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Crop Insurance in India

Where Do We Stand?

Meenakshi Rajeev (meenakshirajeev2000@gmail.com) teaches and Pranav Nagendran (mn1612@gmail.com) is a research assistant at the Institute for Social and Economic Change, Bengaluru.

 

Crop insurance is a vital component of agriculture, especially in a country such as India, where the majority of farmers are small and marginal with low savings that reduces their ability to weather agricultural risks and fluctuations. Programmes extending insurance cover for crops in India have long been in operation, but have not been able to include the majority of the agricultural sector within their ambit. Analysing the 70th Round Situation Assessment Survey data, collected by the National Sample Survey Office, the performance of crop insurance at the household level is examined and factors that determine its adoption are identified using an econometric analysis. The Pradhan Mantri Fasal Bima Yojana is then analysed by looking more closely at the structure of the scheme.

We thank the Reserve Bank of India and the Indian Council of Social Science Research for their support to the Institute for Social and Economic Change, Bengaluru. An earlier version of this paper was presented at the ISEC–SASS conference held at ISEC in 2018.

The latter half of 2018 witnessed widespread protests by farmers in Delhi and other important centres of the country. These protests were aimed at pressing the government to find solutions to the ongoing agricultural crisis that is characterised by inadequate farm incomes. This situation has been exacerbated by drought and water shortages in several parts of the country. Farmers are not able to repay their debts, leading to distress and in some cases, suicides. While low income is an issue, the fluctuation in incomes is even more distressing. Furthermore, it is likely that such concerns will be more frequent in future due to climate change. It has been stated by scholars that weather-based risks to agriculture have increased in the recent years, notably in the form of climate change (Lobell and Field 2007; Rosenzweig et al 2013). Developing regions that depend on farm incomes are more vulnerable to these systemic changes (Rosenzweig and Parry 1994; Fischer et al 2005).

Farmers in developing nations such as India have to incorporate these risks into their production arrangements and many are ill-equipped to do so (Rajeev et al 2016). Here, the role of crop insurance is important. Crop insurance schemes are required to bridge the gap between income and consumption requirements during periods of crop losses or crop failures.

The government is a vital agent in this process, as it provides the necessary legal framework and incorporates the crop insurance scheme as a part of the national agriculture policy, as well as co-finances the agriculture premium and makes the terms of availing insurance reasonable to the farmer. Such measures, researchers argue, ensure higher market penetration and stability of the insurance product (Herbold 2010). The Indian government has been facilitating such insurance schemes since 1985 (Venkatesh 2008). However, the system has largely been unable to significantly expand coverage and consequently many farmers in the country continue to be exposed to several agricultural risks over the course of time.1 A new crop insurance scheme has been introduced, namely the Pradhan Mantri Fasal Bima Yojana (PMFBY) that has shown some early promise. As of 2016–17, the data reveal that about 29% of the cultivated land in the country has come under the ambit of crop insurance, a marked improvement from the figures witnessed in the previous years.

While the PMFBY has improved insurance coverage, it is still far from satisfactory and has not been able to reach its target coverage of 50% of the gross cropped area (Bhati 2019). This leads to the need for a study of the determinants of crop insurance adoption and an examination of concerns that continue to persist.

These aspects are analysed using secondary data from the National Sample Survey Office (NSSO) and other government sources and this is supplemented by some insights from field visits to the different parts of India. The results from the analysis show that farmers operating in more risky conditions (such as in rainfed areas with low irrigation) tend to opt for crop insurance. Poorer and marginalised farmers show lower insurance adoption indicating the lack of financial capacity and illiteracy as some of the major reasons. The field-level experiences further reveal that the damage assessment mechanism is presently not farmer-friendly enough, and that it is in need of improvement. This paper, thus, arrives at certain policy implications based on field-level experiences as well as evidence from certain countries that have relatively more successful agricultural insurance programmes.

