Social Capital Heterogeneity: Examining Farmer and Rancher Views About Climate Change Through Their Values and Network Diversity

Abstract

Agriculture plays a crucial role in discussions about environmental challenges because of its ecological footprint and high vulnerability to environmental shocks. To better understand the social and behavioral dynamics among food producers and their perceptions of climate change-related risks, this paper draws on forty-one in-depth, semi-structured interviews with farmers and ranchers in Colorado (USA). Leveraging the concept of social capital, the paper extends the concept analytically in a direction missed by previous research highlighting network structures, such as by focusing on its bonding, bridging, and linking characteristics. Instead, focus centers on the inclusiveness and diversity of values, beliefs, worldviews, and cultural orientations within those networks, arguing that these elements can be just as influential, if not more so in certain instances, than structural qualities. The concept of social capital heterogeneity is introduced to describe a network’s level of diversity and inclusivity. The findings do not question the importance of studying network structures when trying to understand how food producers respond to threats like climate change; an approach that remains useful for explaining social learning, technology adoption, and behavioral change. However, this method misses elements captured through a subjective, interpretivist perspective. With social capital heterogeneity, we can use social capital to explore why farmers and ranchers hold specific values and risk perceptions, peering deeper “within” networks, while tools like quantitative social network analysis software help map their structures from the “outside.” Additionally, social capital heterogeneity provides valuable insights into questions about “effective” agro-environmental governance. The paper concludes by discussing practical implications of the findings and reviewing the limitations of the research design.

1. Introduction

Among the many grand challenges facing society, climate change and our collective ability to adapt to weather-related extremes, while reducing global greenhouse gas (GHG) emissions, are among the most urgent [1,2]. While affecting everyone, differences exist in how climate change impacts groups and sectors of the economy, just as responsibility for GHG emissions is not distributed evenly. For instance, farmers and ranchers have a complex relationship with the natural environment. On one hand, with livelihoods situated at the intersection of society and nature—the sector is called “agriculture”, after all—food producers are among the first to experience disruptions because of environmental stocks, such as livestock illness, crop failures, and other food system disruptions caused by extreme weather events like wildfires and flooding [3,4]. On the other hand, agriculture is a major source of GHG emissions, accounting for an estimated 11 to 22 percent of global emissions, depending on model parameters [5,6,7]. Therefore, farmers and ranchers are a key group when developing strategies to address climate change mitigation and adaptation.

This importance is reflected in the growing research over the last decade examining how food producers view phenomena such as climate change [8,9], climate science [10,11], and strategies for climate change-related adoption and mitigation [12,13]. Such research emerges out of the acknowledgment that climate change adaptation and mitigation strategies involve complex processes, combining subjective and objective factors, including natural resource availability, livelihood practicalities, knowledge and information access, the built environment, money and credit, social networks, culture, and cognitive filters [6,7,8]. Thus, while this paper focuses on certain sociological elements related to the phenomena of climate change, including risk perception, agroecological governance, and the values that lie “beneath,” this analytic emphasis does not mean to minimize all the other qualities that make it a wicked problem.

The concept of social capital has a long history of use in research relating to sustainable development and, more recently, in efforts directed at achieving the United Nations Sustainable Development Goals (SDGs) [14]. Building on this research, this paper utilizes the concept of social capital to examine how food producers perceive and respond to climate change and its associated risks. While many scholars view social capital as a structural network attribute, and thus as an asset that can be accessed and accumulated [15,16,17], as suggested by the noun capital, this paper highlights other elements of the concept that have yet to receive equal attention. Placing analytical attention on the nature of what flows through these networks, in terms of their level of inclusiveness and diversity of values, beliefs, worldviews, and cultural orientations, does not dismiss social capital’s structural and asset-based aspects, as represented by such concepts as bonding, bridging, and linking social capital [18,19,20]. The framework presented aims to complement, not compete with, earlier social capital research.

To do this, the paper introduces the concept of social capital flow heterogeneity—or simply social capital heterogeneity. Building on the well-known sociological phenomenon of homophily—the tendency for individuals to connect with others who are “like” (similar to) them, summarized by the phrase “birds of a feather flock together” [21,22]—the paper explores how the diversity of a farmer’s networks influences perceptions of climate change risks. In doing this, social capital heterogeneity levels and those risk perceptions are shown also to have meaningful relationships with specific values.

Social capital heterogeneity enables us to explore the degree of difference—of ideas, values, beliefs, and perceptions—that circulate within a food producer’s networks. Do their peers share similar beliefs and values? How receptive are they to opposing views? How frequently do they meaningfully engage with individuals from different economic, cultural, and social backgrounds? Questions like these—focused on phenomena beyond network structure—emphasize why social capital heterogeneity warrants serious consideration. To frame it as a research question: What insights can social capital heterogeneity provide about how farmers and ranchers perceive climate change-related risks, and what relationship, if any, do these phenomena—a network’s level of inclusivity and diversity and risk perception—have to the values held by respondents?

The following section sets the stage by briefly describing social capital, while also explaining how the paper contributes to an already well-theorized concept. Here, the focus is directed at outlining how the reframing of social capital, specifically in terms of social capital heterogeneity, contributes to our understanding of how farmers perceive risk and respond to events associated with climate change. Attention then turns to the paper’s empirical grounding. The argument presented leverages data from forty-one personal interviews with Colorado-based (USA) farmers and ranchers. The methods of this research are outlined in Section 3. The remainder of the paper presents the study’s findings (Section 4), followed by a discussion of their implications for policy and future research (Section 5).

