Risk Management and Financial Literacy Practices of Rural Kentucky Farmers

INTRODUCTION

Since the 1980s, farm consolidations have led to a decrease in the number of farms and an increase in the size of larger farms in the United States, resulting in greater competition from corporate and large farms for smaller producers (Keller and Kassel, 2025). This change in the farm landscape, marked by rising input costs, decreases in household income, an aging farming population in part due to the migration of young people from rural to urban areas, and other constraints has made the learning and application of financial literacy skills crucial for small farmers (Touch et al., 2024). Due to these changes, it is increasingly important for modern farmers to understand and utilise risk management tools to mitigate risk (Adnan et al., 2020). Other US studies on the impact of financial literacy and risk management have found significant relationships between the two and individually on different populations, with financial literacy identified as the knowledge and understanding of financial concepts and risks, as well as the skills and attitudes to apply such knowledge and understanding in order to make effective decisions across a range of financial contexts by the OECD Programme for International Student Assessment (OECD, 2025). For example, Davoli and Rodríguez-Planas (2020) reported differences in adult financial literacy among Americans of various ancestries, attributing these differences to cultural factors, such as family. The 2018 FINRA National Financial Capability Study data showed that financial education is associated with higher financial literacy, which in turn indicates better financial well-being (Zhang and Chatterjee, 2023). Siriopoulos (2021) identified financial literacy as a risk management tool in its own right. Chang et al. (2025) explained that the way individuals handle risk, both personally and financially, informs lenders' credit decisions by indicating levels of credit risk and affecting overall access to resources.

Despite general US population studies, little research exists on the financial literacy of US small and family farmers and their demographics. The most comprehensive and recent financial literacy research on US farmers was a United States Department of Agriculture-sponsored study, organised by South Carolina State University, which focused on the impact of financial literacy on the farm performance of South Carolina farmers from 2020–2023 (Ma and Feng, 2025). Findings from Ma and Feng's (2025) survey of small farmers showed that most had gaps in advanced financial literacy. Similarly, the risk management practices of farmers have received limited attention in the literature, aside from a single example involving Illinois farmers (Tudor et al., 2014). Tudor et al. (2014) analysed risk management practices and associated demographics among Illinois farmers but did not include a measure of financial literacy. While the previously discussed research studies did focus individually on financial literacy and risk management among a subset of US farmers, we found no previous study that specifically combined financial literacy and risk management levels with influential demographic variables for US farmers. Additionally, no survey has included data from Kentucky farmers, who represent the respondents in the current study.

Small and family farmers must navigate complex financial decisions with limited formal training, which is why understanding their financial literacy levels seems relevant. Small and family farmers are often vulnerable to financial mistakes or risky decisions that negatively affect the health of their farms and households, reducing their eligibility for credit needed to purchase resources (Nolan et al., 2024). Research further indicates that small family farmers with inadequate agricultural financial literacy are less likely to achieve farm growth and survival (Haryanto et al., 2023). These challenges are compounded by broader structural pressures within the agricultural sector and the unique landscape of Kentucky agriculture. Large corporate farming operations have increasingly made it difficult for smaller family farms to remain competitive (Clark, 2020), particularly when combined with external pressures such as tariffs, price controls, and shifting government policies. These structural shifts have been especially evident in Kentucky, where the phase-out of federal tobacco price supports has contributed to persistent economic vulnerability among small farms, leading to declining agricultural viability, increased reliance on part-time farming, and household incomes that fall well below national averages (Clark, 2020). While tobacco may have decreased as a commodity of choice within the state, many Kentucky producers have diversified into other crops and livestock production. As of 2023, the top commodities produced in Kentucky included cattle, hogs, poultry (predominantly broilers and eggs), corn, soybeans, hay, and the “other” category, which includes the state's equine stock and sales (USDA, 2024).

