Agriculture has been backbone of the Indian economy. According to the Government of India’s Economic Survey 2024-25, Agriculture contributes 16.00 per cent to the GDP and employs 46.10 per cent of the country’s total workforce. Due to its inherent nature, agriculture is susceptible to a variety of risks. These risks include production risks (uncertainties in weather and temperature), price risks (fluctuations in input or output prices), financial risks (farmers’ debt obligations), institutional risks (changes in subsidy policies and laws) and human risks (errors, health crises). Due to climate change, these risks have increased many folds. A study in Bundelkhand suggested that the Kharif season is hotter compared to previous decades (Singh, 2020). It has been noted that production risks contribute to one-third of the overall risks faced by farmers (Birthal et al., 2019). These risks can significantly threaten farmers’ income, thereby adversely affecting their consumption patterns. A significant majority, over three-fourths of farmers in India, utilise one or more methods to mitigate risks (Birthal et al., 2021). Therefore, farmers employ various ex-ante risk management strategies to reduce these risks. These strategies include crop diversification between different crops and livestock, share-tenancy contracts, non-farming activities and crop insurance (Aditya et al., 2018).
       
Crop insurance serves as a crucial formal mechanism for protecting against unforeseen production risks. It is a risk-pooling policy that safeguards agricultural producers against unexpected losses in projected crop yields or in market sales profits. This mechanism allows individuals across different regions or with varying risk preferences to share risks (Banita and Gurung, 2023). While many policymakers emphasise the significance of crop insurance as an essential tool for managing unknown risks (Korekallu  et al., 2021), others question its effectiveness in stabilising income (Walker, 1986). Birthal et al., analysed the 70th round Situation Assessment Survey of National Sample Survey Office (NSSO). They found that risk management strategies, such as crop diversification, crop rotation and shared tenancy, are more effective than risk transfer strategies, such as crop insurance (Birthal et al., 2019). However, traditional methods like these have notable disadvantages, such as their correlated nature, which may not provide sufficient protection against risks during a calamity (Cole et al., 2013). Additionally, there are opportunity costs associated with these activities, as they often require land or labour that could be allocated to growing more profitable crops (Birthal et al., 2019). Crop insurance may address these issues and encourage farmers to adopt modern technology. As it is not directly tied to farmers’ income, it could offer better assistance in the event of unforeseen losses (Aditya  et al., 2020) (Biswal and Bahinipati, 2022).
       
Crop insurance can generally be classified into three approaches based on how losses are assessed: the individual approach, which focuses on fluctuations in the crop output of each insured farmer; the area approach, where losses are assessed collectively for all insured farmers in a designated area; and the weather approach, in which insurance payouts are based on a weather index that correlates strongly with actual farmer losses. Since independence, there have been a number of projects aimed at covering agriculture against many risks. Government introduced a bill on crop insurance to the states wishing to adopt the insurance policy in 1965. Nonetheless, the bill did not pass at that time because the country faced many constraints. Finally, a new crop insurance scheme was instituted in 1985 (Dandekar, 1976) and several others were instituted thereafter. The Comprehensive Crop Insurance Scheme (CCIS), initiated by the government in 1985, was the first crop insurance scheme in the country, providing financial aid to farmers in the event of crop failure caused by natural disasters. (Government of India, 2014). This scheme then gave way to the National Agricultural Insurance Scheme (NAIS) in 1999-2000 which was founded on the area approach to address rampant calamities. It aimed to include all food crops (cereals, millets and pulses), oilseeds and horticultural crops, provided that adequate past yield data were available for several years (Raju and Chand, 2008). In 2016, the government overhauled the existing scheme and launched the Pradhan Mantri Fasal Bima Yojana (PMFBY), which improved upon it. The scheme includes all Food and oilseeds and annual commercial/horticultural crops. The highest rate of premium, which the farmers will pay, is 2 per cent on all Kharif food and oilseeds crops, 1.5 percent on Rabi food and oilseeds crops and 5 per cent on annual commercial/horticultural crops (Biswal and Bahinipati, 2022).
       
Although there have been decades of implementing a variety of crop insurance programs, adoption remains low. According to NSSO 70^th round, only 4.80 per cent and 3.17 per cent of farmers were insured in the Kharif season and Rabi season, respectively (Aditya et al., 2018) and have recorded a meager rise to less than 10 percent in the 2019 records of National Sample Survey (Biswal and Bahinipati, 2025).
       