2. Social Capital and Farmer Responses to Climate Change

The concept of social capital has been around for more than a century, first coined in an article by L. J. (Lyda Judson) Hanifan in 1916 [23]. Yet it was not until the 1980s that the concept began to see widespread use to describe social networks and group and community dynamics. Pierre Bourdieu is credited by many for offering the first sustained treatment of the concept [24], defining social capital at as “the sum of the resources, actual or virtual, that accrue to an individual or a group by virtue of possessing a durable network of more or less institutionalized relationships of mutual acquaintance and recognition” [25]. Bourdieu’s framework is significant for being the first to discuss social capital’s relationships with other forms of capital, including economic and cultural capital. James Colemen also played an important, early role in developing the concept [26]. While Bourdieu leveraged the concept to understand societal inequity, power, and status, Coleman leaned into the noun “capital.” For Colemen, social capital is a collective asset that ought to be targeted and grown, similar to how concepts like human capital and economic capital tend to be treated, especially in the context of group and community development [26]. In a similar vein, Robert Putnam—who brought the concept into mainstream discourse with his best-selling books—also views social capital as a public good due to its relationship with shared norms and trust, which are key features of liberal democracies as they help foster coordination and collaboration [27].

Even before this, in the 1970s, scholarship began to shift towards elements that would later give rise to analyses directed at the network structure characteristics of social capital. Without using the term social capital, Mark Granovetter published a seminal paper in 1973 where he introduced the concept “strength of weak ties” [28]. Granovetter’s thesis was that weak social connections—for instance, casual acquaintances or, to place in a social media context, Facebook Friends—can at times be even more beneficial for accessing information and opportunities than the “strong” connections one has with close friends and family members. This concept has been instrumental in recent refinements in social capital thinking, especially as scholars have begun to understand that social capital takes different forms depending on the shape and structure of the networks involved [29,30].

Social capital is now often divided into (at least) three types: bonding social capital, bridging social capital, and linking social capital [18,19,20]. Bonding social capital refers to the “strong” ties that are contrasted with Granovetter’s thesis of “weak” ties, such as what you might find in, say, the mafia [31], homogeneous communities [32], and families [33]. Alternatively, bridging social capital is generally thought of as a type of “weak” tie between individuals and/or groups on a similar scalar plane—e.g., someone in Community X having a weak (e.g., infrequent) connection with someone in Community Y [34]. Finally, linking social capital is often another form of “weak” tie (although not always), defined by connections with more distant outside or vertically located individuals or groups, such as when someone in Community X has connections to a well-financed non-profit, zero-interest lending organization [35].

As suggested by these concepts, there is a notable interest among scholars in examining social capital through network features, such as analyzing a network’s “thickness” (as defined by the concepts of “bonding” and “bridging”) or its “length” (whether it enables connection of horizontal or vertical nodes). More recently, social capital research has expanded with the development of advanced quantitative social network analysis software. When combined with large social network datasets (big data), this software has introduced a whole new vocabulary to describe structurally related aspects of social capital, including “linkages,” “nodes,” “holes,” “centrality measures,” “betweenness,” and others [36,37].

Decades of research leave little doubt that there is value in conceptualizing social capital in terms of network types and structure [30] and by considering it as an asset [17]. Yet, who is connected and the degree (or strength) of that connection are not all that matters when conceptualizing social capital. A recent systematic review of research on the concept from the standpoint of “building community resilience in a context of climate change,” to quote from the article’s title, highlights the literature’s “strong orientation towards structural understandings of the role of social capital” [38]. Yet, the authors conclude that this focus “provides limited insights to understand how and why different outcomes unfold” [38], calling for research to “highlight the role that less visible socio-cultural dimensions (e.g., values, norms and beliefs) play in social capital” [38], arguing these elements are “often conceptually underplayed” in the literature [38]. The article then concludes with the following recommendation:

“Addressing these knowledge gaps will involve interpretivist perspectives to build on the positivist ways of thinking about social capital and resilience that currently dominate.”

[38]

This paper takes this call seriously. To do this, we can take a cue from research examining network heterogeneity, which is a reference to the level of inclusivity of values, beliefs, worldviews, cultural orientations, and so forth within one’s network. Such research details that this degree of difference is profoundly consequential in terms of actors’ perceptions and behaviors, in some instances being an even greater influence than their network’s structure [39,40]. For instance, while the earlier noted “birds of a feather” characteristic has pro-social qualities by helping to build community and nurture feelings of conviviality (e.g., bonding capital) [27], there are also social “costs” to this behavior. Phenomena like groupthink illustrate one such cost [41]; the creation of echo chambers, as seen with social media platforms, is another [42]. In both instances, beliefs are reinforced and amplified as views are made to appear “normal,” according to group standards, as networks are shielded from opposing views, opinions, and ideas [43]. Not surprisingly, then, individuals lacking exposure to heterogeneous networks are more likely to reject opposing viewpoints [44], to the point of experiencing physiological discomfort when, in those rare instances, they are exposed to different ideas [45]. Those in “like” networks are also less able to explain their decisions, which can be attributed to the fact that such networks instill in participants a belief that their worldviews are self-evident. When something is “obvious,” it does not require justification or explanation [46].

This brings the discussion to social capital heterogeneity, which refers to the levels of knowledge, value, opinion, and cultural difference within an individual’s networks. These qualities are distinct from the phenomena of network structure but demonstrably important. Consider social capital heterogeneity in the context of bridging, bonding, or linking capital. An individual or group can have either high or low levels of social capital heterogeneity independently of their network’s structure. For instance, it is possible for individuals and groups to exhibit high levels of social capital across all structural types (e.g., bonding, bridging, and linking) while showing low levels of social capital heterogeneity. This experience mirrors how many scholars describe today’s highly partisan social environment using the term “political tribes,” where “being well connected” means something very different when those connections are with “like” networks that are horizontal and vertical [47,48]. In critiques of political tribes, scholars are primarily concerned not with the network structure of today’s communities and organizations but with their (low) levels of social capital heterogeneity [46,47].