Background

Crop losses due to various reasons, particularly because of extreme weather situations, are a common phenomenon and can prove to be catastrophic in the numerous rainfed fields of backward regions. It is against this backdrop that the development of sustainable mitigation strategies, one of them being crop insurance, comes into the picture. In this context, it is worth noting that agricultural insurance is underdeveloped in developing countries in general. For high income countries, agriculture insurance premium as a percent of agriculture gross domestic product (GDP) amounts to 1.99%, while for lower-middle income countries, this is 0.16% and in low-income countries, it accounts for a mere 0.01% (Mahul and Stutley 2010). However, between the 1950s and the 1980s, there was a major growth in public sector Multiple Peril Crop Insurance (MPCI) programmes in Latin America (for example, Brazil, Costa Rica, and Mexico) and also in Asia (for example, in India and the Philippines) (see Barnett 2014 for a detailed discussion). These were often linked to seasonal production credit programmes for small farmers. Similar public programmes were implemented in Europe (for example, in Portugal and Spain) and the former Soviet Union as well (Barnett 2014). Since the 1990s, the poor performance of most public sector schemes and their limited uptake among farmers have led many governments to promote agricultural insurance through the private commercial sector, often backed by government financial support under public–private partnerships (Mahul and Stutley 2010).

In India, interest in crop insurance has long been kindled, and can be traced back to as far as 1920, to S Chakravarti who proposed a rainfall-based agricultural insurance scheme (Mishra 1995), relying on his studies conducted in the then Mysore state (Vyas and Singh 2006). Following independence, agriculture became a key area of focus for the Indian government, and owing to the risks faced by farmers, the then incumbent Minister of Food and Agriculture in India, Rajendra Prasad, in 1947, had provided farmers with an assurance that the government would consider the feasibility of introducing insurance schemes for crops and cattle. Although an officer on special duty was appointed in 1948 for this purpose, and two pilot schemes were prepared, the paucity of funds among states precluded them from adopting a crop insurance scheme (Dandekar 1976).

The affirmation of crop insurance into public policy materialised almost two decades after this, when, in 1965, a draft Bill and Model Scheme of Crop Insurance was introduced so as to allow states to easily adopt them, if they opted to. When referred to the Expert Committee on Crop Insurance chaired by Dharam Narain in 1970, however, it was found that crop insurance was unadvisable for adoption in India (Dandekar 1976; Vyas and Singh 2006), owing to the impracticability of the individual-based coverage that it suggested.

In 1976, V M Dandekar provided an alternative to this by suggesting an area-based approach to insuring farmers, which would consider regional output and compare it to the average regional production for indemnity payment. Indemnities, too, were suggested to be uniformly paid to all farmers in regions that suffered losses. Such a system was postulated to avoid the previously identified issues of an individual-based approach, which is impractical in the case of a large farmer population and may lead to considerable moral hazard (Dandekar 1976). To ensure that the scheme was as accurate as possible, it was further suggested that the regions should be delineated such that each zone was agro-climatically homogeneous, so that each farmer’s experience closely mirrors that faced by the region as a whole. It was here that the crop cutting experiments, conducted to estimate crop output for computing national income, were suggested as a useful method of gathering information on regional output and yield (Dandekar 1976).

The suggestions of Dandekar were submitted to the General Insurance Company in 1976, and following a pilot study in some selected districts (Dandekar 1985), the Comprehensive Crop Insurance Scheme (CCIS) was introduced for the kharif season from 1985 for the entire country.2 Under this scheme, farmers were provided financial compensation in the event of crop failure arising from natural calamities such as floods and droughts among others. Credit eligibility was also restored following crop failures for the successive cropping season. Rice, wheat, millets, pulses and oilseed crops were eligible for insurance under the CCIS (Prabhu and Ramachandran 1986). Premiums were uniform for all farmers (approximately 2% in most cases), and indemnities were paid according to the suggestions of Dandekar (Prabhu and Ramachandran 1986). The CCIS continued to operate until 1999, when it was superseded by the Rashtriya Krishi Bima Yojana (RKBY), also known as the National Agricultural Insurance Scheme (NAIS) (Venkatesh 2008).

