The COVID‐19 pandemic initially caused worldwide concerns about food insecurity. Tweets analyzed in real‐time may help food assistance providers target food supplies to where they are most urgently needed. In this exploratory study, we use natural language processing to extract sentiments and emotions expressed in food security‐related tweets early in the pandemic in U.S. states. The emotion joy dominated in these tweets nationally, but only anger, disgust, and fear were also statistically correlated with contemporaneous food insufficiency rates reported in the Household Pulse Survey; more nuanced and statistically stronger correlations are detected within states, including a negative correlation with joy.

JAFSCD: Journal of Agriculture, Food Systems, and Community Development. Publication of the Thomas A. Lyson Center for Civic Agriculture and Food Systems

The location quotient (LQ) measures regional industry concentration with the advantages of easy calculation and interpretation. However, it is a weak method for identifying industry clusters that consist of related industries geographically concentrated in contiguous counties. This paper proposes a new spatial input–output location quotient (SI-LQ) accounting for both the co-location of related industries and the spatial spillover of concentration into neighbouring counties. A bootstrap method is used to determine the cut-off values of the new measure. The practical advantages of the SI-LQ over the traditional LQ include attenuation of the extreme values of the LQ in less populous and remote counties and the identification of large substantive clusters. The SI-LQ outperforms the LQ in a regression analysis of the effect of industry concentration on total employment growth.

The U.S. urban‐rural export gap is important given the share of manufactured goods in exports, the rural concentration of manufacturing activity, and recent federal investments in place‐based policy. These investments raise questions about the size of the export gap and whether it is explained by differences in endowments or inherent rural disadvantages. Confidential trade data linked with business survey data alongside county‐level variables allow an Oaxaca‐Blinder decomposition of the export gapUrban factors compensate for a less trade favorable industry mix, including the advantages of urban scale in export performance which may be insurmountable for rural areas.

Agritourism is growing worldwide as farmers and ranchers seek alternative sources of revenue, and consumer demand for agricultural experiences is on the rise. Understanding this sector is important for policymakers, researchers, agricultural service providers, and others seeking to support farm viability and rural entrepreneurship. However, in the U.S., this support is hampered by the lack of a clear definition and consistent, comprehensive means for measuring the agritourism sector. The best available data for the U.S. are from the quinquennial U.S. Department of Agriculture National Agricultural Statistics Service (USDA NASS)’s Census of Agriculture. However, the two questions used by the Census of Agriculture that relate to agritourism are worded in ways that limit a comprehensive understanding of the size and scope of the sector. To illustrate the limitations, we highlight cases from two U.S. states (Texas and Vermont), where different forms of agritourism are present. One such form, hunting, is included in the USDA’s economic assessments of agritourism; another, pick-your-own berries, is not. Along with tastings and purchases of locally grown products, this falls in the category of direct sales. The discrep­ancy can result in misrepresentation and misinter­pretation of the data in analyses and subsequent publications with distorted policy recommenda­tions related to agritourism. We discuss these cases alongside recommendations on how to more accurately measure, and support, agritourism development in the U.S.