Appearance properties of vegetables can affect consumers' acceptance toward them as well as purchase intent. Hence, appearance is highly associated with quality of edamame (Glycine max (L.) Merr.), a protein-rich vegetable that is experiencing increased sales in the USA. Edamame is a high-value specialty crop and its production has been promoted in Virginia and other states in the USA where the tobacco production has decreased in the last decade. To support current efforts to develop the edamame industry in the USA, consumer and color data of 10 edamame genotypes grown in Virginia were analyzed in this follow-up study to understand the role of overall appearance and color characteristics in consumers' acceptability of edamame beans. In two consecutive years, untrained adult volunteers used 9-point hedonic scales (1 = “dislike extremely”, 9 = “like extremely”) to evaluate appearance and overall liking of edamame samples (cooked and shelled edamame beans) and our researchers measured the reflective color of the samples with a chroma meter. In the first year, sensory panelists also completed a choice-based conjoint analysis to determine their willingness-to-pay (WTP) for dark vs. light green edamame beans in a salad. Edamame genotypes were significantly different in appearance and overall liking (p < 0.05) and the genotype R14-16195 was the most liked overall. Hedonic scores and color were significantly affected by “year” (p < 0.05), so intentional changes between years (e.g., sample preparation) should be avoided in future studies. Consumers showed higher WTP for dark green edamame beans. Additionally, green intensity (color index) and a* color coordinate were correlated to appearance liking scores (p < 0.05), which suggests color data can support breeding selection criteria and possibly predict consumer acceptability. Employing color measurement as quality control method can help improve harvest procedures, post-harvest handling, and define edamame quality standards for the USA market.

There has been little work relating food flows with food insecurity, and how inter‐county trading can drive differences across the rural/urban divide. Using rural–urban continuum codes in combination with a new data set on food flows at the county level we examine the correlation between food insecurity and trade. Results indicate heterogeneous impacts of food trade flows and county types on food insecurity rates. From Oaxaca‐Blinder decompositions we find that differences between rural and metropolitan counties are largely due to explained factors, but differences between nonmetropolitan, nonrural counties and the other county types are unexplained factors.