What Factors Influence Crop Insurance Coverage Level Choices? Learning from the Experiences and Perceptions of Farmers

by Xuche Gong, Hongli Feng, David A. Hennessy, and Jenny Ifft

Initially met with little interest, the federal crop insurance program (FCIP) has evolved into a near-automatic annual choice for farmers across most regions and major crops. Key changes since 1980—including improved rate-setting, higher subsidies, and the introduction of revenue insurance, enterprise-level coverage, and trend-adjusted yields—have made crop insurance products less costly and better aligned with farmers’ risk management needs. As shown in figure 1, both the share of major crop acreage insured and the average coverage level have increased substantially since 1980. Yet despite the high participation rate and average coverage level, farmers select a wide range of coverage levels, and some studies have raised the issue of whether farmers have been making crop insurance choices that maximize their economic benefits (Du et al. 2017). Using a recent survey conducted in Iowa and Kansas, this article presents patterns in farmers’ coverage level choices and examines potential factors driving these choices.

Survey procedures

The survey was funded by the USDA’s National Institute of Food and Agriculture and conducted in 2023. We targeted commercial non-irrigated cropland farmers from Iowa and Kansas who grew at least 100 corn acres in either 2021 or 2022. This selection criterion allowed us to examine choices made by producers operating under different production conditions. We distributed the survey through the Qualtrics online platform in two waves. The initial wave (January–April 2023) recruited participants through farmer meetings, university extension websites, mailed postcards with survey links and QR codes sent to farmers from an agricultural vendor list, and existing researcher networks. The second wave (August–October 2023) was conducted in partnership with Kynetec, a specialized farmer survey company. Participants could complete the survey on laptops, tablets, or mobile phones.

We collected responses from 653 farmers in total, with 330 participating in the first wave and 323 in the second. The sample included 476 farmers from Iowa and 177 from Kansas, covering most counties in Iowa and primarily the eastern and northern regions of Kansas. In our survey, the average per-acre corn yield is 209 bushels for Iowa farmers and 94 bushels for Kansas farmers, highlighting a significant difference in growing conditions between the two states. Additionally, while Kansas respondents tend to farm larger areas (with an average farm size of 2,220 acres compared to 1,180 acres for Iowa farmers), Iowa farmers have higher total farm sales (about 69% of Iowa farmers report total farm sales exceeding $500,000, compared to 52% of Kansas farmers.) Our state-level average acres are broadly consistent with data in agricultural censuses, in the sense that farms in Kansas are much larger than those in Iowa. However, the comparisons are not so straightforward as our survey targeted farmers with a minimum of 100 acres. 

Farmers’ coverage level choices during the 2020–2022 period

Only 15 participants reported never purchasing crop insurance policies during the 2020–2022 period. Figure 2 shows that Iowa farmers predominantly insured at 80% or 85% coverage levels, while Kansas farmers most frequently chose 70% or 75% coverage level during the 2020–2022 period. These patterns align with the state-level coverage level choices observed in the Risk Management Agency’s Summary of Business data (Schnitkey et al. 2021). During this three-year period, 35% of Kansas farmers and 24% of Iowa farmers received indemnity in (only) one year, 16% and 9% received indemnity in two years, and 8% and 3% received indemnity in all three years. Overall, Kansas farmers were more likely to receive indemnity payments than Iowa farmers, reflecting their higher risk levels.

Table 1 highlights year-to-year changes in coverage levels from 2020 to 2022. Around 90% of farmers in both states maintained the same coverage levels over three years. This suggests a preference for keeping the same coverage levels over time. In Iowa, 91% of farmers retained their coverage levels from 2020 to 2021, and 90% did the same from 2021 to 2022. Similarly in Kansas 90% of farmers did not change their coverage from 2020 to 2021, and 80% maintained their level from 2021 to 2022. This pattern suggests that farmers tend to stick with their initial choices, even when circumstances change. This “inertia phenomenon” may be due to the high time and other costs of regularly switching coverage levels, insufficient information on the costs and benefits of switching, or producers focusing their attention on other management decisions (Du 2025). Farmers may also choose the same coverage levels year after year simply because they believe a certain coverage level is right for their farm regardless of market or weather conditions.  