Given these dynamics, the study aimed to better understand how financial literacy and risk management tools intersect with farmers' demographics to develop hypotheses on the potential of predictive relationships between the variables using three research questions and associated hypotheses:

• RQ1. To what extent do demographic and farm characteristics (age, gender, state, income, education, years of experience, credit score, land ownership, acres farmed, and farm type) predict Kentucky farmers' financial literacy levels?

  • H₀1. Demographics and farm characteristics (age, gender, income, education, years of experience, credit score, land ownership, acres farmed, and farm type) do not significantly predict financial literacy levels among Kentucky farmers.

  • H_a1. Demographics and farm characteristics (age, gender, income, education, years of experience, credit score, land ownership, acres farmed, and farm type) significantly predict financial literacy levels among Kentucky farmers.

• RQ2. To what extent do demographics and farm characteristics predict the use of risk management practices among Kentucky farmers?

  • H₀2. Demographics and farm characteristics do not significantly predict risk management practices among Kentucky farmers.

  • H_a2. Demographics and farm characteristics significantly predict risk management practices among Kentucky farmers.

• RQ3. To what extent does financial literacy predict the use of risk management practices among Kentucky farmers?

  • H₀3. Financial literacy does not significantly predict the use of risk management practices among Kentucky farmers.

  • H_a3. Financial literacy significantly predicts the use of risk management practices among Kentucky farmers.

Soekarni et al. (2024) stated there was a “pressing need for systematic and sustainable initiatives to advance financial literacy in the agriculture sector.” This study addresses their call for research and expands upon Tudor et al.'s (2014) examination of risk management practices among farmers.

MATERIAL AND METHODS

Our nonexperimental, predictive correlational study surveyed Kentucky farmers during the period from 17 August 2025 to 28 September 2025. The sample profile for this survey consisted of farmers from Kentucky who generate income from farming, specifically the manager or decision-maker of small family operations, with a limit of one response per household. A social media recruitment process was used by posting a Google Forms survey link on LinkedIn and Facebook, using Kentucky farm association groups and asking for volunteers. When the sample size stalled, flyers with QR codes for the survey were posted in Kentucky at farming and agricultural meetings and events asking for volunteer survey takers. Recruitment was limited to the sample profile of respondents, and the survey included a screening tool to ensure only qualified farmers took the survey. The online survey combined two pre-validated instruments to measure financial literacy (Houts and Knoll, 2020) and risk management practices (Tudor et al., 2014), and collected demographic data on age, gender, state, income, education, years of experience farming, credit score, land ownership, acres farmed, and farm type. Both previously used instruments reported alpha coefficients > .70. Given the age of the Tudor et al. (2014) instrument, we updated some of the terminology. For example, “including futures” was added to the description of forward contracting; the phrase “livestock” was added to “multiple crop enterprises”, and “or livestock risk protection (LRP)” was added to “crop revenue insurance”. “Method of land ownership” refers to whether farmers purchased the land they operated on, inherited it, rented it from another owner, or some combination of these three.

Linear regression tested the hypotheses and determined whether relationships existed between variables. Three research questions guided the study with accompanying null and alternative, two-tailed hypotheses (provide in the Introduction). The population was recruited within Facebook and LinkedIn farming groups (with administrator permissions) and from posting QR-coded flyers at farm events in Kentucky. To access the survey, farmers were screened against the inclusion criteria of being over 18 and running, owning, managing, or leading operations of a small Kentucky farm, and then submitting their informed consent.

The regressions, descriptive statistics, and all statistical tests were conducted using JASP after the data were cleaned and prepared in Microsoft Excel. There was no missing data because all questions were marked as required on the form. Two cases were removed due to anomalies in their responses (i.e., saying “no idea” or “0” for “credit score” or “income”). To prepare specific variable responses for analysis, some variables were coded to correspond to specific answers; for example, gender (Male = 0; Female = 1). Table 1 provides the full operationalization of the data, including coding with reference groups provided.