Upon study of literature, it was found that there is a notable lack of systematic reviews of various studies on crop insurance within India. This paper aims to bridge this gap by addressing several research questions outlined as follow:
RQ1. What is the current trajectory of research in crop insurance?
RQ2. Who are the leading, influential and impactful contributors and sources in the crop insurance literature in India?
RQ3. Which articles are recognised as the most influential in the area of crop insurance?
RQ4. What research methodologies are most commonly employed in this domain?
RQ5. What factors influence the uptake of crop insurance in India?
       
This paper has used a Systematic Literature Review (SLR) to study the factors leading to dismal adoption of crop insurance in India. PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) method has been employed for this purpose. The PRISMA diagram outlines a literature review in 3 Phases. First is the identification of relevant documents from a reliable source; second is the screening of all identified documents based on predetermined criteria. And finally, after screening documents, articles were selected for final inclusion. Vos-viewer software was used for analysis.
 
Literature strategy
 
To select the relevant studies for inclusion in the paper, we conducted keyword searches across journal databases and Scopus-indexed book chapters in October, 2025. Some papers were included based on web searches because they were found to improve the research’s efficacy. Keywords were chosen based on already published papers (Aditya et al., 2018), (Biswal and Bahinipati, 2022), (Raju and Chand, 2008). It was found that not just one keyword but several jargons were used interchangeably. It was also found that various crop insurance schemes were included to make the keyword search more comprehensive and procure a large number of articles. Table 1 provides a list of the keywords employed in the online search of databases for relevant articles.
 
Eligibility criteria
 
Relevant publications for the systematic review were selected based on a set of eligibility criteria, as shown in Table 2. Papers indexed by Scopus were considered for inclusion in the review process. A few papers were selected through a back search of empirical papers. The second criterion concerned language; papers published in languages other than English were excluded. The third criterion is to include only articles published during the period 2000 to 2025. A 25-year period was selected because two significant crop insurance policies (NAIS and PMFBY) were implemented during this period. The fourth criterion was region, since this study is based in India, articles from India or at least one of the regions of India are included. Consequently, the remaining articles were screened for their abstracts and full texts. A study was included if it explored the factors affecting demand for crop insurance.
 
Selection process
 
The PRISMA method delineates three distinct stages (Fig 1). The initial stage involves identifying keywords for the search. Upon conducting a keywords search listed in Table 1, a total of 260 articles were identified. Apart from these 260 articles, 12 articles were added after doing a back paper search of articles. Of these 260 articles, 2 were removed due to duplication and 1 was removed because it was from a region outside India. The second stage involves screening, during which we applied inclusion and exclusion criteria to further refine the selection (Table 2 and Fig 1). Another 10 articles were removed at this stage since they belonged to a period prior to 2000. A total of 247 articles were chosen for the third stage (Table 2). In the third stage, abstracts and full texts of the remaining 247 articles were reviewed for their eligibility for final inclusion. A total of 70 papers (58 from Scopus indexed journals and 12 from empirical searches) were included for final review. We excluded 189 papers since they did not fulfil the objective of the paper.






 
Most cited papers
 
As outlined above, a total of 70 papers were selected. Table 3 highlights the top ten most cited articles. The most cited article is “Barriers to household risk management: Evidence from India” by S. Cole, which has 135 citations, followed by “Farmers’ perception of climate change and adaptation decisions: Micro-level evidence from Bundelkhand Region, India”, by S. Singh, with 132 citations and “Crop insurance in India: Scope for improvement” by V.S. Vyas and S. Singh with 97 citations.


 
Number of publication (Yearwise)
 
Fig 2 highlights the year-wise distribution of number of papers published. Most of the papers were published after 2019. While studying the year-wise trend for published papers, around 58.00 per cent (41 papers) were published between 2020 and 2025. No influential papers were published before 2006 in Scopus Journals. From the following trend, it can be concluded that importance of crop insurance as a risk hedging measure against agricultural risks has grown significantly in recent times.


 
Top author and organisation of this study
 
The study identifies the ten most prominent authors from the selected articles in Table 4. In terms of citation frequency, S. Singh, K.S. Aditya, with 132 citations and 92 citations respectively. Regarding the total number of publications, Senapati, Asis Kumar; Bahinipati, Chandra Sekhar and Rajeev, Meenakshi have the most, with 3 publications each.


 
Top Journal of the study
 
Table 5 delineates the top ten journals across various time periods that are identified in the study. The journal “American Economic Journal: Applied Economics” exhibits the highest citation count of 1355 followed by “Agricultural Economics Research Review” with 190 citations. Regarding the number of publications, “Economic and Political Weekly” has maximum number of publications with 2 publications during the period 2005-10, 3 publications during 2016-20 and 1 publication during 2021-25, followed by the “Agricultural Economics Research Review” with a total of 4 publications, 1 during 2005-10 and 3 during 2021-25 period. American Economic Journal: Applied Economics has the largest number of citations, but has only 1 publication during this time period.