Social scientists leveraging the concept of social capital to understand farm-level phenomena tend to reproduce this structural bias, as evidenced by the frequent disaggregation of the concept using the bridging, bonding, and linking forms [49,50,51]. For example, bonding capital is known to be correlated with knowledge exchange and the adoption and diffusion of climate change adaptation practices among farmers [50], while bridging and linking capital facilitate the integration of new farm management practices and resources (especially linking capital) into farmer networks [51]. This broadly structural focus on social capital exposes a gap in research about farmers’ climate risk perception and adaptation behaviors. Social capital is most often employed to understand behaviors and transitions (e.g., social learning, knowledge co-production, technology adoption, etc.) [38]. Where the concept is lacking, as noted earlier, is in explaining the origins of the values, beliefs, and perceptions that animate those practices.

Take, for instance, a recent study examining the role of social capital on Chinese farmers’ willingness to adopt new agricultural technologies in the face of changing environmental conditions [52]. Based on data from 11,547 farmers from the China Labor Dynamics Survey, the authors conclude that social capital is an important factor for understanding willingness to adopt new technologies and farming practices [52]. Yet, the authors admit when reviewing their study’s limitations to having very little to say about the subjective elements that ultimately give meaning to why farmers do what they do [52].

Regarding risk perception, although discussed within the context of social capital, these discussions often focus narrowly on “risks” as defined by their consequences for technology adoption (e.g., market risk or risks to farm profitability). For example, a recent study explores how farmer “clustering”—a term highlighting the study’s emphasis on network structure—affects risk perception among smallholder shrimp farmers in the Mekong Delta [53]. The study mainly concentrates on market risks, noting that perceived market risk is positively associated with farmers adopting new aquaculture management practices [53].

In summary, the study outlined in the next section complements existing research that uses social capital to understand farmer behaviors and risk perceptions in the context of climate change. It does this, first, by examining the additional insights gained from looking at levels of diversity and inclusivity within networks, rather than focusing only on network structure. Second, extending these insights offers a framework, through social capital heterogeneity, to understand the subjective, interpretive side of the concept. As mentioned earlier, the literature’s “strong orientation towards structural understandings of the role of social capital […] provides limited insights to understand how and why different outcomes unfold” [38]. By focusing on the heterogeneity of what flows within networks, rather than on who is connected and the “length” and “thickness” of networks, this paper aims to improve social capital’s ability to explain the origins of individual and group values, beliefs, and (risk) perceptions. This approach opens social capital to new research questions and perspectives that structural views of the concept are less able to address. While considerable research looks at farmers’ perceptions of climate change and risk [8,9,10], social capital is usually not applied in those more subjective and attitude-based studies, due to its historical “structural” focus.

3. Methods: Data Collection, Coding, and Analysis

The data for this study were collected from August 2023 to December 2024. As noted, this paper speaks to a social capital literature with a strong quantitative orientation [38]. This focus has only intensified in recent years with the advancement of quantitative social network analysis software. To complement this focus, the decision was made at the onset to utilize qualitative methods in this study.

Recruitment began using several resources: the author has created and maintained over the years a contact list of several hundred farmers and ranchers from across the state of all scales and commodity profiles; the Farm Fresh Directory, maintained by the Colorado Department of Agriculture, which lists several hundred farms [54]; and a list of several dozen names (and contact information) of individuals from various Colorado-based farmer organizations (e.g., Colorado Association of Wheat Growers, Colorado Potato Administrative Committee, Denver Urban Gardens, etc.). A purposeful sampling technique was used to ensure the sample included a diverse array of farm and ranch types, in terms of commodity profiles, scale, market orientation (direct-to-consumer vs. direct-to-market), and management techniques (e.g., organic vs. conventional). While those initial lists were used to initiate the sample, potential respondents were invited to suggest the names of other potential respondents, allowing sampling “snowballs” to form.

An interview protocol was developed and is included in

Table 1. Semi-Structured Interview Protocol.

Concept	Questions
Basic Orientation	Tell me about your operation and about what led you to become a farmer/rancher? What are your goals as a farmer/rancher? What opportunities do you see for those in agriculture? And what risks loom on the horizon?
Change	In your view, how are food systems likely to change in the next couple of decades? What does this mean for those producing food? What does this mean for agriculture-dependent communities?
Uncertainties	Realizing it is impossible to know everything, what uncertainties need to be prioritized and made less uncertain to build sustainable food systems that support human flourishing?
Goals	What should our food systems be trying to accomplish? How well do these goals align with current farm/ranch management practices on your operation?
Barriers	What are keeping food systems from better serving our needs and goals? And what barriers stand in your way from adopting practices that better serve your goals?
Performance Measures	What performance measures could or should be used/created to evaluate whether food systems are living up to our values and meeting our needs and goals? What performance measure do you pay attention to when evaluating your management practices and alternative, yet-to-be-adopted practices?
Values	Reflecting on your answers to the prior questions, what values are being prioritized? Discussing values further, describe your positions on topics like equality, inequity, fairness, justice, sustainability, and such?
Risks	What are the most significant threats or risks facing our food systems? How does a changing climate shape risk perceptions?
Heterogeneity (10-Point Scale)	How strongly do you agree (or disagree) with these statements (1 = “strongly agree”/10 = “strongly disagree”)? My peers and I share the same: (1) political views; (2) religious or spiritual orientation; (3) level of formal education; and (4) list of favorite media (television, print, online) that we turn to for news and opinion.
Governance & Compromise (10-Point Scale)	How strongly do you agree (or disagree) with this statement (1 = “always reject”/10 = “always support”)? There is no place for compromise in politics today, and it should be avoided at all costs. Next, answer that question as the average person, in your estimation, would.
Missing Concepts	Are there any other concepts, in addition to the ten noted above, that should have been considered in this interview protocol? If yes: explore the concept.

