Placing community supported agriculture in local food systems

ABSTRACT

Community supported agriculture (CSA) has grown in recent years as a marketing and distribution option for farmers. CSA involves ‘shareholders’ subscribing to a regular share of a farm’s harvest. The experiential structure of CSA provides opportunities for farmers to develop relationships with consumers and strengthen their local food systems (LFSs). By bringing consumer perspectives into view, LFS development personnel and other stakeholders may better understand where their residents place value within their unique systems. In this manuscript, we focus on the place of CSA in LFSs. Using survey responses from 13 communities in the Southeastern United States, we ask how different aspects of LFSs are perceptually linked to CSA by consumers. From these responses, residents seem to be more aware of CSA if they have knowledge about other direct markets – such as farmers markets or specialty retail stores – or are exposed to local food campaigns and branding efforts. Similarly, residents more positively evaluate CSA performance if they have similar evaluations of direct markets and experience with on-farm activities. These patterns differ depending on the location of the respondent. We see a role for place-based LFS promotion activities and consumer education programmes in most communities.

KEYWORDS:

• Local food metrics
• community supported agriculture
• consumer perceptions of local food

Introduction

Community supported agriculture (CSA) has grown as a direct-to-consumer (DTC) marketing option for diversified produce growers over the past 15 years in the United States.¹ The CSA model involves consumers – often termed ‘shareholders’ – subscribing to a weekly (or other set interval) share of a farm’s harvest. With subscription CSAs, farmers can estimate their potential sales and costs each year prior to the growing season. This baseline knowledge and pre-harvest subscription capital can aid in decisions on whether to diversify their market channels, scale-up production in selected vegetable varieties, and/or pursue certifications (e.g. food safety or organic) that facilitate entry into different market channels. Farmers might also use cash on hand to pay for space and labour at farmers markets and food festivals which are important venues for reaching new potential CSA shareholders, institutional buyers, and restaurant chefs.

CSA remains a niche market, however, even among those who frequent other LFS market channels. At the same time, CSA can support and facilitate LFS activity in a few ways. First, CSA farmers are often more likely to participate in other LFS market channels and have a broader commitment to civic engagement (Barbosa et al., 2022; Mert-Cakal & Miele, 2022; Schoolman et al., 2021). The latter commitment can lead to the development of knowledge- and resource-sharing networks centred on LFS development (Rommel et al., 2022; Zoll et al., 2021). CSA farmers may also interface with other social organizations to market the CSA model and support other LFS markets (Barbosa et al., 2022). In short, the existence of CSA farmers in a LFS might facilitate the creation or strengthening of complementary LFS activities and opportunities.

Second, CSA can be an on-ramp to other LFS market channels – especially for those who are introduced to the model through a prescription produce or workplace incentive programme (Rossi et al., 2017). While long-time subscribers to CSA often hold values that lead them to seek out other LFS opportunities (Hvitsand, 2016; Savarese et al., 2020), COVID-19 and cost-offset programmes have expanded the potential market for CSA (Bonfert, 2022; Mert-Cakal & Miele, 2021; Rossi & Woods, 2021). These new, less traditional shareholders (who are not yet frequenting other LFS markets) may join CSA for different reasons such as the potential health benefits of consuming fresh produce (Pierce, 2023).

Finally, CSA has an engaging, experiential quality similar to food festivals, agritourism, farmers markets, breweries, and gardening (Brune et al., 2021; Curtis et al., 2018; Gumirakiza & Milliner, 2022; Organ et al., 2015; Savarese et al., 2020). As shareholders continually receive fresh produce and other farm products each week over a growing season, CSA creates an iterative learning environment that may transform and reinforce consumer habits related to local food systems (Rossi et al., 2017). By requiring a certain level of participation and emotional involvement, CSA can facilitate deeper engagement with LFSs and their actors.

In short, CSA has the potential to support and diversify other local market channels, but only if consumers are aware of and participate in this direct market model. Consequently, this manuscript examines how CSA is connected to other aspects of local food systems (LFSs) to 1) identify opportunities for shareholder expansion and 2) understand how different LFS attributes complement and potentially strengthen each other. By understanding how consumers connect CSA to other local market channels, we can identify points of potential market synergy that expand opportunities for residents, farmers, and food businesses to engage in diverse interactions.

In this paper, we broadly situate CSA within local food systems using primary data collected in 2019 using a unique instrument, the Local Food Vitality (LFV) survey. We received responses from more than 4000 residents in 13 communities in the United States (US) Southeast – a region with emerging CSA operations.

Our goal is to identify how consumer perceptions of different parts of their LFS predict:

1. whether and to what extent consumers are aware of the CSA model (i.e. awareness) and

2. the quality of local CSA options as perceived by LFS consumers (i.e. performance).

This approach contextualizes which LFS activities resonate with and complement multiple aspects of local food systems, and which have no bearing on CSA perceptions. Understanding these associations is critical as they can inform LFS activities that synergize and reach more new potential consumers of local agricultural products. Instead of treating CSA as an isolated market channel, we move toward a systemic evaluation that considers CSA alongside other complementary and overlapping LFS activities. As CSA’s repetitive structure forces continuous subscriber engagement in relation to local food and agriculture, CSA has the potential to support longer-term term consumer relations with multiple parts of their LFS.

The structure of this paper is as follows. First, we contextualize our analysis within literature on 1) CSA and its potential impacts of individuals and food systems, 2) the growth in consumer awareness and participation in CSA, 3) the importance of understanding consumer perceptions of CSA to improve promotion and retention of shareholders, and 4) the resonance between CSA and other aspects of LFSs.

We then describe our project background, survey instrument, and methodology. Following this, we discuss how different LFS attributes/components generally predict different perceptions regarding CSA awareness and performance in place-based respondents. We conclude this paper with a discussion of implications for CSA marketing and promotion.

Literature review

Local food systems (LFS) have the potential to shape local economic development activities (Deller et al., 2017). LFSs encompass complex social relations, economic conditions, community values, and historical events (Blay-Palmer et al., 2016; Feagan, 2007; Potteiger, 2013; Selfa & Qazi, 2005). LFSs often valorize place and local contexts through distinct experiential moments (Brune et al., 2021; Lee et al., 2015). Consequently, there is no singular formula for connecting local food to economic development. We argue, however, that CSA provides unique opportunities for stakeholders to support diverse aspects of their LFS (see also Mert-Cakal & Miele, 2021; Zoll et al., 2021). As such, this research seeks to understand how CSA is related to other aspects of LFS’s in the perception of LFS participants.

