other alternative to yield Recently many private insurance is a necessity index insurance is Weather research and start-up particularly for Based Crop Insurance agencies have started using agriculture sector, Scheme (WBCIS). WBCIS these high-resolution data otherwise 125 countries caught the imagination of the and started producing good in the world would not Central and State results in terms of crop have established Governments from 2007 health monitoring and yield agriculture insurance onwards and was an instant forecast. system. success, and it became almost equal to NAIS in It is also worth mentioning w 2012-13 in terms of area that the Honourable PM under insurance. Success of various claim triggers like held a meeting on technology WBCIS is based on the prevented sowing, mid- intervention in PMFBY way fundamentals of strong season adversity, damage back in mid-2016 involving crop-weather relationship. assessment for localised DST, ISRO, NRSC and With growth of WBCIS, calamities and for post- DAC&FW to bring in gradually, the fundamentals harvest losses. Until and efficiency, objectivity and were compromised, and unless the protocols are sustainability. Subsequent to stakeholders were found to defined and notified, there this NITI Aayog Agriculture be more inquisitive in will be a serious lack of vertical constituted a Task finding premium-claim standardisation. Force on Enhancing relationship so much so that Technology Intervention in The Probable way forward the pay-out term- sheets Agriculture Insurance. The is, therefore, to have a were developed assuring Task Force has since credible independent sure claims. This led to very submitted its institutional mechanism to high premium rate for recommendation to DAC& usher in usage of technology WBCIS. At present WBCIS is FW almost a year back. in a structured manner. a poor cousin of PMFBY, only Ideally the agency should be implemented for For leveraging the more of a Scientific Agency horticultural crops and in technology intervention in of national eminence and some districts chosen by the PMFBY what is immediately international access such as State governments. needed is devising and National Remote Sensing defining protocols for using C e n t r e ( h t t p s : / / remote sensing and data for w w w . n r s c . g o v . i n / Crop Insurance 398102 enuJ-lirpA lanruoJ IADRI PMFBY – Probable Way initiated the PM Krishi a different model of financial Forward to Sinchai Yojana, a 5-year-Rs. administration can be sustainability 50000 cr project in 2015. thought of where in insurance company will not It is not difficult to Insurance doesn’t reduce be allowed to make any large appreciate the discomfort of the chances of drought or profit. The empanelled the States which have to pay flood happening, what it does Insurance Company will do PMFBY premium subsidy is, spreading the adverse everything that it is required that takes a lion’s share of impact over space and time to do today and will continue State’s agriculture budget so that the affected farmers to participate in the only to discover later in the do not get a rude financial tendering process to win the year that the money has shock and their livelihood is districts and clusters. been made to some of the reasonably sustained. Additionally, they will aslo Insurance Companies. If it Therefore, insurance is a submit the accounts at the happens year after year, one necessity particularly for end of the year to the Centre can be sure of criticism agriculture sector, otherwise and States. Insurance pouring in from all quarters. 125 countries in the world companies will carry the The issue is that nobody would not have established risks with an overall cap of, would like a commercial agriculture insurance say, 120% on its portfolio company making money out system.It is also to be noted and a cap of, say, 80%. Which of farmers’ plight, given the that the agricultural means losses beyond agrarian distress in the insurance system is better 120%falls on Central and country. established and gaining State at a ratio of 40:60, strength in most of the high Over last 17 years, starting whereas surplus arising out and middle-income with the introduction of of pure losses below 80% is countries (where contri- NAIS in Rabi1999-2000, ploughed back to the Centre bution of agriculture to their Government, Centre and the and State in the same ratio. respective national GDP is in States combined spent Centre and every State will single digit only) than in approximately an amount of create a separate crop lower-middle and low- Rs 75000 crore in imple- insurance fund account income countries. In many menting crop insurance. A (similar to CCIS regime) developed and developing question arises whether the which will be used only for countries, the system is amount could have been crop insurance purposes. codified through proper better utilized in the form of Minimum limits of various legislation, so the various developing long-term capital expenses such as agencies involved in the investment viz., augmenting management and publicity process do their job irrigation facilities in 104 expenses etc. to be borne by sincerely. In India an perennially drought-prone an insurance company can Agricultural Insurance districts. There is no doubt be prescribed so that Act is overdue. Time is ripe that the cost of insurance insurance companies are that India takes it seriously would have been much lower bound to incur the minimum and goes for it as a by de-risking agriculture service related expenses to substantial part of