Weather Indexed Contracts in India

The pioneering work on agricultural insurance by J S Chakravarty titled Agricultural Insurance: A Practical Scheme Suited to Indian Conditions (1920), was in fact a proposal for a rainfall insurance scheme to protect farmers against drought. He felt that a direct system of crop insurance (yield insurance), though desirable, would not be feasible in India given the associated problems of moral hazard, difficulty of arriving at a consensus on the basis of insurance (value of crop or its quantity) besides the inherent characteristics of Indian agriculture, namely, illiterate cultivators, inadequate village statistics and the general level of backwardness. According to him, given the extreme dependence of Indian agriculture on rainfall, it was practicable to introduce an “indirect system” of insurance in the form of rainfall or drought insurance which would also eliminate the problem of moral hazard and issues relating to the estimation of the crop yield or value. Suggesting an area approach as the only feasible one for the implementation of the scheme (as rain gauges cannot be set up in every field), the proposed scheme envisaged the following terms of a rain-insurance contract:

If the aggregate rainfall from the beginning of the agricultural year as measured at the rain gauge at the taluka headquarters up to a certain date is less than a certain amount, then a certain sum of money will be paid in respect of the insured field as compensation.

Though Chakravarty did not envisage a scheme of compulsory insurance, the key elements of the contract suggested by him, namely, a specified risk fixing date (periods of preparing land, sowing, maturing and harvesting), a specified degree of deficiency in rainfall (affecting crop yield) and a prescribed amount of compensation (indemnity to stabilise net farm income) have become the bedrock for the present models of rainfall/weather insurance. Surprisingly, despite the very promising concept, the same was not given a practical shape till a few years ago with the introduction of rainfall insurance and weather-based schemes in the country.

In India, weather-based insurance was first introduced in 2003 by ICICI Lombard with technical assistance from the World Bank for groundnut and castor farmers of Mahbubnagar district in Andhra Pradesh, a region characterised by low and uncertain rainfall, low levels of irrigation, and shallow and infertile soils. About 154 groundnut farmers and 76 castor-bean farmers participated in the scheme in 2003 followed by 430 farmers in the following year for an average sum insured of Rs 6,000. About 305 farmers of the 657 insured farmers in the first two years received payouts, with the average payout of approximately Rs 1,475. The company in conjunction with the Government of Rajasthan, also launched a weather insurance programme in 2004, insuring 783 orange farmers and 1,036 coriander farmers which was scaled up to include more crops and farmers in 2005.

IFCCO-Tokio, a joint venture insurance company, launched a similar weather insurance contract, selling more than 3,000 policies in 2004 and more than 16,000 in 2005. This was followed by the pilot rainfall insurance scheme in 2004-05 in Andhra Pradesh, Karnataka and Gujarat.

The early pilot schemes offered by the private sector were followed by the entry of the public sector insurer AIC into this market with the introduction of rainfall insurance (Varsha Bima) targeting three risks: inadequate rainfall over the entire cropping cycle; inadequate rainfall during critical stages of crop development; and sowing failure due to inadequate rainfall at the start of the season. Under the seasonal rainfall option, coverage is against adverse deviations of 20% and beyond in “actual rainfall” from “normal rainfall” for the entire season. The payout structure, designed on the basis of yield output elasticity is on a graded scale, corresponding to different degrees of adverse deviation in rainfall, with the full sum insured for adverse deviation beyond 80%. For rainfall distribution index option, coverage is against deviations of 20% and beyond in “actual rainfall index” from “normal rainfall index” for the entire season. Lastly, for sowing

August 21, 2010 vol xlv no 34

failure option, the coverage is against adverse deviations of 40% and beyond in “actual rainfall” from “normal rainfall” between 15 June and 15 August. Varsha Bima was piloted in 20 rain gauge areas spread over 27 districts of four states of Andhra Pradesh, Karnataka, Rajasthan and Uttar Pradesh in 2004-05. In the following year, the scheme was extended to 142 districts in 10 states viz, Andhra Pradesh, Gujarat, Maharashtra, Madhya Pradesh, Karnataka, Orissa, Tamil Nadu, Uttar Pradesh, Chhattisgarh and Uttarakhand. The coverage expanded from 1,100 farmers in 2004 to 1.25 lakh farmers in 2005 to more than seven lakh farmers in 2008.

