Integrating farmers’ preferences into the breeding and dissemination of new genotypes is a effective approach to enhance their successful adoption by farmers. In the case of sweet potato, a staple crop in many parts of West Africa, there is a need for more research on the selection criteria used by farmers when choosing which varieties to grow. This study aims to highlight farmers’ selection criteria for sweet potato varieties in the main production areas in Benin. A total of 480 farmers from the top three sweet potato production areas were surveyed. The relative importance of various traits for sweet potato farmers was evaluated using best-worst scaling methods. Latent class analysis was applied to find groups of farmers with similar preferences. Best-Worst Scaling analysis revealed that high root yield, root size, marketability, and early maturing were the most important variety selection criteria. Latent class analysis revealed three farmers’ groups referred to as ‘Yield potential’, ‘Market value’, and ‘Plant resilience’ classes. ‘Yield potential’ farmers were more likely to be from Atlantique and Alibori departments; they significantly committed more acreage to sweet potato production. The ‘Market value’ farmers highlighted the variety of root size and commercial value as the main selection criteria and consisted of farmers with primary education levels from the Ouémé department. ‘Plant resilience’ refers to a group of Alibori farmers who prioritize environmental issues and primarily grow sweet potatoes for self-consumption. Our findings shed light on farmers’ preferences and suggested that heterogeneity in sweet potato selection criteria was highly influenced by various socio-economic factors and location.

Hard cider is a sector of a maturing craft beverage industry that continues to experience growth in the United States. Cider is also experiencing challenges, however, such as competition from other alcohol markets, changing consumer preferences, the supply chain, and inflationary pressures. National policy changes may help promote more optimal outcomes for this sector, but public support is important to policy formation. This study uses survey data from a best-worst scaling experiment of consumers in four leading cider-producing states (Michigan, Washington, Wisconsin, and Vermont) to understand preferences toward ten broad cider policy initiatives. The results of multinomial logistic modeling reveal that consumers prefer policies mandating ingredients, nutrition facts, and allergen labeling across all ciders. The least preferred policy initiatives include allowing producers to use vintage on labeling and funding regional cider development. These results have important implications for stakeholders across the industry, including the benefits of labeling disclosures in marketing and the need to improve public awareness of barriers to cider industry development.

Consumer misperception and misinterpretation of food labels can lead to consumers not buying a product or purchasing products that do not align with their environmental or sustainability interests. Consumer purchasing behavior can be explained by looking at consumer food values or food quality attributes. This study aimed to (a) determine the effect label information has on consumer preference shares for selected sustainability-related food labels and (b) if correlations exist between food labels and food values. To the best of our knowledge, this is the first study to examine the comprehension of 12 different labels and identify how food labels relate to food value preferences. Responses from the best-worst scaling experiment of food value and environmental food label choice sets were analyzed using the random parameter logit model. Results reveal preference shares changed for each label as more information was provided to the respondents about the various labels included in the study. These findings should support food policy efforts requiring strict, clear label standards. Food labels should represent the food’s core food values to increase consumer preference for the product. These findings also further support the need for efforts to increase consumer knowledge and understanding of the labels on food packaging.

In this study, we sought to explain why dairy farmers give importance to various use and non-use values of animal welfare. In particular, we examined how the farmers could be segmented according to the relative importance they give to various use and non-use values in animal welfare and whether this segmentation could be explained by farmers’ personal values and personality traits. Based on a latent class analysis using best-worst scaling data on 123 Swedish dairy farmers, three segments of farmers were found: animalcentred, human-centred and business-orientated. These groups were related to measures of farmers’ personal values and personality traits in a point-biserial correlation and a hierarchal multinomial logistic regression analysis. The results suggest that the segmentation is related to personal values, but not to personality traits. This finding is important from a policy perspective, because the existence of different segments of farmers who are motivated by different values in animal welfare indicates a need to approach different farmers in different ways if policy is to succeed in improving animal welfare. It also indicates a possibility to influence the segments to improve animal welfare by measures that are sensitive to value dynamics of the farmers.

Discrete choice experiments are used to collect data that facilitates measurement and understanding of consumer preferences. A sample of 750 respondents was employed to evaluate a new method of best-worst scaling data collection. This new method decreased the number of attributes and questions while discerning preferences for a larger set of attributes through self-stated preference “filter” questions. The new best-worst method resulted in overall equivalent rates of transitivity violations and lower incidences of attribute non-attendance than standard best-worst scaling designs. The new method of best-worst scaling data collection can be successfully employed to efficiently evaluate more attributes while improving data quality.

The objective of this research was to evaluate producers’ perspectives of four key precision agriculture technologies (variable rate fertilizer application, precision soil sampling, guidance and autosteer, and yield monitoring) in terms of the benefits they provide to their farms (increased yield, reduced production costs, and increased convenience) using a best-worst scaling choice experiment. Results indicate that farmers’ perceptions of the benefits derived from various precision agriculture technologies are heterogeneous. To better understand farmers’ adoption decisions, or lack thereof, it is important to first understand their perceptions of the benefits precision agriculture technologies provide.