RESULTS

A total of 118 complete surveys were received, with Kentucky farmers representing nearly all participants. Six responses from other states were removed from the analysis (with one farmer each from Alabama, Iowa, Kansas, Louisiana, Nebraska, and Tennessee) based on their response to the “State” variable identifying the location of the farming operation on the survey. This resulted in a final sample of n = 112 and the removal of “State” as a variable of focus in the analysis. Table 2 summarises the response counts for each categorical variable, and Tables 3 and 4 present the descriptive statistics. Values reported for income levels and credit scores were participants' self-reported estimates (they did not have to pull their official credit scores or provide their annual income to the dollar), and those values were not obtained through any independent data service by researchers.

Table 3 shows that the median acreage farmed in this sample was 184.5 acres, with a mean of 294.052 acres farmed. The majority of farms in this sample cover less than the average United States farm size of 496 acres in 2025, while the sample average is more than the 2025 Kentucky reported average of 182 acres (USDA, 2026c). The USDA Economic Research Service (USDA, 2026a) classifies farm size mostly by gross cash farm income (with small farms making less than $350,000), and though this survey requested total household income estimates (combining farm and non-farm income), the average household income of $145,814.29 and the median of $110,000 suggests that this sample mostly represents small family farms by USDA standards (Table 3).

The study used three RQs to organise the results. RQ1 tested whether any of the demographic variables of age, gender, education level, income, farming experience, credit score, acres farmed, method of ownership, and types of farming predicted the financial literacy levels of the participants. Using multiple linear regression, the model showed no significant predictive relationship among the participants (p = 0.227), with an adjusted R² of only 0.034. RQ2 examined the predictive relationship between a farmer's demographics and the risk management practices used (p = < 0.001). Tables 5, 6, and 7 provide the significant results, illustrating that 43.7% of the variance in the farmer's overall risk management scores was explained by demographic variables. Specifically, the number of acres farmed positively predicted the use of risk management practices (t = 7.768, p < 0.001); farming crops only (no livestock) impacted risk management tool adoption (t = 2.273, p = 0.025); and non-college farmers used significantly fewer risk management practices (t = −2.022, p = 0.046) than farmers with graduate degrees. As shown in Table 7, the coefficient for producers who raise only livestock (no crops) approached significance at the 90% level (p = 0.065), with t = −1.867, suggesting that farmers who raise only livestock use fewer risk management tools than those with both crops and livestock.

RQ3 reviewed the relationship between financial literacy and risk management practice usage in the sample of Kentucky farmers. The simple linear regression test between the two variables showed no significant evidence of a predictive relationship between financial literacy and risk management practice usage (p = 0.296).

DISCUSSION AND CONCLUSIONS

The study found that only demographics of education level, acres farmed, and commodities farmed significantly predicted a farmer's use of risk management practices, and that financial literacy was not significantly tied to demographics in the surveyed population. Interestingly, credit scores were also not significantly associated with either financial literacy or the use of risk management practices. This contrasts with Preston and Wright (2023), who noted that financial literacy could positively affect employment, thereby improving individual income and access to resources. Xue et al. (2019) found that financial literacy varies by demographic factors (such as gender, income level, and education).

The lack of evidence linking financial literacy levels and risk management practice usage was also surprising, given previous findings on the individual variables by Ma and Feng (2025) and Tudor et al. (2014), as well as Nolan et al. (2024) review of risk susceptibility at lower financial literacy levels in farm households and Haryanto et al. (2023) statement that financial literacy can impact farm growth and survival for small farmers. Though financial literacy was not found to be a significant predictor of risk management usage in Kentucky farmers, the connection between the topics warrants further study. As both have been found to exert positive influences on smaller operations in agriculture, future replications of this study in larger samples could not only provide more significant findings on the contribution of financial literacy to small farmers in Kentucky but also make that connection between financial literacy and risk management.