 
Methodology used
 
Table 6 lays out most used methodologies used in different research articles. The majority of papers (approximately 70.00 per cent) have used quantitative methods for the analysis, while 11.00 per cent papers have used qualitative methods. Only 1 paper has used both qualitative and quantitative methods and 11 are review articles. Amongst the quantitative methods, Multiple regression is the most widely used (14), followed by Logit/Probit Regression (12) and Descriptive analysis (10). Conducting interviews was the most used qualitative method. When it comes to the type of data used, division is almost equal: 29.00 per cent use primary data, while 26.00 per cent use secondary sources. 4 articles have used primary and secondary sources, while 11 are review articles.


 
Factors
 
Despite the risk mitigation benefits of crop insurance, its adoption in India remains low. Crop insurance covers 28.00 per cent of India’s total gross cropped area, yet it has the highest number of uninsured farmers in absolute terms. This poor adoption is puzzling, as around 60.00 per cent of the gross cropped area depends on the monsoon (Korekallu Srinivasa  et al., 2021). There have been various reasons for the dismal adoption of crop insurance in India. These can be broadly divided into 5 broad groups: social and demographic factors; economic factors; behavioural factors; geographical factors and structural factors. This section emphasizes the key reasons behind the low crop insurance uptake in India as tabulated by Fig 3.


 
Social and demographic factors
 
Education, landholding size, age, caste and gender play a crucial role in determining crop insurance demand. Lack of awareness and education remains the single most important factor (70.00 per cent) hindering the take up of crop insurance in India. The education of the head of the family has a significant impact on crop insurance uptake (Biswal and Bahinipati, 2025). There is a lack of awareness among farmers about insurance products. Farmers with education and access to information sources are more inclined to purchase insurance (Aditya et al., 2018) (Sherrick et al., 2004) (Birthal et al., 2019). This is mainly because they are better aware of recent government initiatives and the best-suited risk-reducing strategy for their farm (Raju and Chand, 2008). It was also found that access to mass media is likely to impact risk management strategies positively (Birthal et al., 2019) (Birthal et al., 2021). Analysis of the 70^th round SAS of the NSSO 2014 survey shows that farmers receiving technical assistance are more aware and likely to adopt crop insurance (Mukherjee, 2019).
       
Insurance uptake is also influenced by caste, gender and age (Keerthikumara et al., 2025). The elderly women and farmers of scheduled castes and tribes are less likely to adopt insurance coverage (Aheeyar et al., 2019; Kumar et al., 2021; Das, 2025). Nearly 40 per cent articles indicated that the size of land has an effect on the choice of farmer to purchase crop insurance. There have been divergent perceptions on the effects of land holdings on the decision of the farmer to take insurance. Farmers with small plots are more exposed to risks and hence they tend to purchase crop insurance. However, there is evidence of this not being the case in India. Indian farmers with smaller land hold sizes may not be able to afford regular premiums. Therefore, it was established that farmers with larger land tenures have higher chances of purchasing crop covers (Birthal et al., 2021). Table 7 shows different social and demographic factors affecting crop insurance adoption in India.


 
Economic factors
 
Crop insurance is found to have a positive influence on farm income (Nivetha et al., 2025), but inability of the farmers to buy premiums was listed as one of the major barriers in around 40.00 per cent of studies (Chander et al., 2020). The Indian government has subsidized over 50% of premiums under PMFBY to encourage enrolment. But some researchers suggested that a one-time subsidy does not significantly change farmers’ decision to take crop insurance (Ghosh et al., 2021; Matsuda and Kurosaki, 2019).
       
The extent to which credit affects the demand of insurance is questionable. According to Rajeev and Nagendran (2023), informal credit is positively associated with the demand of insurance, because in the case of the high interest informal loans, farmers are likely to purchase crop insurance. On the other hand, numerous researches point out that one of the primary causes of the low insurance uptake is the lack of formal credit access (Aditya et al., 2018). Approximately 75.00 per cent of the insured farmers were formal loan-related insurance (Ghosh et al., 2021). Nevertheless, bank loans are not readily available to the farmers because they lack land. The distribution of insurance under PMFBY is highly skewed towards wealthier farming households as enrolment requires farmers to provide legal land proofs. (Kavitha and Nandhini, 2022). Access to formal credit is directly dependent on caste, gender, age, landholding size. (Cariappa et al., 2021) Table 8 summarizes economic factors affecting crop insurance uptake.