At the centre of this inquiry is the idea that CSA’s repetitive, iterative structure encourages and facilitates more intensive and potentially transformational relationships between subscribers and local food (Rossi et al., 2017). This subscription model encourages behaviour changes related to food acquisition and consumption (Allen et al., 2017; Cohen et al., 2012; Russell & Zepeda, 2008; Wilkins et al., 2015) and has the potential to impact health outcomes (Basu et al., 2020; Berkowitz et al., 2019; Biddle et al., 2021). There is also evidence that first-time CSA shareholders often increase their purchases of local and organic foods outside of the CSA arrangement (Rossi et al., 2017). More generally, CSA shareholders are more likely to support other LFS market channels as constant interactions with farmers and other shareholders create shared experiences and social networks with the potential to remake LFSs (Savarese et al., 2020; Zoll et al., 2021).

Incentive programmes – such as health-clinic sponsored produce prescription plans and employer-sponsored cost offsets – encourage individuals from many different socioeconomic backgrounds to try CSA for the first time (Pierce, 2023; Rossi & Woods, 2021). When appropriately funded and structured, CSA can also address food insecurity by providing produce to communities which have limited access to fresh foods (White et al., 2018). Finally, in times of disruption, CSA farmers (and local producers more generally) can fill the gaps for consumers when national/global supply chains fail (Durant et al., 2023; Thilmany et al., 2021a). In short, CSA – with its transformative potential and multitude of connections to community-based LFS stakeholders – plays an increasingly important role in bringing consumers to other local market channels.

Despite general consumer unfamiliarity with the model, CSA has become more well-known in recent years. National marketing campaigns, employer incentive programmes, share customization, and connections with other market channels have increased shareholder numbers and consumer knowledge about CSA.² Consumer experience of COVID-19 also had a positive impact on awareness and participation in CSA (Bonfert, 2022; Mert-Cakal & Miele, 2021). COVID-related supply chain shortages contributed to consumers searching for direct food acquisition arrangements with local and regional farmers (Thilmany et al., 2021a; Thilmany et al., 2021b). CSA provided a safe market channel – especially as farms adopted delivery options – for individuals to access fresh produce and farm products (CSA-IN, 2021). Technical assistance (TA) and other farm support organizations noted record growth in CSA shares in 2020 and 2021 in many locations, though farmers are seeing a reversion to pre-COVID means in 2022 and 2023 in terms shares sold.³ Farms with CSA and engagement with other direct markets were more resilient and better positioned to deal with the impacts of COVID (Durant et al., 2023)

This growth in consumer awareness of CSA has several implications for farmers and advocates. First, shareholders experiencing CSA for the first time – such as those incentivized by cost offsets – often have different motivations and requirements for joining compared to long-time subscribers to the model. Newer shareholders might join CSA for convenient access to fresh food and its perceived nutritional benefits (Rossi et al., 2017; White et al., 2018). They also value CSA arrangements that fit with their schedule and focus on improving access to fresh food (White et al., 2018). Longer-term shareholders are often more concerned with accessing organic food, knowing how food was produced, supporting local agriculture, and participating in sustainable food systems (Galt et al., 2019; Pole & Kumar, 2015; Vassalos et al., 2017). Understanding how different shareholder segments perceive and value the CSA model is critical to attracting shareholders, creating strong relationships with them, and convincing them to renew their subscription beyond their first year of participation (Galt et al., 2019; Pole & Kumar, 2015; Rossi & Woods, 2020; Vassalos et al., 2017; Yu et al., 2019).

A second implication is that CSA presents different challenges to different types of shareholders. CSA is an extremely unique food acquisition model that requires shareholder engagement throughout the harvest season. For all shareholders, CSA requires flexibility, experimentation, and willingness to learn in respect to food preparation (Russell & Zepeda, 2008; Zoll et al., 2021). It also requires shareholders to change their shopping habits since CSA boxes do not often provide all the items needed to create full meals. Shareholders must adjust shopping patterns to complement the items received in their share and to make arrangements to pick up their share on a weekly/biweekly basis (Allen et al., 2017; Brown & Miller, 2008). Those new to the model might not be as familiar with certain produce varieties and less comfortable with vegetable-centric meal planning using seasonal products. These challenges might lead to a CSA experience that dissuades them from frequenting local market channels, including and beyond CSA. At the same time, the CSA experience creates space to transform shareholder relationships with and attitudes toward food (Zoll et al., 2021). If shareholders are able to successfully navigate the unique, intensive experiment of CSA, they may begin to value less-tangible aspects of local foods such as direct relationships with farmers, the protection of small family farms, cultivating agricultural biodiversity, and supporting sustainable production (Hvitsand, 2016; Savarese et al., 2020). By instilling social and environmental values in shareholders, the CSA experience can lead to consumer experimentation with other local market channels. If first-time and less experienced shareholders are provided with resources that support learning – such as those related to food preparation, preservation, meal planning, and seasonality – they might have a positive perception of their CSA and other local food arrangements (Rossi & Woods, 2020).

Third, CSA – as part of a broader set of local food system market channels and attributes – creates opportunities for shareholders and farmers to interact with other aspects of their LFS. Farms that offer CSA are often involved with or market through other local market channels and LFS support organizations such as restaurants, farmers markets, food co-ops, and food festivals (Barbosa et al., 2022; Galt et al., 2016; Jablonski et al., 2019; Schoolman et al., 2021). For instance, in our region, farmers use farmers markets, microbreweries, and specialty retail locations as drop sites for their CSA. They also work with the state Departments of Agriculture and local non-profit support organizations to develop messaging for local food advertising campaigns or labelling initiatives. These arrangements provide complementary opportunities for both farmers and vendors at the other market channels to engage with consumers. Understanding where CSA fits within the broader food system in the perceptions of residents may give farmers and local food advocates (e.g. policy makers, local food development personnel, Extension, food councils, etc.) ideas for how to link attributes, provide technical assistance, reach potential shareholders, and promote LFSs more generally.

Finally, changes in awareness of and engagement with CSA is proceeding differently based on regional histories with CSA. Farmers in the Northeast and Midwest US were the earliest adopters of the CSA model and have a long history of intense shareholder engagement. However, many farmers were noticing decreased enrolment in subscriptions over the latter half of the 2010s in these regions.⁴ In Southeast US, where survey responses were gathered, CSA is a relatively new market phenomenon with fewer farmers using CSA as a marketing option until recently. This region saw a steady increase in CSA shares over the past 15 years.⁵ The recent emergence of CSA in the South is in part explained by the path dependence of previous agricultural and land use practices. For instance, in places like Kentucky, the Carolinas, and Tennessee, tobacco production was cornerstone of the agricultural economy. The 1998 Master Settlement Agreement between state attorneys general and tobacco companies resulted in tobacco-growing states receiving funds to in part to encourage agricultural diversification (Davis et al., 2015). CSA and other direct markets became more prominent in farmers’ marketing strategies since many tobacco farms were relatively smaller scale due to the quota system.