The NAIS made crop insurance mandatory for farmers who received a loan for cropping activity and cultivated certain designated crops in particular areas of each state (Vyas and Singh 2006), and in these cases, the insurance cover equalled the amount of the loan. Crop insurance could also be availed voluntarily for up to 150% of a threshold yield, the yield below which indemnity is paid as per the area-based approach (Venkatesh 2008). The risks covered under the NAIS were expanded to include fire and lightning; storms, cyclones, hailstorm, typhoon, tempest, hurricanes, and tornadoes; floods, inundation, and landslides; droughts and dry spells; and losses arising from pests/diseases (Venkatesh 2008). The coverage of crops was the same as that under the CCIS, with the added inclusion of a few major horticultural crops such as onions and potatoes. The premium rates under the NAIS were 3.5% of the sum insured for bajra and oilseeds, 2.5% for kharif crops, 2% for rabi crops except wheat, and 1.5% for wheat.3 The scheme was implemented by the newly formed Agriculture Insurance Company of India (Vyas and Singh 2006), which was supported by National Bank for Agriculture and Rural Development (NABARD) and the general insurance companies. By 2016, two other crop insurance schemes in addition to the NAIS were introduced. The Modified National Agriculture Insurance Scheme was introduced in 2010–11 based on the recommendations of a specially constituted Joint Group to improve farmer-friendliness and ease of access.4 The Weather-based Crop Insurance Scheme (WBCIS) was introduced on a pilot basis in 2006–07 and later expanded. This scheme uses weather-based thresholds rather than crop yields to compensate cultivators for deemed losses.5

Thus, by the time the data for the 70th Round of the NSSO’s Situation Assessment Survey of Agricultural Households (SAS) was collected in 2012–13, it was evident that the country had ample experience on the subject of crop insurance. The unit record data from the SAS is analysed in detail to understand the problems in its operation. This survey was conducted in two visits. The first visit (visit-1) was used to collect data on farm households for the period July–December 2012, and the subsequent visit (visit-2) followed up on the same households and collected information for the period January–June 2013. In the first visit, 35,200 agricultural households were sampled and interviewed. For the second visit, however, only 34,907 of these households could be revisited. The descriptive analysis undertaken here compiles information from both visits to provide a year-round picture of crop insurance in the Indian agricultural sector.

Performance of Crop Insurance

Agricultural insurance is necessary in India, where a plethora of risks act simultaneously on farm incomes. However, according to the 59th Round NSSO SAS (2002–03) data, only 4% of farm households had insured a crop at any time. The 70th Round SAS reveals that around 7% had a major crop they cultivated insured, which is an improvement, but an increase of only about 3% over a decade. This performance is on account of several reasons that are analysed in detail.

Farmers with smaller landholdings have significantly lower incomes than large farmers. In the case of marginal and small farmers, it is also evident that during the survey period, expenditures were greater than income. Fluctuations in incomes brought on by crop losses and failures can therefore have a much more serious impact on small and marginal farmers than on larger farmers since the latter may have higher savings and consequently, better ability to weather crop fluctuations. In this context, it is worrying to see that it is the small and marginal farmers that are the least likely to be insured (Table 1).

Absence of awareness is one of the major factors behind a lack of insurance adoption in India, as evidenced from the data in Figure 1. Interestingly, however, around a fifth of the farmers opined that they were not interested in insurance facilities or not satisfied with the terms and conditions indicating possible problems with the formulation of the scheme.

Although larger farmers were more likely to lack interest in the scheme (Appendix TableA1, p 35), they had greater insurance coverage than others (Table 1). Observing from the NSSO data that the rate of adoption of crop insurance is low, it is useful to identify the factors responsible for this situation. If certain segments of the population have lower access, then it is necessary to provide focused attention to these segments. In order to test this, an econometric exercise is carried out based on the 70th Round NSSO SAS household level data. Although the survey was carried out some years ago (in 2013), the findings can be used to discern some of the factors responsible for lower adoption of crop insurance, such as regional or social influences, that may continue to be important even in the recent context.

Factors Affecting Adoption of Crop Insurance

The SAS data set provides information on whether a farmer household has insurance cover for each of the four major crops cultivated. By compiling visit-1 and visit-2 data, one can ascertain the percentage of farm households that had at least one major crop insured between July 2012 and June 2013, which is the dependent variable in the regression analysis.

The Probit Model

Crop insurance adoption is a binary variable, which indicates whether or not a household has insured a major crop. Such variables typically take values equal to 0 or 1. Employing a normal linear regression in this context can potentially lead to estimates of the dependent variable outside the range [0,1]. Instead, for correctness, the binary outcome variable can be fitted to a normal distribution, and it is assumed that the outcome (whether or not a household has crop insurance) is a normalised function of the independent regressors.