 
Behavioural factors
 
Behavioural factors significantly influence the demand for crop insurance. Research shows that delays or failures in indemnity payments diminish farmers’ trust and greatly lower future insurance participation (Raju and Chand, 2008). Aditya et al., (2018) in their analysis of NSSO, 2013 data found only 5.90 per cent and 7.30 per cent farmers received timely payments during Kharif and rabi seasons respectively. More than 80.00 per cent of farmers did not receive any payments (Aditya et al., 2018). Major reason cited for delay in indemnity payments is delay in results of Crop cutting Experiments (CCE) (Mukherjee and Pal, 2017).

Prospect theory also negatively impacts farmers’ insurance decisions, highlighting that individuals are more sensitive to losses than equivalent gains (Biswal and Bahinipati, 2022).  Loss aversion theory which is related to prospect theory, explains that many farmers lack understanding of insurance as a risk-transfer tool; when premiums are paid without receiving compensation, insurance is viewed as a wasteful expense (Korekallu et al., 2021). Present bias also plays an important role. Present bias occurs when people prefer a quick payoff rather than a larger and later payoff. It was found that if premium is collected at the time of harvest can significantly improve crop insurance uptake (Biswal and Bahinipati, 2022; Ghosh et al., 2021).
       
Basis risk refers to a case when there is a difference between government generated index and actual loss that the farmer has experienced. According to a study by Gaurav and Chaudhary (2020), it was identified that a basic risk substantially lowers the incentive of the farmer to buy crop insurance When compensation is less than losses, then farmers regret paying the premiums, according to the regret theory (Korekallu et al., 2021).Table 9 represents behavioural factors.


 
Geographical factors
 
Crop insurance is also affected by geographical factors. In those areas, where farmers are more at risk to lose crops, they are more risk-averse and hence, more inclined to buy insurance (Aditya et al., 2018). Nearly 30.00 per cent studies discovered that farmers and products that are more vulnerable to losses tend to take up crop insurance. There is also the availability of alternative risk mitigation mechanisms like concessional loans, minimum support prices, crop diversification and crop rotation, which also brings in income (Devi and Sharma, 2022) (Senapati, 2024). In the case of low income farmers, it can be more profitable to invest in such income generating activities rather than spend on insurance. Other forms of self-insurance such as storing grains and selling livestock factors decrease the need of formal insurance as well. There is also the government disaster relief payment that reduces the necessity to have insurance (Ghosh et al., 2021; Singh and Agrawal, 2020). It was also established that farmers in places where there are improved irrigation facilities will have fewer chances of purchasing insurance, yet a combination of irrigation and insurance can improve income and risk management (Ghosh et al., 2022; Birthal et al., 2022). Table 10 summarizes geographical factors.


 
Structural factors
 
Structural factors play a critical role in determining insurance uptake. 30.00 per cent of studies have identified delay in claim settlement as an important factor which discourages farmers to buy crop insurance. Persistent delays in crop-cutting experiments, which postpone compensation, have been a common issue across schemes since their inception in 1985 (Nayanatara  et al., 2020). Moreover, the amount of claims is usually much less than the real losses (Mukesh and Pandey, 2019), which negatively affects trust and leads to the reluctance to adopt it in the future. The lack of coverage and complicated methodologies used in yield assessments are additional issues that decrease adoption (Patnaik and Swain, 2017; Priyanka et al., 2019). Table 11 enlists all structural factors.

Crop insurance is an important ex-ante risk management instrument significant to farmers that can minimise the effects of climate change over time. A literature search in the Scopus database reveals several reasons for low adoption in India, including social, economic, behavioural, geographical, and structural factors.
       
In addition, the review identifies several gaps in the existing literature that warrant further investigation. Behavioural determinants, particularly those related to farmers’ risk perceptions and preferences, appear to be insufficiently examined despite their potential influence on insurance decisions. The literature also lacks adequate comparative analyses of different crop insurance schemes and studies assessing the impact of structural and policy changes on insurance participation. Furthermore, future research should explore the interrelationships among various risk management and adaptation strategies adopted by farmers.
       
Raising awareness among farmers through government partnerships with banks and insurance companies is a valuable approach to enhancing knowledge levels, because access to financial resources alone is not enough to increase uptake. Confidence among farmers can be restored by simplifying the processes for settling claims quickly, without delaying payments, and by enhancing the means of assessing yields. The policies must be combined with strategies to build trust, expand access to education and funds, and reduce risk to promote the adoption of crop insurance.

The views and conclusions expressed in this article are solely those of the authors and do not necessarily represent the views of their affiliated institutions. The authors are responsible for the accuracy and completeness of the information provided, but do not accept any liability for any direct or indirect losses resulting from the use of this content.

The authors declare that there are no conflicts of interest.