Know the Farmer That Feeds You: A Cross-Country Analysis of Spatial-Relational Proximities and the Attractiveness of Community Supported Agriculture

While food production and consumption processes worldwide are characterized by geographical and social distance, alternative food networks aim to reconnect producers and consumers. Our study proposes a framework to distinguish multiple dimensions of proximity in the context of Community Supported Agriculture (a type of alternative food network) and to quantitatively evaluate them. In a principal component analysis, we aggregated various detailed proximity items from a multinational survey using principal component analysis and examined their relationship with the attractiveness of Community Supported Agriculture in a multiple regression analysis. Our findings highlight the importance of relational proximity and thus of increasing trust, collaboration, and the sharing of values and knowledge within and across organizations in the food system. Rather than focusing on spatial proximity, increasing relational proximity might support alternative food networks, such as Community Supported Agriculture.

Using food network analysis to understand meal patterns in pregnant women with high and low diet quality

Background Little is known about how meal-specific food intake contributes to overall diet quality during pregnancy, which is related to numerous maternal and child health outcomes. Food networks are probabilistic graphs using partial correlations to identify relationships among food groups in dietary intake data, and can be analyzed at the meal level. This study investigated food networks across meals in pregnant women and explored differences by overall diet quality classification. Methods Women were asked to complete three 24-h dietary recalls throughout pregnancy (n = 365) within a prospective cohort study in the US. Pregnancy diet quality was evaluated using the Healthy Eating Index-2015 (HEI, range 0-100), calculated across pregnancy. Networks from 40 food groups were derived for women in the highest and lowest HEI tertiles at each participant-labeled meal (i.e., breakfast, lunch, dinner, snacks) using Gaussian graphical models. Network composition was qualitatively compared across meals and between HEI tertiles. Results In both HEI tertiles, breakfast food combinations comprised ready-to-eat cereals with milk, quick breads with sweets (e.g., pancakes with syrup), and bread with cheese and meat. Vegetables were consumed at breakfast among women in the high HEI tertile only. Combinations at lunch and dinner were more varied, including vegetables with oils (e.g., salads) in the high tertile and sugary foods with nuts, fruits, and milk in the low tertile at lunch; and cooked grains with fats (e.g., pasta with oil) in the high tertile and potatoes with vegetables and meat in the low tertile at dinner. Fried potatoes, sugar-sweetened beverages, and sandwiches were consumed together at all main meals in the low tertile only. Foods were consumed individually at snacks in both tertiles; the most commonly consumed food were fruits in the high HEI tertile and cakes & cookies in the low tertile. Conclusions In this cohort of pregnant women, food network analysis indicated that food combinations differed by meal and between HEI tertiles. Meal-specific patterns that differed between diet quality tertiles suggest potential targets to improve food choices at meals; the impact of meal-based dietary modifications on intake of correlated foods and on overall diet quality should be investigated in simulations and intervention studies. Trial registration PEAS was registered with number NCT02217462 in Clinicaltrials.gov on August 13, 2014.

Quantifying differences in alternative food network supply chain activities and their relationship with socio-economic outcomes

Alternative food networks (AFNs) have the potential to enhance and redistribute value in favour of producers and consumers through novel ways of organizing supply chain activities. What is more, AFNs are often characterized by their ‘sustainability promise’ – or the idea that their networks foster social, ecological or environmental improvements over conventional food networks. Based on a purposive sample of 286 producers across five Swedish AFNs (i.e. community supported agriculture, REKO-rings, farmers’ markets, farm stores and food nodes), we explore how differences in how supply chain activities are managed and relate them to profitability, fair wages, cooperation, logistics efforts, happiness and future beliefs. Using a combination of correlation analysis, linear regression and means comparisons, we challenge the notion that AFNs achieve their sustainability promise or enhance value through novel combinations of supply chain activities. Our findings include several key differences in how supply chain management (SCM) activities are organized across AFNs and their variant importance for profitability. Moreover, we find significant differences in happiness across AFNs that are better explained through beliefs about the future than profitability or fair wages. By exploring happiness and profitability, we offer insights into why some AFN actors thrive despite poor economic returns.