The model presumes the existence of a continuous latent variable Y* which is a linear function of the explanatory variables (Woolridge 2012). In terms of notation, Y* assumes the following form

Yi* = Xi β + εi …(1)

And the observed dependent variable y (adoption of crop insurance) assumes the value 0 and 1 according to the rules given below:

1 if Yi* > 0

Yi = { …(2)

0 otherwise

It is also assumed that ԑi ̴ N (0, σ2), that is, the error terms are independently and identically distributed normally distributed random variables.

Now we have:

 

 

 =

 

 

... (3)

Similarly,

Where φ (.) is the standard normal probability distribution function.

The probit model parameters are estimated by the maximum likelihood method, where the likelihood function is given below (Woolridge 2012).

L

The log likelihood function is obtained by logarithmically transforming both sides of the equation.

In the above formulation, it is assumed that there are m outcomes with Y = 1 and the remaining n-m outcomes have
Y = 0.

One can even obtain the predicted probabilities using the maximum likelihood form. This is the procedure that is used to estimate the coefficients of the independent regressors in the probit regression.

Independent Variables

The factors that may affect the adoption of crop insurance include the following:

Regional factors: Infrastructure such as road connectivity, electricity and so on can influence whether or not farmer households have access to banks for getting crops insured. In our regression, we have controlled for these regional factors by dividing India into six regions, namely North (including Jammu and Kashmir, Himachal Pradesh, Punjab, Uttarakhand, Uttar Pradesh, Haryana, Delhi, and Chandigarh), North-East (Assam, Sikkim, Nagaland, Meghalaya, Manipur, Mizoram, Tripura, and Arunachal Pradesh), East (including Bihar, Odisha, Jharkhand, West Bengal, and the Andaman and Nicobar Islands), West (Rajasthan, Gujarat, Goa, Maharashtra, Dadra and Nagar Haveli, and Daman and Diu), South (Andhra Pradesh, Karnataka, Kerala, Tamil Nadu, Telangana, Pondicherry, and Lakshadweep), and Central (Madhya Pradesh and Chhattisgarh).

Crops grown: Some crops may be riskier than others, and farmers cultivating certain crops may be more likely to adopt crop insurance. A dummy variable is included, that equates to 1 if cereals are one of the major crops of a farm household and 0 if not to capture this effect.

Household monthly per capita expenditure: Richer households may either be less likely to require crop insurance as they may choose to bear crop income fluctuations with accumulated savings, or else more likely to be insured as they are in a relatively better position to pay premiums. Here monthly per capita expenditure (MPCE) is used as a proxy for income, a common practice followed by many scholars.

Land cultivated: Smaller farmers may need and adopt insurance because they are less likely to be able to sustain crop losses, while on the other hand larger farmers may be more likely to avail insurance owing to better awareness, accessibility, and ability to pay premiums. The size of land cultivated is included as an independent regressor to capture this effect.

Education: Better educated households may be more likely to avail crop insurance due to financial literacy, and to analyse this, two dummy variables are used. In the first (primary/middle), the value of 1 is assigned if at least one household member had primary/middle school level of education, and 0 otherwise, and for the other (secondary) the value of 1 is assigned if at least one member of the household has secondary or better education, and 0 otherwise.

Household activities: Households that carry out alternative activities other than agriculture such as livestock, or wage/salaried work provide alternative sources of income and thereby are less likely to choose crop insurance. Dummy variables have been included to capture this effect.

Access to technical advice: Households that have accessed advice from agricultural extension agents, Krishi Vignyan Kendras, attended agricultural universities, had been contacted by non-governmental organisations (NGOs) or had received any other such advice from similar sources may have been better informed of crop insurance and its benefits, and therefore are more likely to have availed it. It has already been observed that lack of information is the main reason for non-adoption of crop insurance (Figure 1). A binary variable is used to capture this effect.

Social factors: Social factors such as caste and religion may create differences in households’ access to insurance, and the effect of this is captured through binary variables.

Expenditure on crop protection and productivity enhancement such as on plant protection chemicals, fertilisers, and irrigation may reduce the need for crop insurance, and per-hectare expenditure on these items are included as independent variables in the following analysis.

