The dairy industries in California, Idaho, and New Mexico expanded rapidly during the early 2000s. This study focuses on the expansion effects on milk-to-hay price responsiveness. Dairy industry expansion makes hay markets tighter, with less available marketable supply in most periods. The empirical models account for the expansion effect as well as those from hay exports and low stocks-to-use ratios that also cause changes in hay market demand characteristics. The results show that hay-to-milk price responsiveness increased after dairy expansion in all analyzed states. Low stocks-to-use and high exports dampened the responsiveness, but were not statistically significant for all analyzed states.

That exchange rates strongly influence agricultural commodity prices is a widely held belief. Observed divergences in price and exchange rate correspondence over time, however, have occasionally brought this conventional wisdom into doubt. We empirically test and find evidence to support hypotheses that key supply-use factors, such as low stocks and policy shifts, intermittently cause greater responsiveness of agricultural commodity prices to exchange rate changes because they give rise to more inelastic market demand. After accounting for these longrun effects, we also find that short-run price responsiveness to exchange rate changes is sometimes greater due to overshooting factors.

The United States (U.S.) cheese sector has been steadily growing throughout the years. Since 1980, U.S. consumers have doubled their annual consumption of cheese, currently at about 37.9 lbs. per capita in 2018 excluding cottage cheese. Cheese varieties are generally classified as American type (Cheddar, Monterey and others), Italian type (Mozzarella and others), and Other type (Swiss, Muenster and others) since they serve different markets. American cheese is consumed regularly in hamburgers, sandwiches and in similar settings. Italian cheese is typically consumed in pizzas, pasta and Italian cuisine. Despite the burgeoning growth in cheese demand, there has been no study addressing the dynamic price relationship among different varieties of cheese. This study investigates the price discovery process among cheese varieties: Cheddar, Mozzarella, Swiss, Muenster, and Monterey by using a vector error correction model and standard innovation accounting. Results indicate relative price interaction among different varieties of cheese, providing empirical evidence of some decouplment or separation among American (Cheddar and Monterey), Italian (Mozzarella), and Other type (Swiss). An exemption is Muenster which despite being classified as Other type of cheese responds to American’s Cheddar.

The purpose of this paper is to describe the main ways in which large amounts of information have been integrated to provide new measures of food consumption and agricultural production, and new methods for gathering and analyzing internet-based data.Design/methodology/approachThis study reviews some of the recent developments and applications of big data, which is becoming increasingly popular in agricultural economics research. In particular, this study focuses on applications of new types of data such as text and graphics in consumers' online reviews emerging from e-commerce transactions and Normalized Difference Vegetation Index (NDVI) data as well as other producer data that are gaining popularity in precision agriculture. This study then reviews data gathering techniques such as web scraping and data analytics tools such as textual analysis and machine learning.FindingsThis study provides a comprehensive review of applications of big data in agricultural economics and discusses some potential future uses of big data.Originality/valueThis study documents some new types of data that are being utilized in agricultural economics, sources and methods to gather and store such data, existing applications of these new types of data and techniques to analyze these new data.

The Chicago Mercantile Exchange introduced a futures contract for distillers’ dried grains (DDGs) in early 2010, but the market became inactive only four months after its inception. While many new futures contracts do not develop into high‐volume traders, interest from DDG cash market participants indicated that this contract could be successful. Prompted by the unexpected lack of trading activity in this new futures market, we empirically revisit the question of what factors contribute to a futures contract's success and extend the literature by investigating the roles of market participants and the significance of supporting futures markets. Estimation results indicate that the market participant type—hedger or speculator—affects futures contract trade volume. More importantly, we find that the viability of new futures contracts for commodities that are jointly produced with other commodities is impacted by hedgers’ trade volume of the related futures contract. These results provide important additions into the portfolio of indicators used by commodity exchanges to more cost‐effectively evaluate new futures contract products.

The organization of the Baton Rouge, Louisiana, retail food industry was analyzed to determine whether spatial competition influenced the cost and availability of food items. Using a spatial competition gravity variable, the costs of two separate market baskets were analyzed in January 2009, and the factors influencing spatial competition were determined. Store type (chain or supercenter) was found to be the most significant determinant of food costs, validating findings of past studies. Although food was not found to be more expensive in low-income areas, results suggest that residents in low-income and rural areas have disincentives to purchase affordable, available healthy food due to the spatial organization of their local food market.

The dominant market where information is discovered plays the role of price leader providing substantial market information to other markets. This study investigates the dynamic relationships of 30 cattle markets across regions, cattle types, and cash/futures markets. The comparison of many markets, using an error correction model, is accomplished with the introduction of a tournament with a hierarchical cluster analysis, which allows us to conclude that the leading price for the U.S. cattle markets is discovered in the futures markets for both feeder and fed cattle.

The purpose of this paper is to show the impact of digital agriculture on food supply chain, research trend, emphasis and implications for future research.Design/methodology/approachThe paper analyzes how the digital technology reshapes the production, assembly, transaction, retail and logistics. Impact from each main technological progress is discussed.FindingsFirst, digital agriculture develops quickly and changes all parts of the food supply chain. Second, while many technological progresses show their impacts in agriculture and food sector, e-commerce and progress of artificial intelligence show its comprehensive impact on the argi-food sector.Originality/valueThe paper shows the technological trend and progress in food and agriculture sector. Researchers focusing on agricultural economics and agribusiness should pay attention to recent developments in the real world, know the recent developments from other disciplines, get more data for empirical research and show the impact of digital agriculture on consumer's preference and social welfare.