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.

Constrained econometric techniques hamper investigations of disease prevalence and income risks in the shrimp industry. We employ an econometric model and machine learning (ML) to reduce model restrictions and improve understanding of the influence of diseases and climate on income and disease risks. An interview of 534 farmers with the models enables the discernment of factors influencing shrimp income and disease risks. ML complemented the Just-Pope production model, and the partial dependency plots show nonlinear relationships between income, disease prevalence, and risk factors. Econometric and ML models generated complementary information to understand income and disease prevalence risk factors.

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.

Here we examine product attributes present in dry dog food to show there exist potential price premiums and discounts associated with health and wellness attributes in dry dog food. The findings indicate price premiums are associated with attributes related to digestion and allergy care. Pricing discounts are found to be associated with immune support and dental attributes. The results of this study are anticipated to be a starting point for more sophisticated and dynamic analysis of pricing and willingness-to-pay studies in the pet food industry.

To sustain its agricultural output, the U.S. relies heavily on imports of potash. Using two different methodologies, this study finds that U.S. import demand falls by approximately four percent in the short run and nine percent in the long run for a 10% rise in the potash import price. Price transmission with leading exporter Canada is imperfect, with U.S. prices rising only 7.7% in the short run when Canadian prices rise by 10%. The findings suggest that policies such as import tariffs would raise costs for U.S. agricultural producers because there are few potash substitutes.

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.