Results

The results of the probit regression are presented in Table 2, from which several important observations can be made. First, that those cultivating cereals are more likely to receive crop insurance. This is possibly because cereals are relatively more risky or more water intensive when compared to other crops. Region-wise, the north region appears to have significantly lower crop insurance than the East, South, West, and Central regions, but higher adoption of crop insurance than the North East region. Thus, regions such as the North East and the North need special attention.

Farmers with more land are more likely to have crop insurance, indicating the role of economic status and also that increasing the adoption of crop insurance among small and marginal farmers is a necessary first step towards stabilising their incomes and giving them greater resilience to agricultural risks. Small and marginal farmers are often found among the poorer households, and are more in need of insurance as they are more harmed by crop loss and crop failure, and a targeted policy improving insurance adoption among this group is necessary. Households having livestock income were more likely to have availed crop insurance than households that did not, possibly due to having additional income for supporting crop insurance premiums. On the other hand, this was not replicated in the case of those with members engaged in salaried professions as they have a relatively more assured and regular income.

Access to technical advice appears to have improved the adoption of crop insurance, and increased awareness generated by such advice appears to have been beneficial. Thus, strengthening agricultural extension services is important for enhancing the adoption of crop insurance. Education is positively correlated with financial literacy, plays an important role as well, and better educated households (with members having primary/middle or secondary/better education) are significantly more likely to have availed crop insurance for a major crop cultivated. Certain socially deprived classes such as Scheduled Castes/Scheduled Tribes (SCs/STs) or religious minorities have lower adoption to crop insurance, and these categories need the special attention of policymakers as these are also the groups with lower income, education and financial literacy.

These results point to the fact that it is the poorer and marginalised communities of India that need greater attention of policymakers in terms of increased crop insurance cover. However, low coverage of crop insurance also points towards the need for improving the design of the scheme.

Pradhan Mantri Fasal Bima Yojana

The years following the NSSO study have witnessed farmers experiencing a greater level of attention in terms of crop insurance. The share of cultivated land under crop insurance cover has been improving. It was 22% during 2013–14 and increased to 29% by 2016–17, even though there has been a decline subsequently (Figure 2).

Figure 3 gives a picture of the percentage of cultivated land under crop insurance statewise in 2016–17 revealing wide regional disparity.

While most of the central and northern states have come under crop insurance to some extent, the same is not as true for some of the southern states, or forUP. Similarly, the north-eastern states have not seen much of their crop area under the scheme. However, the overall figures continue to be much higher than what was seen at the time of the NSSO survey, pointing towards significant improvements in crop insurance during the intervening years, but regional disparity continues to exist.

The PMFBY introduced in 2016 has brought in certain desirable and farmer-friendly provisions. Among these, most notably, is a further reduction in the share of insurance premiums to be paid by farmers (2% from 2.5% for kharif crops, and 1.5% for rabi crops, and a cap of 5% of premiums to be paid by farmers on horticultural produce), and newer claims for prevented sowing, and losses in the mid-season or post-harvest have been introduced to address additional risks faced by cultivators. The number of crops covered under insurance has also been expanded, as have the type of hazards.

However, one major change is the induction of private insurers into the system, operating alongside the state-run Agricultural Insurance Corporation of India, which was earlier the primary provisioner of crop insurance and settler of claims in the country. A bidding process is employed to select insurance provisioners in each region. Thus, the scheme operates with a profit motive by the private players.

The provision of crop insurance is, as under the erstwhile NAIS, to loanee farmers, or by individual application. Those farmers who have taken loans for seasonal agricultural operations from banks are mandatorily and automatically covered under insurance. Others who did not borrow can apply separately for insurance by submitting necessary documents such as records of cultivation, but this is optional. The insurance cover when received through loans is the loan amount.

Assessing losses, too, largely remains the same with the national crop cutting experiments (a series of annual surveys carried out to estimate agricultural output for the purpose of GDP computation) used to identify blocks of farmers that have faced losses. For some of the newer introductions for which crop cutting survey data is unavailable, insurance remuneration is provided on the basis of other indices such as wind speeds (in the case of cotton), excess or deficiency of rainfall, or relative humidity (weather-based crop insurance). A unified package insurance scheme is also provided in some districts on a pilot basis, which bundles crop insurance along with additional insurance cover for non-crop hazards, such as for accidents.