. The instrument was first pre-tested by asking colleagues and friends, some of whom were farmers and ranchers, to read it for clarity (n = 5). The author then met with each one to hear their feedback. Notice the semi-structured nature of the instrument. Respondents were asked all questions listed, though how they answered each shaped follow-up queries. This inductive method allowed data and themes to emerge; opportunities that would have been lost had interviews rigidly followed a pre-determined structure. Interviews took between 90 and 120 min. Enrollment ceased when theoretical saturation was reached. Theoretical saturation is a widely used practice among qualitative researchers to define when sampling can cease, namely, when the emergence of themes, concepts, and connections across those concepts effectively comes to a standstill [55].

Forty-one personal interviews were ultimately completed. (Only four individuals declined an interview invitation.) The demographics of the sample are described in Table 2. For comparison with the demographics of the total population (or sample universe) of farmers and ranchers in the state, note the following data, which are based on the most recent US Census of Agriculture [56]. Among Colorado farmers and ranchers, the average age is 58.3 years. The breakdown between males and females is 59 percent to 41 percent, respectively. Eighty-two percent of the total population is White, with 12 percent “more than one race” and 2 percent American Indian/Alaska Native (all other categories are less than 1 percent). The average operation size, in acres, is 828 (or 339 hectares). Less than one percent of all farms and ranchers in Colorado are certified organic [56]. Colorado ranks in the top 10 (among the 50 states) for producing several commodities, including cattle, winter wheat, sheep, lambs, wool, alfalfa, potatoes, onions, apples, peaches, grapes, and lemons [57].

Having an exhaustive sample was never a top priority. There is no comprehensive formal, statewide list of names, addresses, and contact information for this population, which is also constantly changing as individuals enter and leave agriculture. Additionally, the resource intensity of personal interviews (such as mileage costs, overnight hotel expenses, and the fact that some parts of the state are a 10 h drive away) must be considered when determining where a “snowball” sample leads researchers for interviews. This further emphasizes the importance of the earlier mentioned principle of theoretical saturation [55]. Since many techniques used in survey research (e.g., random sampling, statistical methods to determine sample size, etc.) are difficult or impossible to employ in conducting in-depth personal interviews, grounded research methods depend on theoretical justifications to assess phenomena like internal and external validity [55,58].

Table 2. Characteristics of the sample, n = 41.

Characteristics *	Frequency
Race/Ethnicity
White	20
Hispanic, Latino/a, Spanish Origin	10
More than one	5
Black or African American	5
Asian	1
Other/Prefer not to share	0
Gender
Man	19
Woman	22
Nonbinary or Genderqueer	0
Other/Prefer not to share	0
Age
21–30	6
31–40	14
41–50	14
51–60	6
61–70	1
Farm Size, Acres (owned, leased, and rented)
<25	8
25–100	6
101–300	3
301–600	8
601–900	10
901–1200	6
Market Type
Direct-to-consumer	20
Direct-to-market	20
Both	1
Commodities, for sale
1–3	14
4–6	9
7–9	10
More than 10	8
Commodities, type, for sale (select all that apply)
Wheat	14
Alfalfa	11
Beef	9
Potatoes	6
Hay	5
Millet	4
Quinoa	3
Mutton	3
Dairy	3
Sweetcorn	3
Apples	2
Peaches	2
Eggs	2
Grapes	1
Management-type (select what best applies)
Conventional	21
Organic, certified	17
Organic non-certified	2
Biodynamic, certified—organic certification with additional biodynamic principles	1

* The author allowed respondents to operationalize (self-define) the categories. This interpretivist approach to demographic categories is not uncommon in qualitative research to improve rapport, most notably when the categories are used for descriptive purposes [59], as is the case here. As prior research indicates, researcher-defined categories like this can be off-putting to some historical minoritized groups in agriculture [60].

Table 2. Characteristics of the sample, n = 41.

Characteristics *	Frequency
Race/Ethnicity
White	20
Hispanic, Latino/a, Spanish Origin	10
More than one	5
Black or African American	5
Asian	1
Other/Prefer not to share	0
Gender
Man	19
Woman	22
Nonbinary or Genderqueer	0
Other/Prefer not to share	0
Age
21–30	6
31–40	14
41–50	14
51–60	6
61–70	1
Farm Size, Acres (owned, leased, and rented)
<25	8
25–100	6
101–300	3
301–600	8
601–900	10
901–1200	6
Market Type
Direct-to-consumer	20
Direct-to-market	20
Both	1
Commodities, for sale
1–3	14
4–6	9
7–9	10
More than 10	8
Commodities, type, for sale (select all that apply)
Wheat	14
Alfalfa	11
Beef	9
Potatoes	6
Hay	5
Millet	4
Quinoa	3
Mutton	3
Dairy	3
Sweetcorn	3
Apples	2
Peaches	2
Eggs	2
Grapes	1
Management-type (select what best applies)
Conventional	21
Organic, certified	17
Organic non-certified	2
Biodynamic, certified—organic certification with additional biodynamic principles	1

* The author allowed respondents to operationalize (self-define) the categories. This interpretivist approach to demographic categories is not uncommon in qualitative research to improve rapport, most notably when the categories are used for descriptive purposes [59], as is the case here. As prior research indicates, researcher-defined categories like this can be off-putting to some historical minoritized groups in agriculture [60].

Each interview was recorded and later transcribed. The research project was reviewed and approved by the Institutional Review Board of Colorado State University and assigned protocol code #2279.

Interview recordings were reviewed immediately after each was generated. While performing this, notes were taken on concepts raised and the relationships between them, a method known as “open coding” [61]. The concepts identified are described in

Table 3. Content Analysis Codebook: Values and Risk.