The U.S. livestock industry is increasingly faced with pressure to adjust practices in response to societal concerns—specifically related to farm animal welfare. Using best-worst scaling, we determine which practices the U.S. public and cow-calf producers view as the most effective and most practical practices to improve beef cattle welfare. Latent class models are used to understand heterogeneity within and across the public and producers. Fresh, clean feed and water was viewed by most groups as both effective and practical. Furthermore, castrate with pain control and dehorn with pain control were seen as the least effective and practical practices.

Objectives: Several studies have reported multiple barriers to and facilitators for the uptake of health technology assessment (HTA) information by policy makers. This study elicited, using best-worst scaling (BWS), the most important barriers and facilitators and their relative weight in the use of HTA by policy makers.

Methods: Two BWS object case surveys (one for barriers, one for facilitators) were conducted among sixteen policy makers and thirty-three HTA experts in the Netherlands. A list of twenty-two barriers and nineteen facilitators was included. In each choice task, participants were asked to choose the most important and the least important barrier/facilitator from a set of five. We used Hierarchical Bayes modeling to generate the mean relative importance score (RIS) for each factor and a subgroup analysis was conducted to assess differences between policy makers and HTA experts.

Results: The five most important barriers (RIS > 6.00) were “no explicit framework for decision-making process,” “insufficient support by stakeholders,” “lack of support,” “limited generalizability,” and “absence of appropriate incentives.” The six most important facilitators were: “availability of explicit framework for decision making,” “sufficient support by stakeholders,” “appropriate incentives,” “sufficient quality,” “sufficient awareness,” and “sufficient support within the organization.” Overall, perceptions did not differ markedly between policy makers and HTA experts.

Conclusions: Our study suggests that barriers and facilitators related to “policy characteristics” and “organization and resources” were particularly important. It is important to stimulate a pulse at the national level to create an explicit framework for including HTA in the decision-making context.

We administered an online choice experiment to a sample of U.S. raw-oyster consumers to identify factors influencing preferences for Gulf of Mexico oysters, determined the extent of preference heterogeneity, and estimated marginal willingness to pay for specific varieties and other key attributes. Results indicate significant preference heterogeneity among select varieties, with non-Gulf respondents estimated to require a price discount on Gulf oyster varieties on the order of $3–$6/half dozen. Gulf respondents were found to be less sensitive to oyster variety, and estimated to be willing to pay a price premium only for select Gulf varieties on the order of $0–$3/half dozen.

Scientists and beef industry participants are investigating ways to improve the healthiness of beef. We report results of a nationwide mail survey developed to determine Consumers' preferences for fat content in ground beef and identify how consumers would most like to improve the healthiness of beef. The results from a choice-based conjoint experiment indicate that consumers place significant value on reducing saturated fat and the Omega 6:3 ratio in ground beef, but were relatively unconcerned about conjugated linoleic acid. The relatively new method of best-worst scaling was used to further identify which methods consumers most preferred producers use to improve fat content in beef. The results indicate consumers preferred feeding cattle a grass-fed diet as opposed to supplementing cattle feed with fish-meal or flaxseed to improve the fatty acid content in beef. Although consumers were receptive to the idea of using genetic testing to breed only those cattle with improved fatty acid content, using cloning to achieve this end, was viewed as very undesirable.

Objectives: There is a growing need for efficient procedures for identification of emerging technologies by horizon scanning systems. We demonstrate the value of best-worst scaling (BWS) in exploring clinicians’ views on emerging technologies that will impact outcomes in hepatocellular carcinoma (HCC) in the next 5 to 10 years.

Methods: Clinicians in Asia, Europe, and the United States were surveyed and their views about eleven emerging technologies relevant to HCC were explored using BWS (case 1). This involved systematically presenting respondents with subsets of five technologies and asking them to identify those that will have the most and least impact on HCC within 5 to 10 years. Statistical analysis was based on sequential best-worst and analyzed using conditional logistic regression.

Results: A total of 120 clinicians uniformly distributed across ten countries completed the survey (37 percent response rate). Respondents were predominately hepatologist (41 percent) who focused on HCC (65 percent) and had national influence in this field (39 percent). Respondents viewed molecular targeted therapy (p < .001) and early detection of HCC (p < .001) as having most potential, while improved surgical techniques (p < .001) and biopsy free HCC diagnostics (p < .001) were viewed upon negatively.

Conclusions: We demonstrate that BWS could be an important research tool to facilitate horizon scanning and HTA more broadly. Our research demonstrates the value of including clinicians’ preferences as a source of data in horizon scanning, but such methods could be used to incorporate the opinions of a broad array of stakeholders, including those in advocacy and public policy.

Few hard data are available on emergent diseases. However, the need to mitigate and manage emergent diseases has prompted the use of various expert consultation and opinion elicitation methods. We adapted best-worst scaling (BWS) to elicit experts' assessment of the relative practicality and effectiveness of measures to reduce human exposure to E. coli O157. Cattle vaccination was considered the most effective and hand-washing was considered the most practical measure. BWS proved a powerful tool for expert elicitation as it breaks down a cognitively burdensome process into simple, repeated, tasks. In addition, statistical analysis of the resulting data provides a scaled set of scores for the measures, rather than just a ranking. The use of two criteria (practicality and effectiveness) within the BWS process allows the identification of subsets of measures judged as potentially performing well on both criteria, and conversely those judged to be neither effective nor practical.