The recent emergence of CSA in the Southeast, and LFS growth more generally, puts us in a unique position to consider the coevolution of and relationships between local market channels. With these considerations in mind, our manuscript is based on responses from residents in 13 communities of diverse sizes and levels of interest in LFS in the US Southeast. Survey responses and their analysis provide insights into a region where CSA is growing in popularity as a direct marketing approach. In what follows, we use the survey-based to understand how particular food system attributes resonate with our existing understanding of CSA from other qualitative information, related research projects, and involvement in national networks of CSA technical assistance providers.

Methodology

Survey development and rationale

Community development centred on LFSs requires the characterization of local resources and system attributes and different strategies for LFS assessment (Cleveland et al., 2015; Feenstra et al., 2005; Freudenberg et al., 2018; Gasteyer et al., 2008; Levkoe & Blay-Palmer, 2018; Prosperi et al., 2016). Many of these strategies prioritize market channels and other infrastructural assets. While these considerations are undoubtedly important, especially to those interested in where resource gaps exist for farmers and food businesses, they can over-emphasize market factors and capital investments and de-emphasize residents and their perceptions (Soma et al., 2022). Without a consideration of different needs, aspirations, and values related to residents’ LFSs, food system assessments (and investments) may prioritize consumers who are already invested in the concept of local and/or can afford to buy local (Tregear, 2011). Resilient food systems are those which address the needs of the many (Béné, 2020). By understanding where LFS can be improved and made more inclusive, LFS stakeholders can expand opportunities and benefits for residents, farmers, and food businesses.

With these considerations in mind, we developed ‘The Local Food Vitality’ (LFV) survey that directly asks residents to evaluate diverse aspects of their local food systems including, but not exclusive to market channels. To do this, we first conducted focus groups in 2018 that asked residents to identify components of their communities that they felt were important to understanding local food systems. We used these responses alongside conversations with local food system experts and stakeholders to refine a list of components to include in our survey. After developing and piloting a survey in our local community in that same year, we workshopped the survey with research collaborators in the US Southeast and eventually identified 29 total LFS components for which respondents were asked to evaluate.

We have described our iterative survey development process and its differences from other assessments elsewhere (Rossi & Woods, 2023). In short, it uses primary responses from residents to understand food system performance rather than secondary data related to counts of discrete elements (e.g. # of farmers markets, # of CSAs, etc.). By evaluating a constellation of components that constitute their local food system, we can gain insights into how different resident segments value and associate different LFS aspects together.

Survey distribution

In the spring/summer of 2019, we distributed surveys to residents in 13 communities in North Carolina, South Carolina, Kentucky, Tennessee, Alabama, and Arkansas. These communities were identified by state cooperative Extension agents and food experts in the region as places with identifiable local food market channels and activities. Each locale has at least two CSA farms in a 50-mile radius, a state-sponsored local food branding programme, and other LFS components which we include in our analytic models (see Analysis section).

We used a combination of 1) paper surveys with pre-paid return envelopes sent to random residents using addresses purchased from a small business marketing company, 2) in-person group surveys in communities with limited internet access, and 3) on-line surveys where respondents were recruited via the Dynata survey company, community-based digital newsletters, and social media groups. Paper survey recipients also received a follow-up postcard with details on how to complete an online version if preferred. Both services used for recruitment were asked to provide respondents that proportionally represented specific zip codes and property values across each community. We also oversampled addresses for rental and lower income properties to improve response numbers among the lower income households. The response rate to paper surveys was between 5-10% for each community. Response rates from survey companies and other digital engagement efforts are not possible to calculate. In all, we received over 4000 usable responses to our survey.

Survey structure

Each respondent is asked to score 29 total LFS components on a 1–5 scale for overall performance. The general question text for each component is ‘How would you rate the performance of the following aspects of your community’s local food environment?’. We gave respondents a few guidelines for defining high performance (see Figure 1). These guidelines are very general and allow for individuals to apply their own perceptual criteria for high performance. Given that individuals were evaluating 29 different aspects of their LFS, we expected that each respondent would have an internally consistent set of criteria for evaluating diverse LFS components. In the context of a large sample of individual responses, the relationships between different component scores would provide insights into how CSA is generally connected to other LFS aspects.

Figure 1. Variables as Defined by LFVI Survey. * CSA is the dependent variable in each analysis conducted for this manuscript. All others are independent variables.

Respondents were also given a ‘Don’t’ Know’ option if they felt they did not have enough knowledge to rate that component. Residents who gave a non-zero⁶ performance score for a LFS component were considered to be aware of that component. Residents who answered ‘Don’t Know’ were considered to be unaware of that component. In short, awareness measures the visibility of specific LFS components in a locale. Performance is a perceptual measure related to how well a respondent feels different aspects of their LFS are functioning and/or meeting their needs and expectations. Performance measures only take into account the perceptions of residents that have knowledge about (i.e. are aware of) specific LFS activities. In this current analysis, we include only 16 LFS components (out of the total 29) because we expected that residents’ perceptions of these components might be associated with their perceptions of CSA. These specific components and literature-based reasons for their inclusion are described in the next section.

Analysis

Our analysis proceeds with two general approaches. First, we identify which specific variables predict whether an individual is more likely to be aware of CSA via a probit regression. The second approach is to determine which specific variables predict whether an individual is more likely to give CSA a higher performance scores via an ordered logit regression. This analysis proceeds along similar lines to those outlined by (Rossi & Woods, 2023).

LFS performance variables used in analysis

Both approaches use the same 16 performance scores as independent variables to understand CSA.

Each variable is initially scored on a 1–5 Likert scale with 1 = ‘Very Poor’, 3 = ’Average’, and 5 = ’Excellent’. The variables, their abbreviations, the question structure, and a definition of performance for each category of questions are included in Figure 1.

As shown in Figure 1, performance scores variables are organized by their role in LFSs. For instance, the following variables are categorized as market channels:

• Restaurants

• Retail (i.e. conventional grocery stores)

• Farmers markets

• Microbreweries, and

• Specialty retail markets (e.g. co-ops and health food stores).

We expected that individuals who attended some of these market channels might be more aware of CSA and score its performance in a similar manner. Others have noted that consumers with similar values seek out alternative food acquisition experiences (Brune et al., 2021; Curtis et al., 2018; Lee et al., 2015; Savarese et al., 2020). As noted above, farmers often use some of these complementary market channels to serve as CSA drop sites and/or to market their farm brand (Barbosa et al., 2022; Jablonski et al., 2019; Schoolman et al., 2021). Conventional retail is included to control for general food acquisition since most residents will frequent this type of market channel for at least some of their food needs.

A second category includes the following local food product characteristics:

• Quality,

• Diversity,

• Healthiness, and

• Price competitiveness.

Consumers of LFS markets and CSAs have been documented to consider locally produced foods to embody the first three attributes above, while sometimes struggling with the cost (Gumirakiza & Milliner, 2022; Mert-Cakal & Miele, 2021; Pierce 2023). We theorized that individuals who valued local product characteristics might also evaluate CSA in a similar manner.