In 2005, with technical inputs from the Coffee Board and Central Coffee Research Institute, AIC developed a product for coffee farmers in Karnataka, which was later on extended to the states of Tamil Nadu and Kerala. The scheme compensates the insured against the likelihood of diminished output resulting from shortfall/excess in the actual rainfall for different coverage options within a specific geographical location and specified time period. Since 2007, the Coffee Board has been extending 50% subsidy to the small growers (landholdings below 10 hectares). In its three years of implementation, around 30,000 farmers have been insured generating a total premium of around Rs 6 crore. The product has however resulted in high losses for the insurer with the indemnity payouts exceeding three times the premium received.

Another product introduced in 2005 targeted wheat in parts of Haryana and Punjab states, and used the remotely sensed Normalised Difference Vegetation Index (NDVI) as a proxy for crop health. However this has encountered problems due to cloud cover during critical crop growth periods. A generic product was also developed by AIC for protection against adverse temperature deviations and unseasonal rainfall in 2006 and implemented in Madhya Pradesh, Uttar Pradesh, Rajasthan and Maharashtra. The crops covered were potato, mustard, chickpea, barley and wheat. Similar index products have also been developed for about 30 different crops, including perennial horticultural crops such as cashew nut, grapes, mango and apple.

However, the coverage remained limited as all the above initiatives on the part of insurers suffered from lack of government support, making the premium rates seemingly unaffordable. This continued till 2007-08, when the Government of India for the first time earmarked Rs 100 crore for the implementation of the Weather-Based Crop Insurance Scheme (WBCIS) on a pilot basis in a few states as an alternative to NAIS. Piloted by the AIC in Karnataka in kharif 2007, weather-based schemes are presently being offered in selected regions for different crops by both the public and private sector insurers.

The WBCIS too operates on the concept of “area approach” (as adopted under NAIS), whereby each reference unit area (RUA) is linked to a reference weather station (RWS) and all farmers in a given RUA are deemed to have suffered the same level of adverse weather incidence. To the extent possible, such RUAs are restricted to 25 km radius around the RWS. The risk period under weather insurance products extends from sowing to harvesting and thus varies from crop to crop. The payout structure that defines the scale of payout for a given strike and exit in different covers is specific to a particular crop in a notified RUA. The WBCIS is based on actuarial rates of premium (with a cap at the rate of 8%-10% for food crops

Economic & Political Weekly

EPW

and oilseeds and 12% for commercial crops) but to make it affordable, the premium actually charged from farmers has been fixed “at par” with the NAIS. The scheme operates with upfront premium subsidy contribution by the government (central and state), with the same level of financial support to the public and private insurers, while the entire claims are borne by the insurers.

Analysis across Seasons, Regions and Crops

The coverage of the scheme has been increasing with every season with the expansion of the scheme to more number of notified areas and crops. Table 1 shows the season-wise progress of the scheme in the country. The scheme has, so far, insured about 21.5 lakh farmers covering approximately 29,000 hectares of land for an insurance value of around Rs 5,000 crore. The quantum jump in the expansion coverage has come in the latest kharif season which saw about 11.35 lakh farmers being insured, a jump of a lmost 600% from the previous year.

In terms of implementing states, Rajasthan has remained the dominant state, with 42% of the total farmers insured and

Table 1: Season-wise Coverage and Indemnity under WBCIS

FP = Farmers Premium, TP = Total Premium, TC=Total Claims, FB = Farmers Benefited. CNF: Claims not finalised. Source: Agriculture Insurance Company of India.