Central with more cropped areas keep the service quality at a and State funds are involved. covered under permanent standard level. Insurance irrigation. Perhaps this is the Coming back to the issue of Companies will be free to reason why the Central States’ discomfort is paying make their own reinsurance Government has already upfront premium subsidy – arrangement to protect their Crop Insurance 408102 enuJ-lirpA lanruoJ IADRI own account.With this initially and with greater risk best practices prevailing arrangement the cost of management by insurance elsewhere particularly in reinsurance will also come companies and States, the high and middle-income down. As far as upfront premium rate may fall countries, a comprehensive premium subsidy is further after some time. legislation on Agriculture concerned, the sharing Insurance companies will be Insurance should be put in pattern between Centre and encouraged to use all place. Till that point of time State may be, 60:40, Centre scientific tools to at least an Independent picking up a greater share authenticate losses. It will be Agency should be set up as a i.e., 60% of the premium a win-win situation for all, for part of strong institutional subsidy leaving 40% to be the Scheme, for the Centre, mechanism to objectively borne by the State in place for the States and for long- and transparently assess of 50:50 at present. This will term insurers. crop losses in the insurance put less pressure on the units. Remote sensing Conclusion: State’s budget making them technology is a handy tool to more comfortable. On the PMFBY is a well-designed objectively assess crop area claims financing side beyond insurance solution in the planted and monitor crop 120% the sharing may be a Indian context characterised health and to ultimately reverse one i.e., 40% Centre by large number of small arrive at an indicative yield and 60% State – this will land holdings. It is quite or yield losses. make States more vigilant comprehensive in covering Further to strike a win-win about maintaining quality the major production risks situation for all the check on the Crop Cutting induced by adverse weather stakeholders the existing Experiments. State conditions during the entire risk sharing between Governments may choose crop life cycle. The scheme Insurance companies, for reinsurance protection to is very cheap for the farmers Central and State protect their own account. though perceived as costly Governments and by the State governments. Since insurance companies’ Reinsurers may be reviewed losses are capped at 120%, PMFBY, to be a major as suggested above. certainly the actuarial success needs adequate Views expressed in this premium rate will come support services. To paper are author’s down by at least 10% - 20% facilitate this, following the personal only and not of the affiliating organisations Crop Insurance 418102 enuJ-lirpA lanruoJ IADRI A Closer Look at Agriculture Insurance of India Vivek Lalan, Asst. VP, Agri Business, Bajaj Allianz General Insurance With weather being its agricultural insurance on their risk profile, premium greatest ally as well as its schemes. potential and agro-climatic greatest adversary, agri- zones and each cluster is The Schemes as of culture is one of the most allotted to a single insurance Today elemental form of activities company based on where a farmer toils the Fast forward to today, competitive bidding. There is ground, and reaps the the Pradhan Mantri Fasal no capping on rates, hence, reward – an occupation vital Bima Yojna, implemented insurance companies will be for the very sustenance of from Kharif 2016 onwards able to charge actuarial human life on earth. In a works on the principle of – premium for the clusters, but country like India, where “One Season, One Crop, One the farmer’s share is limited agriculture and allied sectors Premium Rate”. to 2% of the total premium in account for around 14% of the Kharif and 1.5% of the total Under this scheme the GDP and employ about 50% premium in Rabi for food states are divided into of the workforce, is ranked grain and oil seed crops and homogeneous clusters based top in a list of populations 5% of the total premium for most at risk from natural commercial and horticulture Crop insurance was disasters, adequate solutions crops. The claims are a hence devised by Indian need to be implemented to function of Crop cutting policy makers to make render the economy less experiments done by revenue good the financial losses exposed. departments across the incurred by the agrarian country. Crop insurance was community of India. hence devised by Indian While the first ever crop The scheme is spread policy makers to make good insurance scheme got across an area of 57 million the financial losses incurred implemented in 1972, the hectares covering 5.71 crore by the agrarian community of credit for pioneering the farmers in its first year of India. While the first ever idea goes to J. S. operations itself, as against crop insurance scheme got Chakarvarti, who as 4.85 crore in 2015-16. Crop implemented in 1972, the early as in 1915, had insurance witnessed an 18% credit for pioneering the idea proposed rainfall based spike in penetration in the goes to J. S. Chakarvarti, who agricultural insurance very first year of the scheme’s as early as in 1915, had schemes. implementation. Assuming a proposed rainfall based similar rate of growth, the w Crop Insurance 428102 enuJ-lirpA lanruoJ IADRI penetration of scheme should the problem areas that farmers basis their credit soon reach 50% in