Next we also have the following variables related to branding and identifying the source of local products:

• State branding programmes

• Identifiable farm brands

• Local food labels

Consumers of local food market channels generally care about the transparency and legitimacy of the production processes behind their foods. Labels and other certifications confer messages about provenance (Barbosa et al., 2022; Hvitsand, 2016; Woods et al., 2018). If labels and branding information about local products is strong in a community, then residents might have more positive perceptions of CSA. Similarly, CSA provides opportunities for shareholders to learn about existing farm brands, labels, and branding programmes.

Our final category includes the following variables that encompass various experiences, programmes, activities, and organizational support for LFSs:

• Home and community gardens

• Local buying campaigns

• Food festivals

• On-farm events (e.g. u-pick, agritourism, and farm stores)

We theorized that if communities have social events and other experiential activities that promote local foods, CSA might be a more common occurrence. Agritourism, food festivals, and gardening, for example, provide opportunities to deepen relationships between the participant and their foods (Brune et al., 2021; Curtis et al., 2018; Gumirakiza & Milliner, 2022; Organ et al., 2015; Savarese et al., 2020). CSA shares an experiential emphasis alongside the components listed above (as well as farmers markets and breweries). These components are included as independent variables in our analytic models, as described in the following sections. We verified that all communities surveyed had each of these aspects.

Who is more likely to be aware of CSA?

Our first analytic approach is to use probit regression analysis to evaluate which other food system components and demographic characteristics increase the likelihood of an individual providing a score for CSA. As described above, an individual is aware of CSA if they have enough knowledge about this market channel to provide a performance score other than ‘Don’t Know’. If they choose’ Don’t Know’, they are considered to not be aware of CSA. In this model, CSA is our dependent variable with ‘Don’t Know’ responses coded 0 and all other performance scores coded 1.

In other words, does resident awareness of specific market channels, LFS promotion programmes, and product characteristics affect their awareness of CSA? A similar approach to evaluate awareness was used in a previous publication (Rossi & Woods, 2023). Our independent variables include all components described in the previous section and listed in Figure 1. They are recoded in the same manner as our CSA variable (i.e. ‘Don’t Know’ = 0, all other responses = 1). We also include selected demographic variables to control for individual resident differences:

• age (continuous)

• income (continuous)

• interest in local food (binary: 0 = low to moderate/1 = high)

• community size (categorical: 0 = large/1 = small/2 = medium)⁷

• years resident (continuous)

• CSA farms within 100 miles of community (continuous)⁸

Probit models generate insights into how specific indicators affect the likelihood of an individual choosing one of two possible options. In this case, residents either score (1) or don’t score (0) the performance of CSA. For independent LFS variables, significant results with a positive coefficient indicate that a respondent scoring that component is more likely to also provide a CSA performance score (i.e. be aware of CSA). For demographic variables, significant positive/negative coefficients indicate an increased/decreased likelihood that the individual is aware of CSA if they fall within that certain demographic category.

We generated marginal effects for each significant independent variable to give a sense of the magnitude of influence each has on the likelihood of rating CSA with a performance score. We conducted analysis of CSA awareness in the full sample of responses, as well as for each individual community. Probit results for all respondents are presented in Table 2. Probit results for individual communities are presented in Table 5. The latter results illustrate how LFS-related variables impact awareness differently depending on one’s community.

Who is more likely to perceive CSA as high performing?

The second approach is to evaluate residents’ perceptions of CSA performance. In this step, we only evaluate the responses of individuals who provided performance scores for CSA. We use ordered logistic regression models (ologit) to understand how independent variables impact the likelihood of a higher or lower rating for CSA. For this model, we use the same independent variables as in the probit model for awareness. In comparison to our probit model, which involves binary choice variables, the ologit treats the dependent variable (i.e. CSA performance rating) and independent variables (i.e. other LFS component performance ratings) as ordered phenomena. In other words, we keep the original values from the 1–5 Likert scale rather than recoding these values to binary. When variables are statistically significant and positive/negative, they indicate that residents’ perceptions of CSA are likely to increase/decrease in concert with that variable. As with the awareness analysis, we evaluate the full sample of respondents and respondents by community. Ordered logit results for all respondents are presented in Table 3. Ologit results for individual communities are presented in Table 7. The latter results illustrate how LFS-related variables impact performance differently depending on one’s community.

Results

Descriptive statistics

Table 1 shows the descriptive statistics of the responses for all variables included in each analytic model. For the awareness probit analysis, we present the percentage of individuals who did not score that LFS component (unaware) versus those who provided a score (aware). The latter column is a descriptive representation of awareness – with the higher percentages corresponding to higher component knowledge.

Table 1. Descriptive Statistics for Variables Included in Awareness and Performance Models.

	Awareness Probit (N = 3637)		Performance Ordered Logit (N = 1145)
LFS Component Variables
	unaware (%)	aware (% )	Mean	Std Dev
csa (dep var)	43.4	56.6	3.2	1.1
fmkt	10.9	89.1	3.7	1.1
specialty	21.2	78.8	3.4	1.1
retail	2.2	97.8	3.9	0.9
rest	3.6	96.4	3.9	0.9
brew	30.9	69.1	3.5	0.9
diverse	11.4	88.6	3.5	1.0
state	21.1	78.9	3.5	1.1
brands	21.5	78.5	3.3	1.1
price	10.2	89.8	3.4	1.0
label	17.2	82.8	3.4	0.9
quality	9.9	90.1	3.8	1.1
healthy	10.6	89.4	3.6	1.1
gardens	22.4	77.6	3.2	1.1
fest	13.3	86.7	3.5	1.1
onfarm	29	71.0	3.1	1.1
campaign	33.8	66.2	3.1	1.1
Demographic Variables
	Awareness		Performance
	Mean	Std Dev	Mean	Std Dev
age	47.1	16.9	44.2	16.6
yrs_resident	16.5	9.7	16.8	9.5
income	75,100	55,700	79,700	57,918
farms	13.1	6.5	13.4	6.6
	%		%
low.int (base)	37.4		34.1
high.int	62.6		65.9
Small.Comm	17.2		14.1
Med.Comm	30.0		29.9
Large.comm (base)	52.8		56.0

CSA is the component where residents had the most ‘Don’t Know’ responses. In other words, it is a comparatively unknown market channel in the local food space. Other lesser-known components – as measured by the percentage non-zero responses – include microbreweries, specialty retail stores, and community/home gardens. Retail and restaurants were the most well-known components and scored by nearly all respondents. In terms of demographic variables, residents who knew about CSA were on average 44 years of age with ∼$80,000 in household income and had approximately 13 CSA farms within a 100-mile radius of their home. Over half of the respondents were from large metropolitan cities. Finally, while 39% of respondents were extremely interested in local foods (high.int), nearly 29% had little to no interest (low.int).