Table 2: State-wise Coverage under WBCIS (2007-09)

SI = Sum Insured, FP = Farmers Premium, TP = Total Premium. Source: Same as Table 1. Figures in parenthesis denote percentages to total.

accounting for almost half of the total area, liability and premium generated (Table 2). In fact, the state government promoted the scheme in a major way in the initial years and the scheme had covered about 5.8 lakh farmers contributing about Rs 129 crore of total premium (inclusive of subsidy) in rabi 2007-08. Bihar is the second most important state, covering about 30% of the total farmers insured. The other emerging states are Gujarat, Orissa and Maharashtra.

The crops notified under weather-based schemes are generally the ones growing under rain-fed conditions. Table 3 shows that wheat is the most important crop accounting for one-third of the total farmers insured, acreage and premium generated. Paddy and gram amongst the food crops and cumin amongst the annual commercial and horticulture crops (ACH) are the other important crops in terms of coverage. Surprisingly, though the ACH crops offer a distinct advantage in terms of applicable premium rates,

FI= Farmers Insured, SI = Sum Insured, FP = Farmers Premium, TP = Total Premium, TC=Total Claims, FB = Farmers Benefited, LC=Loss Cost (Claims/Sum Insured *100), CR = Claim Ratio (Claims/

Sustainability and Equity

Though both the area and weather-based schemes are based on area approach, are compulsory for the borrowing farmers and flat premium rates are applicable for FCOS, there are important differences in the coverage parameters (besides the technical differences) that are often missed out in the general comparison of the two schemes.

First, while the NAIS covers the loan disbursed, the latter covers the loan sanctioned. In other words, while NAIS compulsorily the entire loan amount, irrespective of the quantum is insured. For the non-loanee farmers, there are two limits; one till the value of guaranteed yield, on which flat premium rates are applicable and an upper limit which is the value of 150% of the value of average yield, with the actuarial rates becoming applicable on the liability amount exceeding the first limit. Thus, unlike the NAIS, WBCIS offers no flexibility to farmers; for seeking higher or lower coverage. This becomes difficult for farmers borrowing a loan amount less than the sum insured limits, as the scheme provides for charging of premium on the entire pre-fixed sum insured. On the other hand, farmers who borrow a higher quantum receive only partial insurance.

Third, while the premium rates are fixed at par for FCOS, the insured farmers have to pay an additional component of applicable service tax under the WBCIS. Also, the premium payable for the ACH crops under the WBCIS is capped at a maximum of 6%. Thus, while the NAIS, being exempted from service tax makes it advantageous for FCOS; the absence of any upper limit on premium rates under ACH crops makes it distinctly disadvantageous in comparison to WBCIS.

Fourth, as per the applicable franchise limit under the WBCIS, any loss exceeding 5% of the sum insured is compensated. Under the NAIS, the present levels of indemnity are 60%, 80% or 90% corresponding to high, medium and low risk crops.6 Needless to say, with guaranteed yield at 60% (of the average yield of 3-5 years), the small and medium intensity adversities fail to c ompensate the farmers. The farmers also do not have the flexibility of choosing a higher indemnity even by paying a higher

August 21, 2010 vol xlv no 34

premium. The guaranteed yield being the moving average (multiplied by the indemnity level) also fails to provide protection to the farmers in areas where there has been a consecutive adverse season. While weather insurance scores heavily over the traditional scheme in adequately reflecting yield losses in such cases, it fails to adequately compensate the catastrophic losses (given that the total sum insured is divided into various covers and only an adverse deviation in all the parameters would result in the insured receiving a substantial proportion of the total liability).

Weather Insurance: A Sustainable Insurance Programme?

