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Purpose – Despite the existence of hundreds of studies and several review articles on consumer preferences for genetically modified (GM) food, it remains difficult to ascertain the current state of knowledge on the topic. The purpose of this chapter is to distill some of the key findings from the body of research on consumer preferences for GM food.

Approach – In reviewing key pieces of literature, including two meta-analyses, the chapter identifies four key unresolved questions and includes discussions on how the questions might be resolved.

Findings – The chapter identifies four questions in need of additional thought and research. The questions relate to (1) why the market for GM-free food is so small in the United States despite the large estimated willingness-to-pay premiums for GM-free food, (2) why consumers remain so uninformed about biotechnology despite their seemingly high levels of aversion, (3) why economists have generally ignored the information-content of GM food policies, and (4) why it is so difficult to determine why U.S. and European consumers have seemingly reacted so differently to GM foods.

Value – This chapter should be useful to those interested in learning about the current state of knowledge on consumer preferences for GM food, and to those seeking to identify areas in need of additional research.

While previous studies have looked at the negative consequences of beer drinking often as a prelude to discussing benefits of laws that curtail consumption, the purpose of this paper is to understand the downside of such regulations insofar as reducing entrepreneurial activity in the brewing industry.

Using a unique data set from the Brewers’ Association that contains information on the number and type of brewery in each county, this study explores the relationship between the number of breweries and regulations targeted at the brewing industry. Zero-inflated negative binomial regressions are used to determine the relationship between the number of microbreweries and brewpubs per county and state beer taxes, self-distribution legislation, and on-premises sales.

The authors find that allowing breweries to sell beers on-premises as well as allowing for breweries to self-distribute have statistically significant relationships with the number of microbreweries, brewpubs, and breweries. The authors do not find an economically significant relationship between state excise taxes and the number of breweries of any type.

Results suggest that whatever public health benefits are brought about by alcohol laws, they are not a free lunch, as they may hinder entrepreneurial development.

This paper investigates whether individuals’ risk-taking behavior is affected by background risk by analyzing individuals’ choices over a series of lotteries in a laboratory setting in the presence and absence of independent, uncorrelated background risks. Overall, our results were mixed. We found some support for the notion that individuals were more risk averse when faced with the introduction of an unfair or mean-preserving background risk than when no background risk was present, but this finding depends on how individuals incorporate endowments and background gains and losses into their utility functions and how error variance is modeled.

Smart farming technologies (SFTs) can increase yields and reduce the environmental impacts of farming by improving the efficient use of inputs. This paper is to estimate farmers' preference and willingness to pay (WTP) for a well-defined SFT, smart drip irrigation (SDI) technology.

This study conducted a discrete choice experiment (DCE) among 1,300 maize farmers in North China to understand their WTP for various functions of SDI using mixed logit (MIXL) models.

The results show that farmers have a strong preference for SDI in general and its specific functions of smart sensing and smart control. However, farmers do not have a preference for the function of region-level agronomic planning. Farmers' preferences for different functions of SDI are heterogeneous. Their preference was significantly associated with their education, experience of being village cadres and using computers, household income and holding of land and machines. Further analysis show that farmers' WTP for functions facilitated by hardware is close to the estimated prices, whereas their WTP for functions wholly or partially facilitated by software is substantially lower than the estimated prices.

Findings from the empirical study lead to policy implications for enhancing the design of SFTs by integrating software and hardware and optimizing agricultural extension strategies for SFTs with digital techniques such as videos.

This study provides initial insights into understanding farmers' preferences and WTP for specific functions of SFTs with a DCE.

The purpose of this paper is to use prediction markets to forecast an agricultural event: United States Department of Agriculture's number of cattle on feed (COF). Prediction markets are increasingly popular forecast tools due to their flexibility and proven accuracy to forecast a diverse array of events.

During spring 2008, a market was constructed comprised of student traders in which they bought and sold contracts whose value was contingent on the number of COF to be reported on April 18, 2008. During a nine‐week period, students were presented three types of contracts to forecast the number of COF. To estimate forecasts a uniform price sealed bid auction mechanism was used.

The results showed that prediction markets forecasted 11.5 million head on feed, which was about 1.6 percent lower than the actual number of COF (11.684 million). The prediction market also fared slightly worse than analysts' predictions, which on average suggested there would be about 11.795 million head (an over‐estimate of about 1 percent).

The contribution of this study was not to provide conclusive evidence on the efficacy of using prediction markets to forecast COF, but rather to present an empirical example that will spark interest among agricultural economists on the promises and pitfalls of a research method that has been relatively underutilized in the agricultural economics literature.

In recent years, the craft beer (CB) industry has gained impetus and has experienced significant growth in scientific publications. This study aims to present a systematic review of the literature on CB in areas related to economic and business sciences.

Based on the data from Scopus, Web of Science and a set of articles not indexed to these databases until June 2021, a total of 132 articles were included for analysis, using bibliometric and content analysis techniques.

The study allowed us to identify that CB has four main clusters/themes of research, namely, CB industry and market, marketing and branding, consumer behavior and sustainability. Detailed information on the clusters is provided. In addition, the results showed that publications addressing CB have grown significantly from 2015 onwards and are dispersed across many journals, with none assuming a clear leadership. Quantitative approaches account for more than half of publications.

This study is a useful guide for academics intending to develop studies with CB. It provides a framework to structure future research by identifying existing literature clusters and proposes several research propositions.

The findings from this study are useful for CB companies to get an overview of the main issues affecting the CB industry and market to be able to adapt their strategies and stay aligned with market tendencies in the four main clusters identified.

This is the first systematic review of CB. Therefore, it provides a significant contribution to frame and strengthening the literature on CB and serves as a reference for future research. Based on the content analysis and cluster identification, the findings portray the status of current research. Accordingly, a set of research opportunities are offered.

The study investigates whether there is an association between climate types and farm risk attitudes of principal operators.

The study exploits temperature variation in the diverse climate types across the US and defines hot- and cold-climate states. Ordered logit and generalized ordered logit models are used to model principal operators' farm risk attitudes, which are measured on a Likert scale. The study uses two datasets. The first dataset is a 2017 survey of US large commercial producers (LCPs). The second dataset provides a Köppen-Geiger climate classification of the US at a spatial resolution of 5 arcmin for a 25-year period (1986–2010).

The study finds that principal operators in hot-climate states are 4–5% more likely to have a higher willingness to take farm risk compared to principal operators in cold-climate states.

It is likely that farm risk mitigation decisions differ between hot- and cold-climate states. For instance, the authors show that corn acres' enrollment in federal crop insurance and computers' usage for farm business are pursued more intensely in cold-climate states than in hot-climate states. A differentiation of farm risk attitude by hot- and cold-climate states may help agribusiness, the government and economists in their farm product offerings, farm risk management programs and agricultural finance models, respectively.

Based on Köppen-Geiger climate classification, the study introduces hot- and cold-climate concepts to understand the relationship between climate types and principal operators' farm risk attitudes.