next continue to hinder a smooth usage through banks. The couple of years. functioning of the crop scheme guidelines suggests insurance schemes of India. A that Loanee farmers are to be Another existing few have been broadly covered compulsorily through scheme that has been analyzed and discussed their banks. However, the restructured and re- through this article: number of loanee farmers implemented is the WBCIS – covered under the scheme is the Weather Based Crop 1. Delay in Transfer abysmal as compared to the Insurance Scheme. This of Data: Since the actual Kisan Credit Cards (KCC) scheme provides protection to losses are determined basis issued. Significant efforts the insured cultivators in the Crop Cutting Experiments need to be taken to improve event of loss in crops yields conducted by State the insurance outreach to the resulting from the adverse Governments, the claim loanee farmers. One such weather incidences, like un- payments highly rely on enabler would be linking of seasonal/excess rainfall, heat correct and timely flow of AADHAR numbers with bank (temperature), frost, relative yield data. Often this data accounts which will make it humidity etc. Claims arise takes a lot of time to get easier to identify defaulting when there is a certain transferred from the farms to branches. adverse deviation in Actual the insurer’s data base. Weather Parameter Instead of manual processes The non loanee farmers Incidence in Reference Unit of data keeping, the State also receive the same Areas (RUA) (as per the Governments need to start premium subsidy as the weather data measured at using technology to capture loanee farmers. However, Reference Weather Stations), results of crop cutting they are not mandatorily e.g. its “Actual temperature” experiments. An application covered under the scheme. A within the time period has already been developed non loanee farmer can use his specified in the Benefit Table by Central Government for Bank, Agent or Common is either less or more this purpose, which ensures Service Centre as well for compared to the specified “ timely submission of yield enrolment under the scheme. temperature Trigger”, data to Government and Interesting to note, IRDAI leading to crop losses. In such Insurance companies so as to has authorized all Village case, subject to the terms and enable a faster claim Level Entrepreneurs conditions of the Scheme, all settlement. Efforts also, need (Common Service Center), insured cultivators under a to be taken to enable Direct numbering up to an particular crop shall be Bank Transfers(DBT) so that approximate of 2.4 lakhs, to deemed to have suffered the farmers get the claim sell crop insurance. Never same “adverse deviation” in payment directly in their before was such a huge temperature and become account. This can be done channel was opened up eligible for claims. once AADHAR number is overnight to increase linked to all bank accounts penetration of insurance. A Closer Look which will make DBT easier Though, in the first year of the On face, with numbers and errorless. scheme’s implementation, around 1.37 crore farmers supporting it, the scheme look 2. Low Insurance successful, however, certain were insured under the non Penetration: For the challenges in its implementa- loanee category, still, the non purpose of insurance, farmers tion still remain. A deep loanee coverage has a huge are usually classified as analysis of the same reveal scope of improvement. Loanee and Non Loanee Crop Insurance 438102 enuJ-lirpA lanruoJ IADRI 3. Low Levels of Insurance Awareness: Large scale awareness responsibility of insurance The biggest selling point for and education companies along with any product is its timely programs on insurance Governments to restore faith adoption by its targeted need to be conducted at of farmers in insurance as a customers. This has been one the grassroots levels so concept. of the chink in the armour of that the benefits seep Large scale awareness the crop insurance schemes of down to even small and education programs on India. This is a main challenge scale and tenant insurance need to be area as a lot of farmers need farmers. The infusion of conducted at the grassroots to be still made cognizant of technology, at all levels levels so that the benefits the benefits of having their of the scheme seep down to even small scale crops covered by an insurance implementation from and tenant farmers. The policy. They need to be issuance to claims infusion of technology, at all educated on the coverage offered and on what’s covered payout is another pre- levels of the scheme and what’s not covered by the requisite to a smooth implementation from various schemes. The deployment. issuance to claims payout is another pre-requisite to a insurers and the w smooth deployment. Finally Government. together can should sit together to decide a robust cooperation amongst hence do wide range outreach how to use this data to the various stake holders – programmes aiming towards decrease the number of Crop from Union and State simplification of the policy Cutting Experiments (CCEs). Government, to banks to the clauses and conditions to the This will reduce the financial insurance companies is farming grassroots. This burden on states and further required to ensure would also help to build a insurance companies, that the third largest crop positive image about these improve efficiency and will insurance market after USA schemes which despite having enable timely claim and China, builds and a claims ratio of around 70% settlement. maintains