Worldwide, the public sector has had a bleak history of providing crop insurance. Hazell, Pomereda and Valdes (1986) cite numerous problems that have plagued public crop insurance programmes and point out that these programmes cannot be sustained without continual subsidies. It is also contended that the associated costs of providing crop insurance schemes have historically outweighed the gains from risk spreading and that crop insurance is uninsurable and unsustainable in the long run as the

WBCIS: More Equitable Distribution of Benefits?

An argument often made in favour of the yield insurance scheme is its extremely high claim ratio which in a few seasons has exceeded five times the premium paid by the farmers. In terms of

Table 5: Season-wise Proportion of Farmers Benefited and Covered under NAIS and WBCIS

Source: Same as Table 1.

claims ratio, the traditional product is seen faring better than the weather-based schemes in a majority of the seasons (Table 5). The high claim ratio is often taken as an indicator of the higher

Table 6 shows the state-wise coverage and indemnity under both the schemes. The per-farmer sum insured is noticeably higher under the WBCIS than under NAIS across all states, Tamil Nadu being the only exception. The reason has to do with the inflexible sum insured limits applicable for both the borrowing and nonborrowing farmers. The proportion of farmers benefited vis-à-vis insured is much higher under the WBCIS than under NAIS across

Indemnity Payouts in Karnataka

It is to be understood that despite the several advantages that the product offers to the insured, the state governments understandably remain sceptical whether a product covering just parametric weather events can be comparable (let alone superior) to the multi-peril yield insurance. The apprehensions also relate to the

WBCIS % Farmers Benefited NAIS % Farmers Benefited

Kharif 2007 Kharif 2008 Rabi 2007-08 Rabi 2008-09 Total Season

trend has been similar in all the seasons with the excep-

tion of rabi 2007-08. Overall, while 44% of the farmers

insured received indemnity payouts under the WBCIS,

only 25% of them benefited from the NAIS (Figure 3).

Thus the claims under the NAIS are given to a smaller

proportion of beneficiaries and the higher per-farmer

fact that basis risk and poor design of the weather index may result in “no claims” despite crop losses at the individual farmers’ level. These factors have contributed to the inadequate geographical spread of the weather products.

The comparative analysis in the above section is, however, based only on the aggregate experience; and the evaluation of the two schemes would require a disaggregated area-wise analysis. Here we present the analytical results of coverage and claims experience under both the schemes in Karnataka, where the pilot WBCIS was launched in 2007. The analysis includes all the three seasons during which WBCIS was implemented covering only those crop hoblis (circles) where both the schemes operated7 in the respective seasons. To facilitate the comparative analysis, per hectare indemnity payouts under both the schemes are calculated. While the payouts under WBCIS are as per the claims structures and the associated trigger levels, the per hectare claims under NAIS are arrived at by applying the shortfall percentage to the WBCIS sum insured limits. In other words, the per hectare NAIS claims are the product of percentage of shortfall in yield and the per hectare sum insured limits applicable under the WBCIS.

Table 7 shows that about 96%, 88% and 97% of the areas during kharif 2007, kharif 2008 and rabi 2008-09 did not produce claims under the NAIS. On the other hand 44%, 62% and 51% of Table 7: Comparison of Claims Experience under NAIS and WBCIS

Kharif 2007 Kharif 2008 Rabi 2008-09

WBCIS NAIS WBCIS NAIS WBCIS NAIS

No of Notified Crop Hoblis 140 148 57

No of claim producing areas 62 6 83 29 29 2

% claim producing areas 44 4 62 22 51 3.5

No of zero claim producing areas 77 134 59 112 55 29

% zero claim producing areas 56 96 38 88 49 96.5

Average claims (per ha) 641 85 916 916 834 204

Claim settlement period 30/1/08 2/7/09 & 20/01/09 5/08/2009 01/10/09 19/01/ 23/7/09 2010

Source: Same as Table 1.

all the notified areas received payouts under the WBCIS. The a reas with no payouts were thus significantly lower under the WBCIS. The WBCIS also seems to be faring better than the yieldbased schemes as far the quantum and the average claims are concerned. Thus even the micro experience in Karnataka reveals the highly concentrated claims under NAIS and the reasonably even spread under WBCIS.

