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.

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.

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.

This study introduces a data-driven benchmarking method to assess the relative price and profit efficiencies of cattle auction sales. Transaction inefficiencies represent the divergence between observed sales and their corresponding maximum feasible value. Data envelopment analysis is used to assess market efficiencies and to identify the optimal array of animal characteristics based on peer comparisons. In Texas, about half of the evaluated transactions were inefficient. Furthermore, by modifying some of the observed attributes, sales prices could increase by 23% and profits improve by 74%. Efficiency results can be used to develop effective value-added management practices and educational programs.

The study uses the Living Standards Measurement Study–Integrated Surveys on Agriculture to evaluate mobile phone ownership’s direct and indirect effect on yields in Tanzania. The results indicate that transitioning from not owning to owning a mobile phone improves maize yields by about 16%. Mobile phones indirectly affect maize yield by facilitating farmers’ access to extension services – regardless of the type of provider – but only account for about 2% of the total effects. Considering both direct and indirect effects, this study suggests that extension services partially moderate this causal relationship. Further, the impact of mobile phones is stronger among male-headed farm households.

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.

Foodborne illnesses are costly to society and have been associated with local produce. The affordable “3-step wash” cleaning procedure was designed to reduce pathogens on produce. We estimate consumer willingness to pay (WTP) for food safety (i.e. 3-step washed), prepackage, and sales location attributes in locally grown produce (e.g., lettuce). On average, consumers are willing to pay $1.46 more for 3-step washed and $0.30 more for prepackaged lettuce. Additionally, consumers are willing to pay $0.16 more for fresh produce sold in natural stores and farmers markets compared to supermarkets, but $0.22 less for produce sold in other direct-to-consumer locations such as roadside stands. Higher WTP for the food safety attribute is associated with consumers who have greater risk aversion, less knowledge of foodborne illness, and stricter food safety cleaning and handling practices. Consumers highly concerned about foodborne risks also show higher WTP for both food safety and prepackage attributes. These findings can guide local farmers in making decisions about adopting pathogen-reduction cleaning procedures, selecting sales locations, and developing effective marketing strategies.

This research explores the integration of regional markets for 21 food commodities in Ghana. Utilizing co-integration tests and autoregressive distributed lag models, it investigates price linkages and market responsiveness, also considering the impact of telecommunications and road infrastructure. The study reveals that markets generally adjust quicker to price increases than decreases, with disparities noted in specific regions and commodities. It emphasizes the importance of improving internet speeds and road quality to enhance market integration, suggesting policy measures to sustain and optimize agri-food networks amid global disruptions.

A slate of class action lawsuits has been filed in California against Monsanto (and now Bayer), alleging that exposure to glyphosate, an active herbicide, has caused non-Hodgkin’s Lymphoma in people. The current litigation may create liability risks for stakeholders continuing to use glyphosate. We estimate the impact of litigation on glyphosate use in California by leveraging the court rulings awarding damages as a natural experiment. Our findings suggest that glyphosate usage in California has fallen substantially in the wake of these lawsuits. Decisions in the courtroom regarding glyphosate users’ health risks are already having a decreasing effect on pesticide use decisions in the real world.

We compared three common unit value imputation methods using household purchase data from 2018 to 2020 concerning five milk categories. Regression-based imputation outperformed household mean and retailer mean imputations, based on root mean squared error, mean absolute error, and mean absolute percent error. In a censored QUAIDS model, retailer mean imputation yielded statistically different estimates from the other two methods concerning compensated own-price and cross-price elasticities. We demonstrated that different price imputation methods used in household demand estimation generate different results in predicted prices and estimated price elasticities, and these differences may not necessarily be trivial.

Increasing consumer demand for sustainably-sourced products has created a need to benchmark sustainability at the field level. To address this issue, some companies are offering incentives to producers, but are still lacking participation. This study estimated producers’ willingness to accept for participating in sustainability programs and implementing sustainable practices at the field level using a double-bounded dichotomous-choice framework. The results revealed preferences for longer contracts in length of time, industry as the verification party, supplemental benefits that yield an economic incentive, and a per-bale payment. This project will give new insights to the value and importance of documenting, verification, and traceability throughout the supply chain.

This study uses established procedures to estimate the effects of changes in mortality and growth implant protocols on feedlot net returns (NRs). We then propose new methods for estimating concurrent impacts to feedlot greenhouse gas emissions intensity. Reducing mortality consistently increases NRs and reduces greenhouse gas emissions intensity in the feedlot regardless of sex or placement weight. Results indicate that use of two implants in the feedlot may increase NRs and reduce greenhouse gas emissions per pound of dressed beef produced, compared to just one growth implant.

Oklahoma has witnessed a remarkable expansion of its medical marijuana industry since legalization in 2018, emerging as the largest in the nation in terms of both dispensaries and growers per capita. However, the ramifications of this burgeoning sector remain largely unexplored in rural areas of the state. A focus group in one rural community provides information on the most important perceived impacts of the industry, which include influences on local housing values. An event study confirms that high-grower (but not high-dispensary) counties saw housing value increases of roughly 20% post-legalization when compared to neighboring states where marijuana remains illegal.