Purpose We examine how a platform-mediated gig work employment model would function for agriculture, addressing two distinct labor market trends: the growing prominence of the gig worker and the pervasive labor struggles for agricultural operations. The main objective of this study is to test the economic viability of this model from the perspective of potential agricultural gig employees. In doing so, we examine two barriers to adoption: producer perspectives and the practical functionality of existing platforms that connect employees and employers. Our paper concludes by providing policy implications and practical strategies for developing this kind of workforce. Design/methodology/approach The study combines self-collected data from a survey of potential gig workers, data scraped from online postings by current gig workers on agriculture-focused platforms, and administrative data to provide a comprehensive descriptive analysis of workers’ interest in gig work and their earning potential. We describe heterogeneity in the latter across a variety of tasks gig workers might perform on an agricultural operation and the characteristics of those tasks. This analysis is complemented by qualitative data from structured roundtable conversations with producers (i.e. potential employers) about their perspective on the promise and pitfalls associated with this employment model. Findings Our results examine workers’ preferences for agricultural gig work and their earning potential. Enthusiasm for gig work among our sample of potential employees was high. We observe notable wage growth for “ideal” gig tasks, which are characterized by high urgency and low trust, over time. Workers’ earning potential is augmented most by moving into tasks that are higher trust rather than higher skill. On the other hand, we observe comparably low returns to workers doing high-urgency tasks. Our exploration of barriers to adoption urges caution: we find marked reticence among potential employers and current platforms lack mechanisms that promote trust. Originality/value This paper is among the first to consider the implications of the gig work platform model for agriculture. It provides a cohesive summary of the overlap between the agricultural labor market and the gig economy and uses a coordinated combination of quantitative and qualitative data from both sides of this labor market. These complementary sources drive our ability to make unique insights about this employment model in the agricultural space. By testing the viability of platform-mediated gig work in the agricultural sector, we explore a creative and potentially actionable solution to the sector's growing need for a skilled and flexible workforce.
Purpose The purpose of this paper is to explore the returns to different functions of farm management. Various external factors influence farm incomes including volatile commodity prices, variable production costs, interest rates, and geopolitical factors, which can affect access to and prices in both input and output markets. To meet performance goals and ensure the financial health of their business, farmers need to decide how best to develop and deploy appropriate human capital across a range of management functions. Design/methodology/approach We use farm level data from cooperator members of Illinois Farm Business Farm Management association to estimate measures of latent (unobserved) financial, production, and marketing management functions. A structural equation modeling approach is used to examine how the different managerial functions contribute to farm performance. Findings Our results show that returns to financial management are positive and consistent across all time periods. Returns to production management are also positive, but always lower than returns to financial management. Returns to marketing management are positive and large relative to financial and production management but also highly variable across time and economic conditions. Originality/value These findings show that functional diversity within the farm business is important, and that the returns to different functions may change given changes in the environment. These findings have value in helping farmers to decide how best to invest in their human capital to develop skills in the different areas of functional management.
Purpose This study examines how crop insurance mitigates financial risk in accessing formal agricultural credit and how digital inclusion policies reduce transaction costs associated with borrowing. Design/methodology/approach Using farm-level survey data, the analysis investigates both credit participation and the amount of institutional credit accessed, distinguishing demand-side and supply-side constraints. Heckman's two-stage selection model is employed to account for potential sample selection and heterogeneity among borrowers. Findings The findings indicate that crop insurance plays a significant role in improving creditworthiness and willingness to engage with formal lending institutions. Insured farmers are also likely to obtain higher loan amounts compared to non-insured counterparts, reflecting reduced default risk perception by lenders. In parallel, digital inclusion measured through access to smartphones and banking services significantly lowers transaction costs related to credit application, documentation and loan disbursement. These findings suggest that both insurance mechanisms and digital tools enhance farmers' creditworthiness and lender confidence, thereby improving access to and uptake of formal credit. Research limitations/implications The study underscores the complementary role of risk mitigation and cost-reduction policies in strengthening agricultural credit. Crop insurance primarily addresses the financial risk, while digital inclusion improves procedural efficiency and access. The study highlights the importance of integrating crop insurance coverage with digital financial infrastructure to promote inclusive and resilient agricultural finance. The study is based on cross-sectional data from a specific geographic context, which may limit generalizability. Future research could explore longitudinal impacts or replicate the study in different agro-economic settings to validate the findings. Originality/value This research provides empirical evidence on the synergistic role of crop insurance and digital inclusion in improving access to agricultural finance. It contributes to the growing discourse on integrated financial services for smallholders and offers important intervention policy insights for enhancing rural credit delivery systems.
Purpose The expected rapid development of solar energy in the United States has raised concerns about its effect on high- value farmland. This study examines how commercial and utility-scale solar energy facilities influence farmland prices across Indiana from 2015 to 2020. Design/methodology/approach We estimate a hedonic price model to capture the relationship between farmland values and distance to the nearest solar facility. Quantile and expectile regressions are employed to measure effects across the conditional price distribution. Findings Our results provide consistent evidence that farmland closer to solar facilities sells at a premium. Traditional OLS results suggest a 1.4% price premium per mile closer to a solar facility, while the premium increases to 1.8% at the upper 90th expectile. Research limitations/implications Our findings support the concern that solar energy development may impact high-value farmland. These findings have significant implications for policymakers, highlighting the need to balance the economic benefits of solar energy expansion with the preservation of agriculturally productive and high-value agricultural lands, ensuring that farmers, tenants and landowners are not disproportionately affected by increasing land costs. Originality/value This paper provides new evidence on how solar energy development shapes farmland values across the price distribution, extending the standard hedonic framework through the integration of quantile and expectile regressions.
Purpose This study aims to determine whether areas and intervals with lower average precipitation exhibit greater rainfall index variability in the Pasture, Rangeland and Forage Rainfall Index (PRF-RI) program, and whether this variability widens the gap between expected payouts and premium rates, thereby raising loss ratios. Design/methodology/approach Using PRF-RI and precipitation data for 66 South Dakota counties (1948–2024), we apply a linear mixed model that accounts for correlation among overlapping bi-monthly intervals to relate index variability (coefficient of variation) to long-run average precipitation. We then link this variability to expected payouts and loss ratios by combining the PRF-RI payment calculation factor with 2024 premium rates, comparing outcomes under the empirical distribution of the rainfall index and a truncated normal distribution. Finally, we estimate the relationship between average annual precipitation and cumulative loss ratios (2016–2024) across 43 states. Findings We find that intervals and regions with lower average precipitation exhibit greater variability in the PRF-RI. Loss ratios are higher in drier intervals and regions, but this pattern is not driven by non-normality alone: the truncated normal assumption overstates expected payouts relative to the empirical distribution and can push premium rates too high. Across states, average annual precipitation is negatively related to cumulative loss ratios – states where recent precipitation (2016–2024) has fallen below long-run averages tend to experience higher losses. Originality/value This paper fills a gap in understanding PRF-RI program design by showing how long-run precipitation levels and bi-monthly rainfall variability widen the gap between expected payouts and premium rates and affect actual loss ratios – offering insights for premium rating, interval design, and the program's long-term viability under climate change.
