Bovine trichomoniasis is a venereal disease that causes significant losses in the US beef industry. The USDA Animal and Plant Health Inspection Service views bovine trichomoniasis as endemic and delegates control to state agencies and producers. Disease management’s positive externalities are not reflected in a producer’s profit maximization problem, leading to potentially suboptimal levels of control. Our objective was to assess the economic impacts of 50% and 100% reductions of herd-level bovine trichomoniasis prevalence. The cumulative present value of net welfare increased by $388.856 and $193.222 million under the 100% and 50% scenarios, respectively. Feeder cattle producers and retail beef consumers benefit most from enhanced control.

This study analyzes empirically and graphically the effects of the relative market price level on the premiums and discounts received associated with alfalfa hay quality. Findings indicate that at times when alfalfa prices are elevated relative to the average, there is no significant impact on the average premiums and discounts received. Conversely, at times when alfalfa prices are depressed relative to the average, there are statistically significant variations in the premiums and discounts across quality grades compared with a market characterized by average prices. Understanding these dynamics can aid producers in optimizing production decisions and assist consumers in managing input costs.

Relationships between broiler cut prices are evolving as production and consumer preferences change. Stronger price relationships are observed within white and dark meats categories than between them. This can be attributed to the unique substitutability of cuts within categories, while weaker relationships between categories arising from their roles, white meats are predominantly consumed domestically, while dark meats are more oriented towards export. The analysis also shows that own price is the main driver of price variation, suggesting that treating chicken as a single category may be misleading. Considering different cuts may offer valuable insights into economic/market analysis for other meats.

The 2018/2019 trade conflict between the United States and China impacted a broad array of agricultural products, including soybeans. Previous trade studies using gravity models fail to account for trends and complex seasonal patterns observed in the data. This study uses a machine learning (ML) approach to estimate losses in soybean export value and volume from the trade war. We find that models using ML techniques outperform traditional models and estimate losses in the value of soybean exports of $10.16 billion/year. The ML models fit the complex export trade data series well, highlighting the importance of utilizing improved modeling approaches.

India is a leading producer as well as one of the largest exporters of rice. Export competitiveness of rice thus plays a crucial role in India’s overall trade scenario, particularly in bridging the gaps in country’s soaring trade deficits. The study investigates the impact of key determinants on India’s rice export competitiveness for the period 1990–2020 using the autoregressive distributed lag model. The results reveal that increasing yield of rice and depreciated exchange rate have significant positive impact on India’s rice export competitiveness. In the long run, production cost and export price have a detrimental effect on export of rice from India. However, India’s trade openness and export price do have a mixed effect on export competitiveness of rice in the short run. Moreover, the findings of this study offer some important policy implications for the stakeholders like farmers, exporters, and the government to improve export competitiveness of Agri-products including rice.

Consumer preferences are often influenced by reference-dependent preferences. This study investigates the influence of reference-dependent preferences on the estimation of willingness to pay (WTP) for table grape attributes elicited by a second-price auction. We evaluate two models: the attribute-based reference dependence model, where individuals compare the target product’s attributes with their favorite ones, and the alternative-based reference dependence model, where comparisons are made with a reference product. Results show that including reference points impacts the WTP estimation for different attributes, with varying levels of loss aversion, suggesting the attribute-specific influence of reference points.