Historical ambiguity on how cover crop use influences future crop insurance eligibility has been proposed as one explanation for low cover crop adoption rates. However, explicit guidance on cover crop use for crop insurance participants was added in the 2018 Farm Bill. This study uses farm level data from the Agricultural Resource Management Survey to ascertain whether crop insurance participation influenced adoption of cover crops and to what degree that influence persisted after the 2018 Farm Bill. Estimation of a double hurdle model, combined with a control function approach to address endogeneity, suggests statistically and economically significant effects between crop insurance expenditures and cover crop use at the “extensive margin,” but no statistically significant effect at the “intensive margin.” Estimation on subsets of the data defined by before and after the 2018 Farm Bill suggest that the effect is primarily attributable to participation trends prior to the 2018 Farm Bill. Following the 2018 Farm Bill, no statistically significant effects are observed between cover crop use and crop insurance expenditures.

The decline in fed cattle cash sales and its impact on price discovery are concerning. This study extends existing literature by utilizing machine learning to explore factors, particularly decision trees and random forests, to explore factors influencing fed cattle price ranges, complementing traditional regression analyses. These models uncover hidden patterns and provide additional insights into the cattle market. Key variables such as weight range, head count, and trade location, are found to be associated with price ranges. Notably, the weight range emerges as the primary variable influencing the price range, with smaller weight ranges linked to lower price ranges.

We examine the convergence of lean hog futures and cash prices, focusing on the thinning of negotiated cash markets. Using daily Livestock Mandatory Reporting data from 2001 to 2024, we confirm significant non-convergence between negotiated and futures prices over the past two decades. Regression results show that as the share of negotiated transactions declines, the absolute basis increases, emphasizing the critical role of negotiated markets in ensuring convergence. These findings highlight concerns about the reliability of negotiated prices as a benchmark for contracts and offer valuable insights for price risk management in the hog industry.

Novel plant-based meat alternatives (PBMAs) have the potential to disrupt traditional meat industries, but only if consumers substitute PBMAs for meat over time. This study uses weekly household scanner data from 2018 to 2020, to estimate demand for PBMAs in the ground meat market. We use a basket-based demand approach by estimating a multivariate logit model (MVL) to determine cross-product relationships between PBMAs, ground turkey, ground chicken, and ground beef, while simultaneously exploring the role of prior consumption habits and demographics on demand. The only demographic characteristic affecting PBMA demand is the household education level of having a college degree when controlling for other factors. We found no significant seasonal difference in purchasing patterns, after controlling for cross-product effects, prior purchases, and demographics. Demand for PBMAs is driven by habit formation rather than variety seeking, as higher past purchases of PBMAs lead to a higher likelihood of current PBMA purchases. Consumers with higher past ground beef purchases are less likely to choose PBMAs, suggesting growth of this new product is coming from consumers on the margin rather than from heavy beef buyers substituting away from their traditional purchases. PBMAs and ground beef are utility complements with all meat products, suggesting that traditional meat and PBMA companies, along with retailers, should explore synergies in product marketing and offerings.

Analysis of feeder and early weaned pig markets, important segments in pork production, is nearly nonexistent. We derive and estimate a structural econometric model relating demand and supply for market hogs, feeder pigs, and early weaned pigs. Estimates from the econometric model predict how disruptions are transmitted through hog and pig markets. Results indicate that hog and pig markets are most sensitive to hog processing plant utilization relative to capacity and that this sensitivity has increased compared to prior estimates. A set of counterfactual scenarios quantify the effects of shocks to hog processing capacity, wholesale pork demand, and supply response.

Alternative disposable dinnerware treatments to per- and polyfluoroalkyl substances (PFAS) are under development. A discrete choice experiment of 1,304 U.S. consumers addressed the market’s response to bio-based alternatives. Information nudges were used to assess the impact of health and environmental information on behavior. Data were analyzed using mixed logit models. Bio-based treated plates generated premiums compared to the PFAS-treated plates. Participants exposed to either environmental or health information were willing to pay a price premium of $2.0-$2.12 for bio-based treatments. Both information nudges generated premiums for the USDA Certified Bio-based products relative to the control.

The USDA’s resilience strategy of subsidizing small meat-packer entry has prompted studies on plant size, market structure, and resilience, each study employing a different conception of resilience. None accounts for the duration and speed of slaughter downturns and recoveries. We account for these factors by developing metrics across 35 U.S. states and estimating how the metrics vary with plant size, labor conditions, and COVID-19 policies. We find medium-sized plants enhanced resilience during COVID-19, raising questions about the USDA’s narrow focus on smaller plants. This highlights the need for more nuanced strategies to strengthen the resilience of the beef processing sector.

While migration can provide economic opportunities and remittances for rural households, it may also lead to loss of skilled labor and disrupt farm operations. This study examines the impact of rural-urban migration on technical efficiency in Ghana, using robust methodology that combines propensity score matching with a difference-in-differences, selectivity-corrected stochastic frontier model. Analysis is based on panel data from 1,056 farm maize households. Results show that migration significantly improves technical efficiency and maize output. Migration history, farm characteristics, and education shape this relationship. Strengthening extension services and promoting the best farming practices are vital for improving smallholder productivity and efficiency.