Code	Subcode	Definition
Value	Distributive justice	Concern over how material costs and benefits are distributed
	Representational justice	Decision making is insufficiently inclusive
	Precautionary principle	Innovations need to be proven safe before adopted widely
	Intergenerational justice	Concerns on how current activities impact future generations
	Wealth/efficiency	Privilege wealth creation, market expansion, and scalability
	Capabilities/affordances	Individuals are afforded structural capabilities to flourish
	Collectivism	Emphasize the importance of societal over individual needs
	Individualism	Emphasize individual (over collective) needs
Risk	Changing climate	More weather events at the “tail” of the normal distribution
	Government regulation	Increasing government oversight
	Land availability	Rising land prices, urban sprawl, diminishing arable land
	Food prices	Increasing food prices
	Resource scarcity	Dwindling (access to) natural resources
	Corporatization	Growing corporate control of food systems
	Geopolitical uncertainty	International conflicts that disrupt trade/production
	Poverty	Rising levels of inequality
	Farm profitability	Farmers getting squeezed by buyers and sellers
	Population growth	Concerns about production keeping up with demand
	Farm lifestyle viability	Concerns family farms/ranches might disappear

, specifically “values” and “risks” along with their related sub-codes and definitions, based on how they were represented in the interview data. The “risks” identified highlight climate change-related risks (in agriculture) noted by respondents.

After collecting the data, the process transitioned to “focused coding” [61]. Focus coding involves curating and organizing concepts and relationships into themes while engaging with the relevant literature, thereby sharpening the conceptual and analytical gaze of those conducting the coding. These themes thus provide the organizational structure of the following section.

It was during focused coding that attention turned to identifying “co-occurrences,” defined elsewhere as “when two concepts or themes were discussed concurrently, during the course of a single participant’s response” [62]. Note, too, that “a single participant could demonstrate multiple co-occurrences of the same two concepts (in different responses to different questions)” [62]. I made tabulations of these co-occurrences, with specific attention paid to the connectedness of sub-codes across the categories of “values” and “risk.”

Another technique employed during focused coding involved calculating the prevalence of the eight values listed and defined in Table 3. Value prevalence was calculated by curating all quotations for each respondent that articulated one or multiple values, following a practice published elsewhere [63]. I then assigned a numerical value, as a percentage of the total number of quotations, to each value. For instance, if one respondent had 100 quotes and 50 related to the value of “distributive justice,” its prevalence would be 50 percent. And if that percentage were the highest among all the values articulated for that individual, I take that as evidence that it is an important (if not the most important) value for them.

Lastly, a word about the set of questions in the interview protocol labeled “social capital heterogeneity” and “governance and compromise”—points 9 and 10 in Table 1. These were the only non-open-ended (Likert-scale) questions asked, though once answered, respondents were asked to explain their responses. The “social capital heterogeneity” questions were inspired by a similar series of questions used in prior studies to assess an individual’s network heterogeneity/homogeneity [64,65].

Two “scores” were then created: (1) social capital heterogeneity score and (2) compromise score. The former score was calculated by averaging each respondent’s answers to the 10-point Likert questions having to do with “social capital heterogeneity” in Table 1. A Cronbach score was calculated to assess whether these questions target shared characteristics. The Cronbach’s α equaled 0.77—the literature indicates a score above 0.70 is acceptable [66]. In addition, a compromise score was calculated by averaging their responses to the two 10-point Likert questions about “governance and compromise.” No statistical proof was applied to assess this score.

No further statistical techniques are employed due to the study’s sample size. Because of the large standard errors associated with such a small, non-representative sample, statistical tests have very low “power.” Emphasis instead is placed on data triangulation, which cumulatively affords the most significant “power” to this analysis.

4. Results

This section is organized into several subsections, each building upon the next. In the first, co-occurrences between values and climate-change-related risks are examined alongside supporting qualitative interview data. This discussion underscores the significance of values in shaping perceptions of climate change and its associated risks. Next, focus shifts to the “social capital heterogeneity score,” with particular attention to how social capital heterogeneity relates to the values expressed by respondents. The “compromise score” is then discussed in the context of its connection to social capital heterogeneity. The section concludes by examining how some of these variables shape perceptions of climate change. Throughout this section, qualitative interview data are also used extensively to describe and explain relationships, rather than statistical techniques, for the reasons given above.

4.1. Co-Occurrences: Risks and Values

Table 4. Co-occurrences across respondents with values (vertical) and risks (horizontal).

RISK ALUE	Government Regulation	Poverty	Corporate Concentration	Climate Change	Farm Profit	Resource Scarcity	Land Availability	Geopolitical Uncertainty	Farm Lifestyle	Population Growth	Food Price	Total
Wealth/efficiency	42				31	33		12		15		133
Individualism	38		16		22	14		8	4			102
Distributive justice	8	14	24	12			12		5		11	86
Collectivism		32		24			14		3			73
Capabilities/affordances		19	27				4					50
Intergenerational justice		15		18		3			5	2		43
Representational justice	4		12									16
Precautionary principle			8	2								10
Total	92	80	79	56	53	50	28	20	17	17	11	Total

explores how often climate change-related (agricultural) risks were discussed alongside values. When interpreting these results, remember the main goals of this research, which include investigating the links between social capital heterogeneity and the values and risks respondents highlighted. While the concept of social capital is routinely used in the literature to understand behaviors and transitions (e.g., social learning, knowledge co-production, technology adoption, etc.), it is typically not leveraged to explain subjects having to do with values, beliefs, and perceptions, including risk perceptions. This subsection lays the groundwork for expanding the explanatory potential of the concept.

Let us begin with some high-level observations. Government regulation was the most frequently mentioned risk, which speaks to a concern among respondents that climate change—regardless of whether they believed it was real or not—would be used to justify an expanded state and additional rules, regulations, and restrictions that this move implies. This risk had a total of 92 co-occurrences across all eight values. Representative quotes of this sentiment include the following.

“When someone says, ‘climate change,’ they are thinking ‘more government.’ That concerns me because what that means is higher prices, more paperwork, bureaucracy, and lower profitability.”

(Farmer #3)

“I think the biggest threat to agriculture are politicians who believe in climate change. If they had their way, they’d put people like me out of business by regulating us to death.”