Given that the payout triggers under the WBCIS are ideally fixed to reflect the yield losses, the per hectare claims under both the schemes should ideally be comparable to each other. Surprisingly, the analysis proves otherwise. The per hectare claims u nder both the schemes not only show a negative correlation, but in several instances, where NAIS claims are seen to be exceptionally high, there is nil/negligible payouts under the WBCIS (Appendix I, p 81). A major drawback of the NAIS as revealed by earlier studies (James and Nair 2009; GoI 2004; World Bank 2007) is the highly biased settlement of claims for certain areas and crops. Given that the spread of benefits under the weather-based scheme appears to be more even, the tinkering of yield data cannot be ruled out. Yet, concrete conclusions cannot be drawn in this regard u nless the exact cause of crop loss is known.

Economic & Political Weekly

EPW

Though the data gaps and timely non-receipt of weather data do result in significant delays in the settlement of claims, these products still fare much better than the traditional scheme. In any case, the farmers receive indemnity payouts before the advent of the next season and on an average six to seven months earlier than the NAIS. On the other hand, the extremely delayed payouts under the yield insurance scheme provide limited insurance against consumption fluctuations.

Basis Risk: How Does the WBCIS Fare?

Because farm-level yields are not perfectly correlated with the area average yield, the insured farmers are exposed to basis risk. Thus, an insured may experience production losses on his/her farm and yet not receive an indemnity or may receive an indemnity triggered by a shortfall in the unit area average yield despite not experiencing individual losses. High basis risk is one of the greatest drawbacks of the NAIS, as most of the implementing states continue to notify larger insurance units.

The WBCIS was expected to bring about a significant improvement in this regard. While the basis risk associated with weather could be high for rainfall, it was expected to be moderate for many of the other weather parameters. Yet, the potential for a mismatch between parametric insurance claims settlement and the actual losses exists since these payouts too are not based on individual loss adjustments, but determined according to the measurement of the index. Experience with the implementation of the WBCIS has shown that there are various potential sources of basis risk under WBCIS too – the weather station being used for the contract may be too far from the insured; losses may be caused by factors other than the weather variable on which the index is based on, the management by the individual farm operator can be significantly different than the conditions that are imposed in a crop growth model, the poor design of the product resulting in its failure to establish accurate correlation between productivity levels and weather variations, etc. Given all the above, it is possible for the policyholder to experience a loss and yet receive no index insurance indemnity or receive indemnity despite no perceptible loss of yield, diametrically revealed by quadrants II and III, respectively in Figure 4. Alternatively, a perfectly correlated index along with low basis risk would result in a situation shown by quadrants I and IV.

Figure 4: Basis Risk in WBCIS

Presently, weather data is often recorded at the taluka/ district level though rain gauge stations (RGSs) provided rainfall data do exist for lower insurance units. Given the high spatial variation in India, if RWS is located more than five to seven kms away from the farm location, the quantum and time of the weather parameter at the farm location may significantly vary from that of a RWS. Also, the capping of premium rates under the weather-based crop insurance scheme does not augur well for the usefulness of the product from the farmers’ point of view since it is most likely to come at the expense of payouts.

Thus, a critical element for decreasing the basis risk associated with weather insurance would be to make necessary investments in weather stations so as to ensure accurate and tamper-proof measurements. Also, a high correlation between the index and the individual’s risk is important for reducing basis risk. A continuous research on the climatic variations and suitable modifications in the product structure to adequately reflect the weatheryield relationship is also critical to reduce basis risk associated with this promising product.