Despite this overhaul of the scheme, adoption has spread to less than 30% of the country’s agricultural land while the target was 50% of gross cropped area (Bhati 2019). In comparison, China has significantly higher rates of crop insurance at 69% and the United States has an even higher rate at 89% (Pullamvilavil 2018).

It is important to note, however, that in 2017–18 there was a reduction in the share of crop area under insurance (Figure 2). Judging by the details of the crop insurance scheme and through our field visits to several parts of India in the past (Rajeev 2012), we are perhaps able to point out some of the salient shortcomings that may continue to leave crop insurance an unattractive risk mitigation option for Indian farmers.

Concerns

Providing insurance to the loanee farmers is the major vehicle through which insurance adoption rate can be increased. However, there are several difficulties that farmers face while accessing credit. One of the major concerns in this regard is the lack of land records (such as the Record of Rights, Tenancy and Crop Information or RTC certificate) among farmers. Thus, documentation requirements are indirectly one of the barriers to adoption of crop insurance. Landowning farmers face issues owing to a lack of automatic mutations. Updating these records can prove to be a complex bureaucratic process that most farmers are ill-equipped to handle. Resultantly, many farmers who own land do not possess adequate land records. Non-loanee farmers need to exert extra effort to go through the process of enrolling under the insurance scheme, which often may not happen.

Tenant farmers are often oral lessees who cannot prove cultivation, as landowners are reluctant to provide formal lease documentation for fear of losing land rights. As a result, these tenant farmers and sharecroppers also find it difficult to access credit and hence, crop insurance.

While the PMFBY extends the coverage of crop failure to include compensation for prevented sowing, mid-season and post-harvest losses, the execution of these coverages is a point of contention. Claims for prevented sowing are paid if 75% or more of the region remains unsown owing to delayed monsoon rains and for mid-season losses, if they exceed 50% of the sown area. If losses are even 74% in the case of prevented sowing or 49% in the case of mid-season losses, no claims are paid, which can leave some insured farmers dissatisfied with the scheme (Rajeev and Nagendran 2018). Reporting of post-
harvest losses, too, has to be done within 48 hours of the loss occurrence, which is not always possible for farmers.

Importantly, a lack of interest remains one of the prominent reasons for non-adoption of crop insurance (Figure 1), just after awareness, and continues to be so (Rajeev et al 2016). This disinterest arises from those farmers who had been insured but who did not receive compensation despite facing crop loss. Such an outcome is possible owing to the current loss assessment mechanism.

Insurers verify claims of losses by farmers through data gathered by crop cutting experiments. These experiments include random surveys selecting squares of land at the village level (two in each in the case of Karnataka) per notified crop,6 which are collected primarily for the computation of national income statistics. The issue is that if a farmer in a village faced crop loss, but this was not reflected in the survey plot—then they are less likely to receive compensation. More specifically, farmers are considered in blocks rather than individually,7 and loss assessment is undertaken by comparing current regional output (per block) to average output. If the region’s output was lower than a threshold yield (7-year average yield excluding two extremes multiplied by indemnity level, 60% to 90%), then compensation is disbursed but not otherwise. For farmers facing specific damage to crops that does not spread to the region, insurance is not helpful.

Our in-depth discussions with farmers in different parts of India reveals, further, that most farmers are unaware of this computation method (including concepts such as threshold yield), and thereby feel “misled” when they do not receive compensation despite being insured and facing crop losses. This works to reduce their faith in the institution of crop insurance, and thus, reduces their willingness to participate in it. Improved identification of losses can undoubtedly be beneficial in this regard.

The results of crop cutting surveys take time to be released. Identifying farmers who faced losses takes time, and processing of claims adds to this, so that farmers who faced losses are often compensated after almost a year. This is an added burden to subsistence farmers as they have neither the crop income nor insurance claim during this period. Further, the insurance cover is often only for the crop loan amount (based on cost of cultivation, not on value of output) and therefore, cannot provide cover for their potential income.

Therefore, despite several improvements, the PMFBY also continues to face several bottlenecks in facilitating the widespread implementation in the country. Improvements can be ensured by addressing some of the pertinent issues.