(Farmer #33)

Meanwhile, the most prominent value discussed among respondents also had a high number of co-occurrences with the risk of government regulation, which was wealth/efficiency, with a total of 133 co-occurrences across all risks.

Note, too, the relationships described in the table between specific values and risks. Government regulation, for instance, tended to co-occur with value statements pointing to wealth/efficiency and individualism. Meanwhile, poverty—the second most mentioned risk—was associated with the values of collectivism, capabilities/affordances, intergenerational justice, and distributive justice.

There was also an asymmetrical distribution of co-occurrences across respondents. Recall that value prevalence was calculated for each respondent, providing a basis for understanding how respondents varied in terms of their most closely held values. For illustrative purposes, take the values mentioned in the prior paragraph. No respondent who held wealth/efficiency or individualism as their top value had either collectivism, capabilities/affordances, intergenerational justice, or distributive justice as their second most prevalent value. The inverse is also true. In short, those “sets” of values, for lack of a better term, made for unlikely bedfellows.

This observation aligns with a large body of research that investigates the individualism–collectivism (also referred to as individualism–communitarianism) continuum as a basis for understanding one’s cultural values and worldview [67,68,69,70]. According to this research, these contrasting values are rarely held equally by individuals. In other words, those who ascribe to individualism prioritize individual liberties and freedoms, while values linked to collectivism prioritize social and collective wellbeing and value interdependence over independence [69,70,71,72]. The interview data support this thesis and distinction—that these value “sets” are indeed more contrasting than complementary.

The following quote is from a rancher who had a value prevalence ranking with individualism and wealth/efficiency in the one and two spots, respectively. Note how, in exposing their values, they also belittle those with contrasting values, which I take as a signal that adds further clarity about their value hierarchy.

“People say farmers are selfish and only care about making a profit. Well, I do have to make sure my operation stays in black. If I don’t make money, my family doesn’t eat. So, I do care about running a profitable ranch. I guess that makes me a bad person because I don’t place the needs of polar bears or future generations ahead of my family’s needs like some crazy environmentalist.”

(Farmer #11)

In this quote, we see the inverse: someone who espoused collectivism and capabilities/affordances in their one and two spots, respectively, who mocked those privileging individualistic values.

“There has to be a societal response to climate change because, hello, it’s not called ‘global’ climate change for nothing. I don’t get those who say farmers need to be left alone [e.g., minimum government regulation]. Leaving us [farmers] alone is what got us into this mess. People who say they cherished freedom as just looking for excuses to be selfish”

(Farmer #21)

4.2. Value Prevalence and Social Capital Heterogeneity

The above discussion raises the question: Where do these values originate? Queries like this raise tricky chicken-and-egg causality questions. In this subsection, links between social capital heterogeneity and value prevalence are explored. While sidestepping the question of which is the antecedent—after all, it is difficult to determine whether the networks describe afford values or whether values lead individuals to participate in particular networks—it is enough to emphasize their relationality. Thus, building on where the prior subsection left off, let us now examine the links between value prevalence and social capital heterogeneity.

Figure 1 illustrates the relationship between respondents’ social capital heterogeneity scores (x-axis) and their most prevalent value (y-axis). For instance, two respondents had a heterogeneity score of 1, signifying very homogenous networks, while their most prevalent value, gleaned from the interviews, was “individualism.” Alternatively, the values “collectivism,” “distributive justice,” and “capabilities/affordances” were more likely to be associated with respondents with higher heterogeneity scores.

Several observations can be made about these data. To begin, the social capital heterogeneity score tends toward a bimodal distribution, which is a notable finding given the tendency in survey (Likert-scale) research for respondents to treat the middle category as a catch-all for uncertain, unsure, neutral, and non-applicable responses [73]. The qualitative data offer possible explanations for this, as respondents tended to think about social networks as being either diverse or not. The idea of there being a third—e.g., middling or in-between—category when thinking about network diversity was often not part of how respondents framed the topic. An example of this sentiment is conveyed in the following quote.

“I have family members who refuse to interact with anyone who doesn’t think like they do. I’m nothing like that. Do I embrace diversity or choose to live in an echo chamber? Of those two camps, I’m in the former. I believe in hearing from different viewpoints and backgrounds.”

(Farmer #40)

To further clarify these relationships, a second figure is presented: Figure 2. (“Precautionary principle” did not reach either level of prevalence for any respondent, which explains its exclusion from both images). Figure 2 is similar to the first but focuses on the second most prevalent value for each respondent. Digging further into those figures using the qualitative interview data, tentative observations can also be made connecting respondent positionality with social capital heterogeneity. For instance, producers engaged in direct-to-consumer market relationships were more likely to have higher heterogeneity scores than those in direct-to-market relationships. Also, those operating non-conventional operations (e.g., organic) generally had higher heterogeneity scores when compared to conventional farmers and ranchers.

Regarding the aforementioned chicken-and-egg question, the data are unclear about whether market relationships promote network heterogeneity and related values or if preexisting values and the desire for diverse networks draw individuals into diverse relationships. Both explanations fit with the data. On the one hand, several respondents noted that there is something inherent to direct-to-market relationships that encourages network heterogeneity.

“You can’t do this and not be comfortable around ‘people of all stripes’ [a North American saying to refer to people with diverse backgrounds]. […] If you’re not okay engaging with people different from yourself, then you probably should stop trying to sell directly to consumers.”

(Farmer #22)

“Farmer’s markets are more than transactional spaces. You’re not just exchanging money for food. They’re relational, where you’re getting to know people and they’re getting to know you. […] If you’re not open-minded, if you come across as a know-it-all or bigoted, word spreads and you’ll fail. […] Those spaces open your world by helping you connect with people you’d never otherwise connect with.”

(Farmer #34)

On the other hand, respondents also noted that preexisting values and social networks drew them to become the farmers that they are.

“I’ve been concerned about equity and sustainability since I was old enough to think about those issues. […] It was a given, me becoming an organic, urban farmer dedicated to food and social justice.”