Conclusions

The growing popularity of weather-based schemes reflected in the large and growing number of insured in such a short span of time is indeed impressive. In contrast to the traditional yield insurance scheme, weather insurance is market-based and financially sustainable with the potential to transform the lives of poor agricultural households by addressing output risk – one of the most important obstacles for agrarian households. The benefits enumerated in the analysis, viz, the even spread of benefits, the more advantageous premium regime for commercial and horticulture crops, and most importantly the swift payouts makes the product more advantageous to the vulnerable sections of the farming community as it prevents them from falling into a debt trap or having to pay high interest rates on moneylender loans. A full-fledged analysis of the scheme after a couple of years after it is implemented on a wider scale would show more concrete results.

Yet, there are major issues and constraints associated with weather index products that need to be successfully addressed. Foremost among the risks faced by this promising innovation is the likelihood of insurers continuing to implement the product with high basis risk, so long as it is profitable. There is therefore an urgent need to bring down basis risk arising from insufficient network and spread of weather stations. A hitherto active role of the government, in expanding the network of tamperproof weather stations appears inevitable considering the huge start-up costs. Also, government-owned weather stations would instil greater confidence amongst the insured regarding data transparency and security. Second, for WBCIS to emerge as an effective risk mitigation tool, the product should be suitably designed to cover catastrophic losses. Third, the successful introduction of weather index insurance would also require a significant educational effort. Presently, the claim structures are highly technical and complicated and even the insured farmers seldom have any knowledge of the various covers as also the extent of weather deviation that would result in claims. Adding

4GUGCTEJ 6JGOGU 9JQ ECP CRRN[

Ŗ 'ZVGTPCN %CRKVCN (NQYU CPF (KPCPEKPI HQT GXGNQROGPV Ŗ 4GUGCTEJGTU YJQ CTG EKVK\GPU CPF RGTOCPGPV TGUKFGPVU QH Ŗ QOGUVKE 4GUQWTEG /QDKNK\CVKQP CPF (KPCPEKCN 5GEVQT GXGNQROGPV FGXGNQRKPI CPF VTCPUKVKQP EQWPVTKGU Ŗ +PPQXCVKXG 5QWTEGU QH GXGNQROGPV (KPCPEG Ŗ 4GUGCTEJGTU YJQ CTG EKVK\GPU QH FGXGNQRKPI VTCPUKVKQP EQWPVTKGU YJQ CTG VGORQTCTKN[ DCUGF KP C FGXGNQRGF EQWPVT[
HQT OQTG VJCP ſXG [GCTU %CVGIQTKGU Ŗ 4GUGCTEJGTU YJQ CTG NGUU VJCP [GCTU QNF CU QP VJ 1EVQDGT Ŗ 5VCHH QH DKNCVGTCNOWNVKNCVGTCN CIGPEKGU ECPPQV CRRN[

(QT QTICPK\CVKQP TGUGCTEJGT
U YJQUG RTQRQUCN VQ EQPFWEV TGUGCTEJ QP Ŗ GXGNQROGPV RTQLGEVU DGKPI KORNGOGPVGF KP #HTKEC #UKC /KFFNG CP[ QPG QH VJG VJTGG TGUGCTEJ VJGOGU JQNFU VJG ITGCVGUV RTQOKUG HQT 'CUV .CVKP #OGTKEC CPF VJG %CTKDDGCP 2CEKſE +UNCPFU CPF VJG KORTQXKPI QWT WPFGTUVCPFKPI QH VJG TGNGXCPV KUUWG VTCPUKVKQP GEQPQOKGU QH 'CUVGTP 'WTQRG CPF %GPVTCN #UKC (KTUV 2TK\G 75 5GEQPF 2TK\G 75 Ŗ 2TQLGEVU DGKPI OCPCIGF D[ CP 0)1 JGCFSWCTVGTGF KP QPG QH VJG GCFNKPG VJ 1EVQDGT CDQXG TGIKQPU