a successful are often doubted for their business model. Such a reliability in protecting a Conclusion business model shall build up farmer’s financial interest. There needs to be a the farmer’s confidence in 4. Delayed Claims paradigm shift in how we look insurers and will then not Settlement: Claims are an at insurance in India, where limit itself to only crop insurance scheme’s moment it is historically viewed as an insurance. Rather, in the long of truth and a delay in claims investment rather than a risk term, this shall then cascade dissemination can cause mitigation tool. The lack of into cross selling of various sufficient discomfort. To awareness amongst the other offerings from the address the issue, the use of farmers or other consumers insurance companies, serving technology in claims in general, is rather a big as a greater tool for farmers assessment is being thought caveat of the financial against any financial off from a long time. A lot of education system of the uncertainties they might face. work is being done on remote country which triggers sensing technology, wherein awareness deficit on the Views expressed in this the crop health can be various financial tools and paper are author’s assessed to a great extent limits the opening of bank personal only and not of using NDVI (Normalized accounts and linking them the affiliating Difference Vegetation Index) with Unique IDs. It hence organisations signatures. Stakeholders becomes the collective Crop Insurance 448102 enuJ-lirpA lanruoJ IADRI Crop Insurance 458102 enuJ-lirpA lanruoJ IADRI Crop Insurance 468102 enuJ-lirpA lanruoJ IADRI Crop Insurance 478102 enuJ-lirpA lanruoJ IADRI Crop Insurance 488102 enuJ-lirpA lanruoJ IADRI Crop Insurance 498102 enuJ-lirpA lanruoJ IADRI Crop Insurance 508102 enuJ-lirpA lanruoJ IADRI Crop Insurance 518102 enuJ-lirpA lanruoJ IADRI Crop Insurance 528102 enuJ-lirpA lanruoJ IADRI Guidelines to the contributors of the Journal 1. The article must be original 6. The article must carry the 11. The articles go through blind contribution in the form of name(s) of the author(s), review and are assessed on the essay, research paper or case contact details such as e-mail, parameters such as (a) study of the author. full postal address, telephone / relevance and usefulness of the 2. The article must be an mobile number for article (b) organization of the exclusive contribution for the corresponding on the title page article (structuring, Journal and should not have only and nowhere else. sequencing, construction, flow, been published elsewhere in the 7. A brief write-up about the etc.), (c) depth of the same form. Author must also be sent. discussion, (d) persuasive strength of the article (idea/ 3. The article should ordinarily 8. All the referred material in the argument/articulation), (e) not exceed 2000 words. A article must be appropriately does the article say something longer article/research paper cited. The authors are advised new and is it thought may also be considered if the to follow American provoking, and (f) adequacy of subject so warrants. Psychological Association (APA) reference, source 4. General rules for formatting Style for referencing. acknowledgement and text are as under: 9. 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Crop Insurance 538102 enuJ-lirpA lanruoJ IADRI Policyholder Servicing Turn Around Times Policy Service Maximum Turn Around Time Processing of Proposal and communication of decisions including requirements/ issue of Policy/Cancellations 15 days Issuing copy of proposal form 30 days Response by the insurer on post policy issue service related requests such as change in address/nomination/ assignment of policy etc. 10 days LIFE INSURANCE Surrender value/Annuity/Pension processing 10 days Maturity Claim/Survival Benefit/Death claim without investigation 30 days Raising claim requirements after lodging the claim 15 days Death Claim Settlement / Repudiation with investigation requirements 6 months GENERAL INSURANCE Appointment of Surveyor 3 days Survey Report Submission 30 days Insurer seeking addendum report 15 days Offer of settlement/rejection of claim after receiving first / addendum survey report 30 days GRIEVANCES Acknowledging a Grievance 3 days Resolving a Grievance 15 days Crop Insurance 548102 enuJ-lirpA lanruoJ IADRI Some Important Insurance Related Websites Insurance Related Links 1 Insurance Regulatory and Development www.irdai.gov.in Authority of India (IRDAI) 2 IRDAI Consumer Education Website www.policyholder.gov.in 3 Insurance Information Bureau of India www.iib.gov.in 4 IRDAI Agency Licensing Portal www.irdaonline.org 5 Integrated Grievance Management System (IGMS) www.igms.irda.gov.in 6 Mobile Application to Compare ULIPs www.m.irda.gov.in Insurance Education Institutions 1 Institute of Insurance and Risk Management (IIRM) www.iirmworld.org.in 2 Insurance Institute of India (III) www.insuranceinstituteofindia.com 3 Institute of Actuaries of India (IAI) www.actuariesindia.org 4 National Insurance Academy (NIA) www.niapune.com International Links 1 International Association of Insurance Supervisors www.iaisweb.org 2 National Association of Insurance Commissioners www.naic.org 3 International Gateway for Financial Education www.financial-education.org Other Links 1 Governing Body of Insurance Council (GBIC) www.gbic.co.in 2 General Insurance Council www.gicouncil.in 3 Life Insurance Council www.lifeinscouncil.org 4 Insurance Brokers Association of India (IBAI) www.ibai.org Crop Insurance 558102 enuJ-lirpA lanruoJ IADRI Crop Insurance 56