For our performance analysis, we include the mean score for each component for the subset of individuals who gave CSA a non-zero score. The score of 3 is ‘average’ performance (i.e. does not over or underperform resident expectations and needs). Here retail, restaurants, and farmers markets score highest in performance, while on-farm events, local food campaigns, and home/community gardens score lowest. CSA is slightly above average in performance, which puts it near the bottom of all performance scores included in the model.

The demographic variables were similar to those in the probit model in terms of years of residence and CSA farms in a 100-mile radius. These respondents skewed slightly older with slightly less household income. There were comparatively more respondents from smaller communities and a much higher percentage of individuals with low interest in local foods overall. In short, these results illustrate that CSA is a niche market channel with a relatively average performance among those who are aware of the model. We now explore how knowledge of different LFS attributes impacts awareness of CSA.

Awareness of CSA as predicted by the probit model

In this section, we evaluate predictors of CSA awareness using probit models. If a component is significant in our model, then respondents are more likely to give CSA a non-zero performance score when they also give that component a performance score. We provide marginal effects (MEs) only for significant variables to make the table easier to interpret.

In Table 2, a few results are immediately obvious. First, the retail and restaurant variables are not significant. In other words, knowledge about local food in these channels is not predictive of CSA awareness. These variables had the highest responses rates and are the most mainstream market channels. Similarly, respondents who gave performance scores for local food product attributes (price, quality, healthiness, and diversity of local food) were neither more or less likely to answer the CSA question. Local food market channels that have a more niche constituency, however, had more explanatory power in our model. If an individual provided rating for farmers markets, specialty retail stores, microbreweries, or on-farm events, they were 12-20% more likely to rate CSA performance.

Table 2. CSA Awareness via Probit Model Results.

	Awareness
	coef		ME
fmkt	0.495	***	0.138
specialty	0.728	***	0.203
retail	−0.283
rest	−0.082
brew	0.450	***	0.125
diverse	−0.003
state	0.317	***	0.089
brands	0.369	***	0.102
price	−0.113
label	0.208	*	0.058
quality	0.059
healthy	−0.137
gardens	0.298	***	0.083
fest	−0.084
onfarm	0.422	***	0.118
campaign	0.422	***	0.117
age	−0.003	*	−0.001
high int	0.154	*	0.043
yrs_resident	0.002
income	−0.001
Small.Comm	0.083
Medium.Comm	0.025
farms	−0.001

Note: *p < .05, **p < 0.01, ***p < 0.001

Branding and promotion initiatives are also predictive of increased likelihood of rating the CSA component. Respondents with an opinion on state branding programmes (e.g. ‘Kentucky Proud’, ‘Certified SC Grown’, etc.) and local food campaigns were 9% and 12% more likely to rate CSA respectively. All sampled communities had these state brand designations. Respondents with existing interest and engagement in local foods are more aware of CSA. This is supported by the significance of variables such as high interest in local food systems (high.int) and gardens. Age is weakly significant, with older consumers less likely to know about CSA. Finally, income, community size, and the number of farms within 100 miles had no predictive power regarding whether respondents scored CSA.

CSA performance as predicted by the ordered logit model

We now consider how perceptions of CSA performance are shaped by other variables using an ordered logit model. We again present marginal effects only for variables that are significant in the ologit. CSA performance is explained by similar, but fewer variables when compared to the CSA awareness model. As shown in Table 3, residents that give higher scores to farmers markets, specialty retail⁹, on-farm events, and microbreweries also score CSA higher. Perceptions of restaurants and retail grocery¹⁰ do not impact CSA performance.

Table 3. CSA Performance via Ordered Logit Results.

	Performance
	Coef		ME
fmkt	0.415	***	0.060
specialty	0.508	***	0.074
retail	−0.080
rest	−0.006
brew	0.202	**	0.030
diverse	0.143
state	0.158	*	0.023
brands	0.276	**	0.040
price	−0.009
label	−0.141
quality	−0.005
healthy	0.016
gardens	0.193	*	0.028
fest	0.106
onfarm	0.405	***	0.058
campaign	0.229	**	0.033
age	0.005
high int	0.153
yrs_resident	−0.012
income	0.002	*	0.001
Small.Comm	0.351
Medium.Comm	0.264
farms	0.003

Notes: *p < .05, **p < 0.01, ***p < 0.001

All pairwise correlations between variables are below 0.6 and all VIF scores are below 3 when run as a linear regression. Multicollinearity appears not to be a concern in this model.

We present the combined MEs for the performance outcomes 4 (good) and 5 (excellent) since outcome 4 is where the MEs shift from negative to positive for all LFS component variables. The combined MEs for outcomes 1–3 are equal in magnitude to those of outcomes 4–5 but opposite in sign. We present the combined MEs for outcomes 4–5 to estimate how increasingly positive responses to each independent variable impacts perception of CSA.

Beyond market channel linkages, marketing/branding plays a role in performance perceptions, but not to the same degree as in the awareness model. Individual farm brands are the most predictive marketing/branding indicator of higher CSA scores. Local food campaigns and state branding programmes are also associated with more positive perceptions of CSA while general local food labelling efforts do not have an impact. Resident perceptions of home/community gardens also improve CSA scores.

Finally, income seems to matter in residents’ perceptions of CSA performance. Higher incomes increase the likelihood that an individual will score CSA higher.

Awareness and performance by location

One benefit of our LFV process is that its data can be disaggregated to show trends by residents in different demographic or geographic segments. In the above results, we aggregated responses to broadly understand predictors of CSA awareness in perception. In this section, we illustrate how CSA is linked to different LFS components and demographic variables depending on location. With a more place-based understanding of how residents link CSA to other LFS components, farmers and other CSA stakeholders can identify potential areas for marketing or programme improvement.

Awareness by location

Table 4 provides a comparison of awareness by locale. Each of these locales were included in our survey prior to the COVID outbreak – which disrupted response collection from other places. We also provide information on the number of CSAs located within 100 miles of each community according to the USDA CSA directory. As a baseline for comparisons, nearly 56% of all respondents provided a performance score for CSA in their community and are therefore considered to be aware of the model in their community.

Table 4. General CSA Awareness by Location.

	Awareness		CSA Farms <100 mi
	N	% Answered
All	4519	55.7	12.5
Segmented
Small Locales	731	52.7	11.9
Medium Locales	1265	57.0	9.6
Large Locales	2183	54.8	15.3
Individual
Upstate South Carolina	435	52.9	13
Columbia, SC	301	49.2	6
Catawba, SC	167	52.1	10
Louisville, KY	608	55.3	12
Boyd Co, KY	137	54.0	5
Clark Co, KY	83	49.4	19
Nashville, TN	602	55.3	17
Knoxville, TN	286	63.9	12
Edgecombe Co., NC	197	52.7	12
Raleigh-Durham, NC	670	54.8	20
Baton Rouge, LA	240	56.2	3
Montgomery, AL	183	60.1	2
Little Rock, AR	282	53.9	2

First, we consider locations aggregated by size. Medium-sized metropolitan had the highest overall responses rate (57%) while large metropolitan and small non-metropolitan counties had slightly lower response rates. For individual locales, medium-sized cities like Montgomery, AL and Knoxville, TN both had response rates above 60%. The response rate for Montgomery is somewhat surprising since the USDA CSA directory lists only two CSA farms within 100 miles of the city – neither of which has more than 120 shares. In contrast, there are approximately twelve CSA farms within the same radius of Knoxville.