(Farmer #28)

“Ask my parents, I wanted to turn our [farming] operation into an organic farm since I was a kid. […] When I finally got old enough to make the call, we started the transition [to eventual organic certification].”

(Farmer #1)

The interviews also add resolution by helping to disentangle relationships. For instance, representational justice was reported among those with relatively closed networks, as well as among those with higher social capital heterogeneity scores—see especially Figure 2. With assistance from the qualitative data, I can address these relationships by referencing respondents’ contrasting interpretative frames regarding who deserves justice, as there were differing interpretations as to whose voices and standpoints were marginalized across food systems. Those with low heterogeneity scores may have discussed the need for improved inclusivity when discussing agriculture-related policymaking. Yet it was they—e.g., white, often-male rural farmers—who were being discriminated against.

“No one seems to care about us [white farmers] anymore. […] Not only do our voices not seem to count, but it feels like the public is hostile to our way of life. […] Look at all the money, like zero-interest loans and grants, going to minority farmers. How is that fair? I’d like some free money, too.”

(Farmer #16)

“Think of all the laws and regulations out there that negatively impact people like me, a hardworking, tax-paying American. […] Meanwhile, our government is giving food stamps to illegal immigrants and spending money to take land out of production agriculture, so that people in the city can have their green space. […] You know who comes up with those policies, folks who haven’t a clue what it takes to make it out here; someone who’s never stepped foot on a real farm.”

(Farmer #11)

In contrast, respondents with high social capital heterogeneity scores and who demonstrated high representational justice value prevalence were all farmers with minoritized (i.e., non-white) racial/ethnic identities. For them, food systems and responses to the risks of climate change also require greater stakeholder inclusivity. Yet, unlike the prior group, “inclusivity” for this set of respondents rested on a different understanding of who deserves justice. For this group, inclusivity and representational justice meant “making sure food and ag policy isn’t driven by voices, experts, and politicians, who haven’t a clue of what it means to be a farmer of color” (Farmer #31).

4.3. Governance and the Capacity to Compromise

Figure 3 details the relationship between respondents’ social capital heterogeneity scores (x-axis) and compromise scores (y-axis). The figure suggests a (positive) relationship, indicating that as network heterogeneity increases, so does one’s openness to compromise. The finding aligns with prior studies on the topic, as network homophily has been associated with “preference for certainty” [74] and “dogmatism” and “belief superiority” [75], as those embedded in “like” networks are more likely to believe their views are superior and right [40,41,75].

There also appears to be a ceiling to one’s willingness to compromise, whereas the basement (rejection of compromise) extends to an absolute level. To recall, the range of responses extended from “always reject” to “always support.” While several respondents believed they and others should always reject the principle of compromise, no one was willing to report the opposite level of strength in support of compromise. Respondents who expressed an openness to compromise found it necessary to qualify the value they placed on the principle, especially if dealing with someone with reprehensible beliefs who also demonstrates dogmatism and belief superiority. As one respondent put it,

“I’m all about compromise, but that doesn’t mean I’m open to compromising with a Nazi, white supremacist.”

(Farmer #31)

Leaving some room to reject compromise based on the situation should therefore not be viewed as problematic from the standpoint of effective governance.

The subject of compromise also elicited qualitative data that support the thesis that the social capital heterogeneity score is measuring what it purports to, namely, the inclusiveness and diversity of values, beliefs, worldviews, and cultural orientations among an individual’s networks. For a representative quote, take Farmer #22, who had an “8” for both their compromise and social capital heterogeneity scores. While elaborating on their views about compromise, they made the following observation about their networks, which is useful, as it reinforces the idea that heterogeneous networks are inclusive, and vice versa.

“I interact with all types of people: young and old, different educational levels, sexual orientations, races, religions, Democrats and Republicans, different class backgrounds. […] I interact with all sorts of people because I’m comfortable doing so.”

(Farmer #22)

At this point, they refer to something mentioned earlier in the interview, which I had quoted in an earlier section—see Section 4.2—before concluding by tying the observation back to the principle of compromise.

“As I said earlier, I can’t run a successful business if I’m just interacting with people that look, think, and pray like me. When you interact with a lot of people, you inevitably develop some acuity to different viewpoints, which all feeds into some level of willingness to compromise.”

(Farmer #22)

4.4. Perceptions of Climate Change Responsibility

All respondents admitted to having observed changes in climate during their lifetime, ranging from alterations in seasonal precipitation and temperatures to more frequent weather extremes. Views differed, however, on (1) whether those changes were human-caused and (2) if food producers had any responsibility to lower their GHG footprints. Yet, typologies can be loosely constructed around the characteristics discussed.

Lower social capital heterogeneity scores were more frequently associated with respondents who denied the human link to climate change and thus rejected the idea that society, and farmers, in particular, needed to adopt practices and policies that mitigated GHG emissions. A representative quote describing this grouping is as follows:

“Because we’re seeing less snowpack [in recent years compared to years past] doesn’t mean it’s because of anything humans have done. Climate changes. That’s what it does. […] As part of a natural cycle, we’re shooting ourselves in the foot by taking steps [that reduce GHG emissions] that increase costs while lowering production.”

(Farmer #13)

This view is further amplified by the nature of the information that moves through their networks, namely, like-minded information that does little to challenge existing beliefs.

“The notion that man is responsible for climate change is a bunch of hooey [North American slang for ‘nonsense’]. Everyone I know knows it’s [climate change] a load of crap.”

(Farmer #25, my emphasis)

In contrast, those with higher social capital heterogeneity scores were more likely to accept that humans are at least partly responsible for climate change. This, in turn, made them more inclined to discuss GHG mitigation as a consideration when making farm management decisions or developing agricultural policies. As one farmer put it when describing how their diverse networks shape understandings about who needs to act in the face of climate change:

“The more I interact with people across the food system in our state, from food activists in Denver dealing with food access issues to potato growers grappling with water shortages or dairy farmers worried about temperature extremes and zoonotic disease, the more I realize we’re in this together. Once you see that, you appreciate the need for a collective response to climate change.”