The results associated with RQ 2 indicate that variables such as the number of acres farmed, the types of commodities farmed, and the education levels could predict the use of risk management practices. Our results align with those of Vigani and Kathage (2019), who noted that the use of risk management in farm operations depends on specific variables, with larger operations and a diversified set of commodities farmed being more likely to use risk management tools. Similarly, Sahoo and Behera (2025) identified that adopting a specific risk management tool (crop insurance) in Odisha, India, was positively associated with higher education levels, presence of non-farm income, more costly production, a larger proportion of owned land, greater experience and use of other risk management tools, and increased credit access. Sahoo and Behera's (2025) results aligned with the findings of Tudor et al. (2014) that those operations with more acreage used more risk management practices. Furthermore, the number of risk management tools used was higher for farm operations that consisted solely of crops than for those that raised both crops and livestock. This differed from the results of Tudor et al. (2014) study. It is possible that diversified operations that raised different categories of commodities had different options or needs for various risk management tools.

Limitations existed in this study and these could be improved upon in future replications. One limitation was the online survey tool's population representation and reach. Recruitment likely favoured farmers within established social networks or younger people with a greater online presence, which thus potentially excluded more isolated or atypical operations. The sample may underrepresent older farmers or those with limited internet access, reducing the extent to which findings can be generalised to the broader agricultural population.

The risk-management instrumentation presented an additional limitation. The demographics section and risk management options in the survey instrument, as created by Tudor et al. (2014), did not account for the specificity in the commodities raised (such as identifying specific species within the livestock category or specifying which grains or produce were raised as crops). Of the 18 tools available in the risk management tool, all may apply to crop producers, while only 11 can be utilised by operations that raise livestock alone, which was applicable in both the original and in the only other evidenced use of this tool in Illinois (Tudor et al., 2014), a state that garners most of its farm income from cropland, with its top two commodities being corn and soybeans (USDA, 2026b). Of the available tools presented for risk management by Tudor et al. (2014), some were limited to specific animal species. For example, livestock risk protection insurance is a programme that helps manage price risk in the market for certain categories of feeder and fed cattle or swine. However, it is not available for adoption by sheep, goat, or other species (Griffeth, 2020). All respondents analysed were from Kentucky, where livestock and related products accounted for 54% of total cash receipts from farm marketing in 2023, and where cattle and hogs make up a significant share of that livestock production (USDA, 2024). According to the USDA (2024), there are other animal product categories important to the Kentucky agriculture industry that also face unique challenges in risk management, including the growing production of poultry and related products (19.5% of 2023 farm cash receipts) through the state's hatcheries and broiler units. Also of note is the Kentucky equine industry, which, as of 2022, accounted for 209,500 horses in the state and $2.1 billion in 2021 sales (University of Kentucky, 2023). Producers of these products cannot use LRP or other risk management tools and have limited access to government programmes, thereby increasing their risk exposure. Since respondents were not asked to specify their commodities, the results are unclear on whether this affected the risk management scores of some producers.

Another limitation of note was the smaller sample size due to time constraints. Researchers were limited by a specific timeframe for recruiting participants from Kentucky's farming population for this study, resulting in a final sample of n = 112 responses. A larger sample size would most likely boost the capability to generalise results and potentially yield more significant findings regarding the relationships between financial literacy, risk management, and demographics in Kentucky farmers. To address the limitations mentioned, a longer survey period, changing the time of year surveys are administered, revising the risk management practices presented, and improving recruitment to include more in-person options are suggested for future replications of this study.

Our study examined the relationships between financial literacy, risk management use, and demographics among Kentucky farmers as of 2025. Findings from this study identified a significant predictive relationship between levels of risk management tool use and farmed acreage, farmed commodities, and respondents' education. Future research should use instrumentation that accounts for modern farm practices and includes larger sample sizes within each group to better clarify the extent to which financial literacy and risk management practices affect Kentucky farmers' productivity, income, or other demographic indicators.