On the lower end of the awareness spectrum, Clark County in Kentucky and Columbia, South Carolina had a lower than a 50% response rate to the CSA question. Clark is close to many of the prominent CSA farms in Kentucky while Columbia has fewer CSA options. The number of proximal CSAs does not appear to have a significant impact on awareness of CSA. At the most basic level, CSA awareness appears to fluctuate depending on locale and community size, but other factors impact individuals’ knowledge of CSA.

In Table 5, we show which LFS components increase the likelihood that residents of selected locations are aware of CSA. We conducted probit regression models using the same variables as in our aggregated model (Table 2) except for community size and farm number since these variables would be the same for all residents in that location. We only include level of significance in Table 5 since our goal is to compare how different places associate CSA with unique patterns of other components. Smaller locations were aggregated due to individually small sample sizes.

Table 5. CSA Awareness by Location via Probit Regression Results.

	Columbia, SC	Louisville, KY	Nashville, TN	Knoxville, TN	Raleigh-Durham, NC	Little Rock, AR	Mont, AL	Small Comms
fmkt		*	*	*				*
specialty	**	***	***	**	**	*		***
retail
rest					*
brew			**		**		***	***
diverse
state				**				*
brands		*			**	**		**
price						*
label						**
quality
healthy
gardens		*		**				*
fest
onfarm		**	**		**	*		**
campaign		***	*		***		*
age	*							*
high.int				*
yrs_resident
income
N	260	530	539	240	577	232	162	625

Note: *p < .05, **p < 0.01, ***p < 0.001

Awareness of specialty retail (a category that includes consumer co-ops and health food stores) is tied to CSA awareness in all locations featured except for Montgomery, AL. Additionally, marketing components (i.e. state, brands, label, campaign) played a role in increasing awareness in most locations. On-farm events and farmers markets also likely play a role in CSA promotion by being physical spaces where a consumer might encounter the CSA model. Food festivals – while fitting in the same experiential space as farmers markets or on-farm events – did not predict an increase in a respondent’s likelihood of giving a score for CSA.

Beyond these general trends, individual communities had their own pattern of components that predicted awareness of CSA. For instance, in Nashville, CSA awareness was tied to awareness of farmers markets, breweries, on-farm events, and local food campaigns. On the other side of the state, Knoxville residents were more aware of CSA if they were also aware of state branding efforts, gardens, and farmers markets. Additionally, Knoxville was the only locale where interest in local food was predictive of awareness. Finally, Little Rock is the only place to have CSA awareness related to local food labels and the price competitiveness of local foods.

Performance by location

Now that we have considered location differences in awareness, we can discuss the performance differences. Overall, Table 6 shows that CSAs are performing just above average when considering the full respondent population. The rating is highest for those in medium-sized and large locations, while being slightly lower for those in more rural locations.

Table 6. General CSA Performance by Location.

	Performance		CSA Farms <100 mi
	N	Mean
All	2518	3.2	12.5
Segmented
Small Locales	385	3.1	11.9
Medium Locales	721	3.4	9.6
Large Locales	1196	3.3	15.3
Individual
Upstate SC	230	3.4	13
Columbia, SC	148	3.4	6
Catawba, SC	87	3.2	10
Louisville, KY	336	3.2	12
Boyd Co, KY	74	2.7	5
Clark Co, KY	41	3.1	19
Nashville, TN	333	3.4	17
Knoxville, TN	183	3.6	12
Edgecombe Co, NC	104	3.1	12
Raleigh-Durham, NC	369	3.5	20
Baton Rouge, LA	135	3.2	3
Montgomery, AL	110	3.2	2
Little Rock, AR	152	3.0	2

When considering individual places, Knoxville, Tennessee again scores highest. Residents in Knoxville had the highest awareness level (see Table 4) as well. Further research is required to determine the specific reasons for this performance, but it is worth noting that this location is within 100 miles of CSA farms from three surrounding states, has a robust Extension presence, and is dominated by an important Land Grant University. Other places with similarly high ratings are Raleigh-Durham-Chapel Hill in NC, Nashville TN, Columbia SC, and Upstate SC (Spartanburg, Greenville, and Anderson County). Columbia is an interesting example of a place with comparatively low response rates (awareness) but high performance. In other words, the residents who do know about CSA feel it is meeting and exceeding their expectations. The only place scoring below average is Boyd County, Kentucky – an area that had few proximal CSA farms all of which were in bordering states at the time of surveying.

CSA performance has fewer components that commonly predict more positive CSA performance as shown in Table 7. Specialty retail is again the most common significant component across communities. Farmers markets are important in both Tennessee communities as well as Raleigh-Durham. Breweries feature as important components in Louisville and Raleigh-Durham. Local food diversity is important in Columbia, SC while local food campaigns are more prominent in smaller communities. Higher income predicts higher CSA ratings in Louisville and Columbia.

Table 7. CSA Performance by Location via Ordered Logistic Regression Results.

	Columbia, SC	Louisville, KY	Nashville, TN	Knoxville, TN	Raleigh-Durham, NC	Little Rock, AR	Mont, AL	Small Comms
fmkt			***	**	**
specialty	*	***	***	*	***		*
retail
rest						*
brew		**			*
diverse	**							*
state	*
brands					***	*
price
label						*
quality				*
healthy								*
gardens				*		*
fest						*
onfarm			*		***
campaign							*	*
age
high.int
yrs_resident
income	*	*					*
n	60	192	179	85	194	71	51	162

Note: *p < .05, **p < 0.01, ***p < 0.001

Discussion

The results above show that CSA connects with other LFS aspects in unique ways. While geographic differences exist for how these aspects interact, there are some broad trends that have implications for how CSA can be promoted and supported by different stakeholders. First, individuals are more aware of CSA if they also have an opinion on other niche or local market channels – especially farmers markets, specialty grocery store, microbreweries, and on-farm events. Consumer participants in LFS markets generally consider social, civic, or environmental concerns when purchasing food, and value experiential and relational aspects of the market encounter (Brune et al., 2021; Curtis et al., 2018; Lee et al., 2015; Savarese et al., 2020). CSA farmers often use locals market channels as drop-off points and/or spaces to market their CSA subscriptions (Barbosa et al., 2022; Jablonski et al., 2019; Mert-Cakal & Miele, 2022). In the case of farmers markets, farmers sometimes offer shareholders the opportunity to supplement the contents of their box with items at the market via CSA-farmers market hybrid models. These drop-off locations can also make CSA visible to non-subscribers. For instance, visitors to a brewery that serves as a distribution site can experience the CSA concept in a tangible way.