Ŗ 2TQLGEVU KORNGOGPVGF DGHQTG VJ ,WPG /GFCNU HQT 4GUGCTEJ QP GXGNQROGPV Ŗ (KPCNKUV RTQLGEVU HTQO RTGXKQWU VJTGG [GCTU ECPPQV CRRN[ (QT EQORNGVGF TGUGCTEJ YQTM VJCV FGOQPUVTCVGU CECFGOKE GZEGNNGPEG Ŗ 2TQLGEVU VJCV CRRNKGF RTGXKQWUN[
PQV ſPCNKUVU OC[ TGCRRN[ YKVJ QP VJG VJGOGU URGEKſGF CDQXG WRFCVGU

,CRCPGUG #YCTF HQT /QUV +PPQXCVKXG GXGNQROGPV 2TQLGEV

(QT RTQLGEVU CPFQT QTICPK\CVKQPU VJCV CTG ECTT[KPI QWV QTKIKPCN CPF KPPQXCVKXG YQTM KP VJG ſGNF 6JGUG RTQLGEVU CTG PQV VJGOG DCUGF

#RRN[QPNKPGCVJVVRQRCIFPGVQTI(WTVJGTFGVCKNUIWKFGNKPGU(#3UCVYYYIFPGVQTI`CYCTFU

August 21, 2010 vol xlv no 34

to the confusion is the fact that similar products (with identical Finally, there should be an in-depth research (on a continuous premium rates but different trigger values) are also being basis) of the associated weather risks for various crops grown in offered by the private insurers, often in the same area/crop, the country, preferably funded by the government so as to prowith the insured having little understanding that the payouts vide the insurers with the requisite technical inputs in developmay widely vary for the same crop in the same area. Lack of ing appropriate risk covers. The need of the hour is therefore to sufficient awareness and understanding of the product address the above challenges that would enable this promising specificities could prevent farmers from buying an otherwise product to emerge as an effective risk mitigation tool for farmers attractive product. in diverse parts of the country.

Notes Appendix I: Season-wise State-wise Crops Insured under WBCIS

Haryana Barley, Tomato, Potato

Karnataka Jowar (I UI), Bengalgram (I UI), Potato, Grape, Mango

Kerala Paddy, Mango, Cashewnut

Rajasthan Barley, Bengalgram, Coriander, Cumin, Fenugreek, Isabgul, Mustard, Wheat

Tamil Nadu Maize, Paddy, Groundnut, Sesamum, Sunflower, Tomato, Onion, Chilly, Cotton, Mango

West Bengal Paddy, Mustard, Wheat

Appendix II: Areas with High NAIS Payouts and Nil WBCIS Payouts

NAIS-WBCIS Claims Comparison – Kharif 2008

Season District Hobli Crop Per Hec Claims WBCIS Per Hec Claims NAIS (Rs) (Rs)

Kharif 2007 Haveri Hanagal Maize (Rainfed) 0 2,283

Kharif 2007 Haveri Shiggaon Jowar (Rainfed) 0 6,136

Kharif 2008 Gulbarga Aland Blackgram (Rainfed) 0 5,285

Kharif 2008 Bijapur Sindagi Greengram (Rainfed) 0 5,612

Kharif 2008 Gadag Ron Greengram (Rainfed) 0 5,667

Kharif 2008 Haveri Ranebennur Greengram (Rainfed) 0 3,325

Kharif 2008 Gulbarga Aland Groundnut (Rainfed) 0 3,134

Kharif 2008 Tumkur Pavagada Groundnut (Rainfed) 0 4,695

Kharif 2008 Chitradurga Chitradurga Maize (Rainfed) 0 4,418

Kharif 2008 Haveri Shiggaon Maize (Rainfed) 0 6,417

Kharif 2008 Haveri Shiggaon Ragi (Rainfed) 0 6,326

Rabi 2008-09 Chitradurga Chitradurga Jowar (Rainfed) 0 10,000

Better Risk Sharing”, Report No 39353. Source: AIC Economic Political Weekly

EPW

August 21, 2010 vol xlv no 34