Unlike inertia, the recency effect hypothesis suggests that farmers are more likely to increase their coverage levels after receiving insurance indemnities due to negative yield or revenue shocks (Che et al. 2020). However, we do not observe this phenomenon in our data. Analyses of our survey data, not shown in this article, provide no evidence that farmers who received any indemnity in the past year are more likely to increase their coverage level in the following year. Instead we find that the increase in coverage levels tends to occur incrementally, that is, among those who increased their coverage in 2021, 49% in Iowa and 76% in Kansas continued to increase their coverage in 2022. This finding suggests that once farmers adjust they are more likely to reinforce the change rather than revert to lower levels. Furthermore, increased coverage levels are mainly made by young and early-stage farmers. This result suggests that crop insurance choices may evolve as producers gain experience and adapt to changing production conditions.

Farmers’ views on impact factors

Figure 3 depicts the percentage of farmers who identified a specific factor as having the most significant impact in response to the question “For the years 2018–2022, how much impact did the following factors typically have on your corn crop insurance choices regarding whether to buy insurance and at what coverage level?” The revenue guarantee function emerges as the leading factor, indicating that the risk protection level primarily drives farmers’ coverage level choices. Iowa farmers are significantly more likely to report revenue guarantees as the most important factor than are Kansas farmers. This may reflect the higher corn yield potential in Iowa, which creates a greater need to secure the associated higher revenue through insurance guarantees. Further analysis of the survey data reveals that small operations (annual sales ≤$250,000) place less emphasis on risk protection. This may be because these farmers often have off-farm income and don’t depend as heavily on crop revenue for financial security.  

The second-most common response is a distaste for paying out-of-pocket premiums. Nearly 30% of farmers in both states indicated that high premiums were a major concern. Since higher premiums correspond to higher revenue guarantees for the same farmer, this widespread distaste for paying premiums suggests farmers carefully weigh cost against benefit when considering additional risk protection. This distaste is most pronounced among small operations (annual sales ≤$250,000), likely reflecting limited payment ability or greater flexibility for self-insurance. Additionally, because crop insurance is relatively more expensive in Kansas, this premium aversion helps explain why Kansas farmers typically insure at lower coverage levels than their Iowa counterparts.  

The subsidy rate and recommendations from agents or lenders are the third- and fourth-most influential factors, although their ranking varies by state. Premiums for crop insurance are subsidized by the Federal Crop Insurance Corporation. The subsidy rate directly determines the subsidy benefit a farmer receives from FCIP. While many farmers might have already factored such benefits into the reduced out-of-pocket premium, the decreasing subsidy rate at higher coverage levels may still be a salient feature that deters some farmers from purchasing high coverage levels. This effect appears stronger when unsubsidized premiums are high, explaining why a larger percentage of Kansas farmers cite the subsidy rate as the most important factor compared to Iowa farmers.

Meanwhile, many farmers rely on advice from crop insurance agents or lenders when determining their coverage levels. These recommendations typically aim to meet specific financial goals, such as covering break-even costs or securing favorable loan terms. Our survey reveals that farmers with higher debt-to-asset ratios are likelier to follow such advice. With limited financial cushion, these farmers may be more attuned to production and market risk as well as subject to the risk management standards required by financial institutions.  

Finally, very few farmers reported receiving indemnities or friends’ and neighbors' choices as important factors. It is interesting that receiving indemnities is not ranked as an important factor by many farmers, implying that farmers value the benefit of “peace of mind” that comes with crop insurance more than the benefit of “getting back” what they pay for crop insurance.