Many of these other market channels are intricately linked to promoting local enterprises more generally. For instance, breweries and specialty retail locations have become popular venues to host farmer meet-and-greet events, where potential subscribers can learn about local CSAs (Constantine, 2018; West, 2020). In other projects, we have observed breweries supporting similar ‘Community-Supported’ subscriptions for non-food items like art and regularly promoting the value of supporting local economies. Farmers markets are important places where consumers can develop relationships with a farm and learn about CSA subscriptions (Savarese et al., 2020). Specialty grocery stores – especially local food co-ops – generally promote locally sourced items to distinguish themselves from more conventional retail (Hvitsand, 2016). They may appeal to similar consumer values that are typical of CSA shareholders. When observing individual locations, awareness of specialty grocery and on-farm events increased awareness of CSA in most communities. By marketing local food and novel food purchasing arrangements, these other local market channels can help improve consumer knowledge about CSA.

One aspect that did not impact awareness of CSA is food festivals which feature local cuisine and occupy a similar experiential space as on-farm events and farmers markets (Brune et al., 2021; Gumirakiza & Milliner, 2022; Rommel et al., 2022). As other research has been associated with increasing consumer willingness to explore other LFS markets (Organ et al., 2015), this lack of impact on CSA is surprising. Perhaps food festivals in the surveyed communities are do not emphasize specific farm operations or agriculture in general. This may be an area where local food advocates might make more of an effort to promote specific farms and market channel their LFS.

Marketing efforts at state and local generally seem to improve awareness of CSA. Variables related to individual farm brands, local food labels, and state branding programmes were significant in our models of CSA awareness. Given the opportunities to promote individual brands through LFS market channels and support organizations (Barbosa et al., 2022) and consumer interest in provenance and production transparency (Hvitsand, 2016; Woods et al., 2018), this association between brands and CSA awareness is not surprising. However, effective strategies for marketing CSA and other LFS opportunities differ by place, according to our models. For instance, state branding efforts seem to be important in Knoxville (a smaller city) and in more rural locales. State Departments of Agriculture might focus on promoting local food more generally in smaller communities. In larger communities like Louisville and Raleigh-Durham, consumer familiarity with individual farm brands seems important. Farmers might need other strategies to promote their CSA options such as farmers market presence, brewery collaborations, farmer CSA coalitions, or hosting on-farm events. In another study, we identified word of mouth as an extremely critical path for new shareholders to learn about CSA (Rossi, Rocker, & Thilmany, 2021).

It is difficult to make conclusive claims on why certain market channels and marketing strategies appear to be the primary drivers of CSA awareness in specific places. However, CSA advocates might take these results and generally conclude that certain niche markets are useful for CSA promotion and focus efforts on recruiting shareholders in these venues. Marketing is generally important – though this can take many different forms depending on local context. Case studies and in-depth conversations with place-based stakeholders – which were beyond the scope of our project – would provide more texture to our observations.

In comparison to awareness, fewer variables predicted residents’ perceptions of performance, though niche markets are similarly impactful. Specifically, farmers markets, specialty retail, and breweries were associated with increased performance perceptions while conventional retail and restaurant channels are not. Consequently, we interpret the perceptual relationships between these niche markets as most important to understand CSA perceptions. If someone feels positively about the local performance of farmers markets or on-farm events, they are more likely to have a higher opinion of CSA performance. Since farmers who utilize direct markets often diversify their marketing and logistics strategies (Barbosa et al., 2022; Jablonski et al., 2019; Mert-Cakal & Miele, 2022), the performance of these niche markets is likely linked. Nashville’s farmers market, for instance, hosted an annual CSA fair prior to the pandemic (Chamberlain, 2016). Knoxville has worked with specialty retailers – such as Whole Foods – to host a similar event (Constantine, 2018). Louisville has used breweries to hold farmer meet-and-greet events (Anonymous, 2019).

Similar to awareness results, positive perceptions of state marketing programmes and individual farm brands and marketing variables are associated with higher CSA performance scores. Regarding the farm brand variable, farmers offering CSA develop relationships with their shareholders to distinguish their farm brand from other CSAs through newsletters, farm visits, u-pick, potlucks, and the weekly produce pick-up. In other words, CSA gives farms an opportunity to cultivate their brand identity through the shareholder’s intensive, iterative experiences. In a separate study, we identified shareholder engagement with the CSA experience as critical to satisfaction with and willingness to renew their subscription in subsequent years (Rossi & Woods, 2020). Farmers that build their brand through shareholder engagement create more positive perceptions of their CSA, and vice versa. In short, the significance of the ‘individual farm brands’ variable supports studies conducted outside this particular dataset.

In addition to farm brands, positive perceptions of state branding programmes are associated with most positive perceptions of CSA. Taken with the results from the awareness model, farm support organizations – including State Departments of Agriculture, local food policy councils, and cooperative Extension – are critical to organizing stakeholder networks and expanding consumer knowledge of CSA (Barbosa et al., 2022; Rossi & Woods, 2020; Zoll et al., 2021). The CSA model can be daunting to those who are unfamiliar with a regular food share. By explaining how CSAs work and educating consumers on seasonal eating, advocates can reduce some barriers to subscribing. Marketing campaigns, consumer food education, and state efforts to promote the work of local farmers can develop a broader, place-based consumer understanding of food, agriculture, and local economies. Given that these variables are also associated with higher performance perceptions, it is plausible that improving information regarding CSA can help shareholders achieve a more satisfactory experience of CSA which is critical to shareholder retention (Rossi & Woods, 2020). Additionally, farm support organizations can improve mechanisms (such as SNAP processing assistance) to integrate CSA into a food assistance strategies. In our state, the Department of Agriculture has worked to publicize CSA operations by developing a CSA directory and holding an annual CSA fair.

Finally, gardens and on-farm events improve perceptions of CSA performance. If residents are engaged with and knowledgeable about agriculture through experiential activities, then they seem to hold a more positive perception of the work of CSA farmers. It is also likely that the CSA experience leads participants to a more nuanced understanding of food production and increased engagement with other aspects of their LFS which mirrors the observation of other researchers (Brune et al., 2021; Savarese et al., 2020; Zoll et al., 2021).