(Farmer #6)

5. Discussion and Conclusions

The findings describe a nuanced understanding of how agricultural producers perceive and respond to climate change, showing a relationship between climate change-related risk perceptions and underlying values, which together have connections to respondents’ social capital heterogeneity scores. The top three risks prioritized among respondents, in the context of climate change, include “government regulation,” “poverty,” and “corporate concentration,” where farmer/rancher autonomy and independence, economic livelihoods, and market economic agency, respectively, are believed to be under threat. Yet, such risks are distributed neither evenly nor randomly among respondents. To understand who expressed which risk, we also need to investigate what values they hold dear. While, for example, “government regulation” was strongly associated with the values of “wealth/efficiency” and “individualism,” the risk of “poverty” tended to be prioritized among those who held very different values, like “collectivism,” “capabilities/affordances,” and/or “distributive justice.” Turning to the concept of social capital heterogeneity, respondents describing the former risk and values tended to be embedded in networks with “low” scores. In contrast, respondents linked with the latter risk and values were part of networks with “higher” scores.

While previous research emphasizes the sociological importance of network structure [28,29,30,50,51,52,53], this paper expands our understanding of social capital by focusing on the inclusiveness and diversity of values, opinions, and worldviews that inhabit networks. Concentrating solely on network type—bonding, bridging, linking, etc.—overlooks an equally crucial aspect of social capital, which is described as social capital heterogeneity. As demonstrated, ignoring the heterogeneity or homogeneity of social capital limits our understanding of human behavior and social perceptions and reduces the explanatory value of social capital.

This paper also contributes to the growing body of criticism directed at the knowledge deficit model of risk perception [76,77], which has been used to explain climate change skepticism [78]. Following this model, disagreement is a product of some having “more” and/or access to “better” knowledge than others [76,77]. Rather than supporting the thesis that risk perception divergence and climate change denialism are best tackled by changing what individuals know, the thesis supported by the data is that who people know is profoundly consequential, to the point of deeply shaping one’s attitudes, values, and behavior.

The findings highlight costs related to networks that are “like”—costs that, at a minimum, hinder our ability to govern effectively. From a practical governance perspective—assuming this involves a willingness to compromise and the capacity to engage with different (non-“like”) others—the findings emphasize the importance of programs, activities, and events that foster social capital heterogeneity. In the case of farmers and ranchers, these diversifying activities might include organizing events that bring together, for example, small-scale, direct-to-consumer producers and large-scale, direct-to-market growers or that connect rural agricultural stakeholders with urban consumers [46,79].

Additionally, these findings raise questions that can only be answered within a governance context because they involve deciding whether certain values should be prioritized, especially since different values tend to afford different perceptions of risk. Depending on how this discussion develops, the paper offers insights into how to promote certain values over others, based on the identified relationships between social capital heterogeneity and various values. However, this discussion about values also highlights a tension in the paper’s findings. While agreeing that effective governance requires some openness to compromise, which supports promoting social capital heterogeneity, this also implies indirectly favoring certain values over others, given the relationships established in earlier figures.

The findings also offer insights into how government funding priorities influence outlooks and perspectives in agricultural communities. Specifically, social capital heterogeneity provides an important perspective on the effects of recent federal spending cuts in the US. For example, programs aimed at promoting diversity, equity, and inclusivity (DEI) within agricultural and farming networks, including those supporting historically minoritized groups, have been shut down since President Trump’s inauguration [80]. Significant funding has also been trimmed for alternative production practices, such as regenerative agriculture, especially in initiatives that mention “climate change” anywhere in their mission, which will likely reduce social capital heterogeneity in agriculture [81]. Viewing social capital solely through a structural lens risks obscuring the full impact of these cuts; this perspective does not consider the diversity of ideas, opinions, and perceptions within networks, but only the network types—e.g., how many bridging, bonding, and linking networks an individual can access. Using the concept of social capital heterogeneity allows us to analyze elements that reveal the true cost of these cuts.

This research also highlights intriguing connections between DEI efforts in agriculture and perceptions of climate change. As described earlier, just as certain values and climate change risk perceptions are linked to “like” networks, so are specific values and risk perceptions connected to high levels of social capital heterogeneity. If social capital heterogeneity were to decline due to shifting federal funding priorities, the data suggest that this would also influence which values and risks are emphasized in these networks. Specifically, values and risks related to individualism and markets (me-directed) are likely to increase, while those representing more collective and communal (we-directed) orientations may diminish.

The study has several limitations and blind spots that need to be acknowledged, which limit the generalizability of the findings. Respondents came exclusively from Colorado, which raises questions about the extent to which the results can be applied beyond that region, especially in an international context. While following the grounded theory principle of theoretical saturation [55] and conducting a total of forty-one in-depth, semi-structured interviews, which resulted in nearly 80 h of interview data, the diversity of characteristics identified means that not all connections in the analysis are equally strong. For example, only 20 individuals interviewed were associated with either direct-to-consumer or direct-to-market value chains. In sum, the findings are at best suggestive and require additional research.

In conclusion, this study highlights the importance of drawing on expertise from the social, cultural, and behavioral sciences, as well as from the ecological, biological, and engineering sciences, in addressing grand challenges. It notes the sociological underpinnings of phenomena such as risk identification and governance [82]. It also foregrounds an underexplored aspect of social capital, which is emphasized to complement the extensive scholarship that focuses on phenomena like network structure and asset characteristics (e.g., number of connections, “thickness” of those connections, “centrality” within a network, “betweenness,”, etc.). Overall, the study provides a critical reminder that who individuals and groups connect with, in terms of “like”-ness, matters as much as how they connect.