Discussion and conclusions

Farmers’ coverage level choices often reflect the interplay of multiple factors. Using data from a recent online survey, we find that Kansas farmers tend to insure at the 70% and 75% coverage levels while Iowa farmers tend to insure at the 80% and 85% coverage levels.   Approximately 90% of farmers maintain identical coverage levels across multiple years. Revenue guarantees and premium expenses are the two most influential determinants of coverage level decisions. Subsidy rates and recommendations from insurance agents or lenders each emerge as the most important factor for about 10% of surveyed farmers. Yet these two factors are also closely related to the cost and risk protection function of crop insurance. We find less support for the claim that farmers prioritize receiving indemnities or following the suggestions of their peers.

For producers, there are tradeoffs based on what aspects of the crop insurance decision they emphasize. Focusing on a revenue guarantee maximizes risk protection and can support both production and investment decisions. However, additional risk protection at the highest levels can be costly. Some producers have shared that they do not select the highest coverage because they believe that the additional premium dollars could be better spent elsewhere. Likewise, focusing on cost can provide savings but higher exposure to yield or market swings that could have long-term impacts. Keeping the same coverage level over time may allow producers to focus on other management and production decisions but may not always provide the desired risk protection. For example, higher coverage levels may be necessary during low-price periods to protect breakeven revenue.

Agents and lenders can provide information and analysis that helps producers make this complex decision. While sometimes this is in the form of advanced decision tools that incorporate producer and market information, simple measures such as “cost per additional guarantee” can also be useful. This measure shows the premium cost per additional guarantee/liability when increasing coverage and incorporates the revenue guarantee, premium cost, and subsidy rate. Agents can also review possible revenue outcomes based on different price and yield changes. This exercise can help producers understand how they are protected under different revenue outcomes, providing a realistic understanding of the current risk environment and preventing post-harvest disappointment.  

Overall, our survey results suggest farmers strategically balance risk protection with associated costs when selecting coverage levels. However, what risk protection level farmers are willing to pay for remains an open question. Using data from the same survey, Gong et al. (2025) show that covering the expected break-even revenue is an important goal for many farmers when making their coverage-level decisions. Further studies are needed to understand better how farmers determine their optimal protection levels across different market and production environments and financial situations.

References 

Che, Y., H. Feng, and D.A. Hennessy. 2020. "Recency Effects and Participation at the Extensive and Intensive Margins in the US Federal Crop Insurance Program." The Geneva Papers on Risk and Insurance-Issues and Practice 45:52-85. 
Du, X., 2025. "Inertia in the US Federal Crop Insurance Market." International Journal of Industrial Organization 103149. 
Du, X., Feng, H., and D.A. Hennessy. 2017. "Rationality of Choices in Subsidized Crop Insurance Markets." American Journal of Agricultural Economics, 99(3):732-756. https://doi.org/10.1093/ajae/aaw035. 
Gong, X., H. Feng, D.A. Hennessy, J. Ifft, R. Schupp, and M. Regenwetter. 2025. "Securing Break-Even: A Behavioral Target-Income Framework for Crop Insurance Choices." Working paper 25-WP 670. Center for Agricultural and Rural Development, Iowa State University. https://www.card.iastate.edu/products/publications/synopsis/?p=1402.
Schnitkey, G., N. Paulson, C. Zulauf, and K. Swanson. 2021. "Crop Insurance Coverage Levels and ECO Use on Corn." Farmdoc Daily (11):71, Department of Agricultural and Consumer Economics, University of Illinois at Urbana-Champaign, May. 
US Department of Agriculture Economic Research Service (USDA ERS). 2024. "Crop Insurance at a Glance." US Department of Agriculture Economic Research Service. https://www.ers.usda.gov/topics/farm-practices-management/risk-management/crop-insurance-at-a-glance.

Suggested citation

Gong, X., H. Feng, D.A. Hennessy, and J. Ifft. "What Factors Influence Crop Insurance Coverage Level Choices? Learning from the Experiences and Perceptions of Farmers." Agricultural Policy Review, Winter 2025. Center for Agricultural and Rural Development, Iowa State University. https://agpolicyreview.card.iastate.edu/what-factors-influence-crop-insurance-coverage-level-choices-learning-experiences-and-perceptions.