These results act as a starting point for community-level inquiries into what components of a LFS residents associate with a positive view of CSAs. At some level, each community’s different pattern of responses outlines a potential consumer profile for CSA shareholders. Knowing where typical shareholders shop, visit, or engage (e.g. farmers markets, co-ops, breweries, gardens) can ensure efforts for recruiting new shareholders are appropriately tailored. For instance, locations with large public universities or regionally important hospital systems (Knoxville, Raleigh-Durham, Baton Rouge, Louisville, Columbia, Nashville, etc.) might consider University-sponsored CSA incentive programmes for their employees and patients since this model has become more popular as institutions focus on ‘food as health’ type efforts. Land Grant Universities in particular might create synergies with student-run farms and/or farmer training programmes to facilitate the expansion of diversified agriculture and the localization of the food system. Additionally, universities have diverse resources available to develop CSA cost-offset programmes that can be replicated in their surrounding communities.

These efforts provide opportunities to address a main critique of CSA, namely that the model is inaccessible to lower income households. Given the high upfront payments for an uncertain service potentially, CSA has historically been a higher income phenomenon (Durrenberger, 2002; Ostrom, 2007). Our model confirms this critique as higher performance perceptions are linked to higher income. Farmers’ potential market for shareholders will only expand if CSA can be made more inclusive (Galt et al., 2016). As a response to this challenge, as well as the pandemic experience, many technical assistance providers are developing strategies to make CSA more accessible through cost-offsets, payment plans, pre-tax payroll deductions, SNAP processing, and sliding scale models.¹¹

Conclusion

Through this analysis, we contend that CSA awareness and performance is often linked to other aspects of LFSs, and that linkages are often place-specific. Nevertheless, we can provide general suggestions for potential CSA development strategies. Most generally, cross-promotion of CSA with other LFS market channels and branding efforts appears to be valuable in improving both performance and awareness. As interest in LFS has some predictive power regarding who is aware of CSA, crafting messages and marketing strategies to reach individuals with different consumer values might expand the CSA shareholder base. Also, finding ways to lower barriers to entry through incentives, cost-offsets, and other mechanisms can help reach potential shareholders who have lower incomes.

The prominence of specialty retail as a component that explains CSA performance suggests an area for further research. Are these venues occupying the same conceptual space or role in the food system as CSA? Or are they more complementary? For instance, would a person who enjoys shopping at specialty markets be more likely to join a CSA or supplement their share with items purchased at this market channel? Our definition of specialty retail is broad and includes places like delis, fish markets, and shops featuring artisanal items. These locations cater toward consumers who appreciate a certain standard or type of items. As such, CSA shareholders and specialty retail shoppers may hold specific values that push them toward these types of market channels. It is also worth being realistic that the model just may not work for some individuals because of their own food values or structural barriers (Galt et al., 2019; White et al., 2018).

Again, this points to the need for more research into the consumer perceptions of CSA. And while we have illustrated some general perceptual connections, as well as unique place-based perceptions of CSA, we recognize that further evaluation of specific consumer food values is necessary to see where and to whom CSA might be appealing. This challenge presents us and others with an opportunity. By developing analyses that more deeply evaluate why CSA appeals to certain consumer segments and not to others, CSA farmers and LFS stakeholders might design better strategies for expanding the potential shareholder base. We are currently developing a survey and analytic process to understand what values different types of CSA subscribers hold.

One further observation is that in our model there was a lack of relationship between number of proximal CSA farms and CSA awareness. Instead, the latter seems to be more of a function of how CSA is promoted, marketed, or otherwise enters public consciousness through connections to other markets. As Galt (2011) notes, USDA Census of Agriculture data are imperfect and may not represent an accurate picture of farmers utilizing this model. And despite the best efforts of individuals in the USDA to understand direct markets such CSA through endeavours such as the USDA’s CSA directory and the Local Food Marketing Practices survey¹², farmers’ strategies to reach markets can change annually. It is difficult to maintain an accurate picture of national and regional CSA dynamics. Given that coordination and collaboration between stakeholders can improve system resilience and farmer opportunities, it is critical for researchers and farm support organizations to develop better metrics for understanding needs in their LFS.

Finally, it must be noted that our analysis is limited by when we collected our responses. Since our data collection stopped at the onset of COVID-19 in early 2020, we are unable to use this current dataset to reflect the tremendous evolution the model has undergone because of pandemic-related changes in consumer motivations. Nevertheless, we have begun collecting responses from selected communities now that over three years have passed since our initial encounter with COVID. Once more responses are collected, we will have the ability to evaluate whether and to what extent COVID has changed how components predict CSA awareness and performance.

Ethics approval

Data were gathered using research methods approved by the University of Kentucky (UK) IRB under protocol # 46354.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Correction Statement

This article has been corrected with minor changes. These changes do not impact the academic content of the article.

Additional information

Funding

This work was supported by National Institute of Food and Agriculture: [Grant Number 2018-68006-27644].

Notes

1 According to data downloaded from the USDA’s CSA Directory (ams.usda.gov/local-food-directories/csas), only ∼19% of farms offering CSA have offered this option for more than 10 years. CSA has been offered for less than 5 years for ∼57% of the listed farms. And while longer operating farms average larger numbers of shares, approximately half of the total shares offered come from farmers offering CSA for less than 10 years.

2 Personal communication with a national network of CSA providers – The CSA Innovation Network

3 Personal communication with a national network of CSA providers – The CSA Innovation Network

4 Personal communication with a national network of CSA providers – The CSA Innovation Network

5 For instance, 71% of farms offering CSA in the Southeast have offered subscriptions for less than 5 years at the time of data collection compared to ∼50% in the Northeast, Mid-Atlantic, Midwest, and West Coast. Only 11% of CSA farms were operating their model for more than 10 years compared to ∼20% in other regions. These trends were gathered from an evaluation of the USDA’s CSA Directory – https://www.ams.usda.gov/local-food-directories/csas

6 i.e. any score other than ‘Don’t Know’

7 Community size is based on USDA RUCC codes. RUCC categorizes counties by whether or not they are part of a metropolitan (metro) area. If they are, they are then categorized by the size of the population. Metro areas with more than 1,000,000 individuals are classified with a 1. Metro areas with 250,000 to 1,000,000 are classified with a 2. Metro areas below this threshold receive a 3. Non-metro counties are described by proximity to metro areas and population size with values ranging from 4 to 9 based on different criteria. We matched RUCC codes to zip codes provided by respondents to match responses to effective population size of the respective LFS. As such, individuals from the same sampling area might receive a separate RUCC designation based on their proximity to a metro area. For our analysis, we classified large, medium, and small locales if they have RUCC values of 1, 2, and 3–9 respectively. See the following documentation for specific code values: https://www.ers.usda.gov/data-products/rural-urban-continuum-codes/documentation/

8 We gathered data for this variable from the USDA AMS’s CSA directory – https://www.ams.usda.gov/local-food-directories/csas.

9 This category includes consumer co-ops, health food stores, delis, fish markets, and artisan markets

10 These are more mainstream market channels where one might still find local foods

11 csainnovationnetwork.org has a number of resources and projects designed to improve CSA access at a national level.

12 https://www.nass.usda.gov/Surveys/Guide_to_NASS_Surveys/Local_Food/