Conclusions

India has a long and varied history with crop insurance, with substantial experience in the field. However, for much of this time, crop insurance schemes have failed to be substantial risk mitigation tools for most of the country’s farmer population. While the recent PMFBY has taken strides in improving this scenario, it is evident that much ground needs to be covered.

Despite including a much more diverse range of crops within its ambit and allowing for more varied insurance products, definitional issues can pose barriers to more widespread adoption. Perhaps the most pressing issue is also the most difficult to tackle: the area-based approach to insurance cover, which makes insurance viable in the first place, was found to leave many farmers dissatisfied with the results as they were not compensated for the losses they faced. One solution would be to use technology such as remote sensing to assess crop losses at the micro level in a timely manner.

Other countries have also implemented successful crop insurance programmes. Although it would be wrong to compare statistics owing to certain differences that can exist between nations, a detailed inspection of some of the particulars of the implementation of crop insurance in such countries could undoubtedly be of use. More specifically, we may consider the cases of China and Canada in this regard.

China introduced a programme of subsidised crop insurance in 2003, a few years later than what was done in India, but has shown considerably greater success in that time. In just four years after inception, by 2007, 10% of China’s agricultural land was insured, which amounted to around 15.33 million hectares (FAO 2011) and this continued to improve so that by 2016 over 115 million hectares of land were under the purview of crop insurance (Krychevska et al 2017). Collections from premiums payment grew as well, from $0.68 billion to $6.3 billion in 2016, as a result of increased subscription to crop insurance.

One of the major factors behind the success is the assessment mechanism employed. In China, a village level chief, who is responsible for collecting and sharing vital risk-related information with insurance providers for assessment. These chiefs maintain records of crops cultivated, assist in damage assessment, and help in computation of final claims by affected farmers. Assessment of damages to crops is done at the village level by a specialised team that includes representatives from the farmers’ community, insurance company, and an agricultural expert from the local university (Krychevska et al 2017). Such a decentralised process makes insurance fair and transparent, which further encourages farmers to adopt such risk mitigating measures for their crops. Considering the system’s success in China, these lessons may be of use in India as well.

The extent of insurance cover is another area of contention. In India, farmers facing losses are often only compensated for the cost of cultivation, and even this amount is received only after much delay. In the case of crop damage, when loanee farmers are compensated, essentially it is the bank that is insured and not the farmers. Such a system leaves many farmers unable to subsist until the following harvest due to a lack of funds. This situation is exacerbated if the entire crop is lost, since the farmer has no income during that time. Even for farmers getting insurance through the voluntary route, the insurance coverage is according to the scale of finance and not the value of output.8 Insurance in many developed nations such as Canada allows farmers to take up insurance according to the value of output.9 Such options help farmers at the time of distress. Incorporation of such flexibility in India’s agricultural insurance, and possibly even a mandatory cover for full value of output for small and marginal farmers would have a tremendous effect on reducing farmers’ distress.

Thus, while crop insurance programmes have been slowly gaining momentum over the years, there exist several concerns which should be addressed so as to more successfully extend risk mitigation to all farmers of the country.

Notes

1 This includes the Comprehensive Crop Insurance Scheme (CCIS), National Agricultural Insurance Scheme (NAIS), the Weather Based Crop Insurance Scheme (WBCIS), etc.

2 http://agricoop.nic.in/sites/default/files/Agricultur%20Insurance-Credit....

3 https://agri-insurance.gov.in/Document/NAIS_Scheme.pdf.

4 http://agricoop.gov.in/sites/default/files/Modified%20National%20Agricul....

5 https://agri-insurance.gov.in/Document/Final_WBCIS_OGs_23.03.2016.pdf.

6 http://des.kar.nic.in/docs/Note%20and%20methodology%20of%20CCEs%20PDF.pdf.

7 http://agripb.gov.in/home.php?page=cropc.

8 https://agri-insurance.gov.in/FaqLogin.aspx.

9 http://www.agr.gc.ca/eng/about-us/offices-and-locations/office-of-audit-and-evaluation/evaluation-reports/evaluation-of-the-agriinsurance-private-sector-risk-management-partnerships-and-wildlife-compensation-programs/?id=1367338599421#a2.0.

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Updated On : 5th Jul, 2019

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