Network-smart extension could catalyze social learning
California Agriculture 69(2):113-122. https://doi.org/10.3733/ca.E.v069n02p113
Published online April 01, 2015
Social learning, learning from others, has value in extending knowledge about farm management through networks of growers. Exactly how much value depends on the structure of the networks. We employed social network analysis to study knowledge networks and social learning in three American Viticulture Areas in California: Central Coast, Lodi and Napa Valley. In a survey, growers confirmed that experiential and social learning are more useful for accessing information about farm management than formal learning. UC Agriculture and Natural Resources Cooperative Extension (UCCE) was found to be well positioned to access and spread knowledge through the grower networks but a bottleneck exists — many knowledge-sharing relationships and relatively few staff. We also found that grower participation in traditional outreach activities, e.g., meetings and demonstrations, is a strong predictor of their number of knowledge-sharing relationships, so UCCE and other agricultural support organizations have an important role to play in strengthening networks. Several network-smart extension strategies might help alleviate the bottleneck and rewire networks to more efficiently connect those with questions to those with solutions.
Agriculture is a knowledge-intensive industry. Therefore, developing new and innovative extension strategies is among the most pressing challenges facing contemporary agriculture (Pretty et al. 2010). Studies have highlighted the value of social learning (people learning from one another), and social learning is considered a critical pathway for extending knowledge about farm management (Pretty and Chambers 2003; Roling and Wagemakers 1998; Warner 2007a). Compared to when they were established in the late 19th century, today's extension systems are more complex, dynamic and networked, and the work of extension may benefit by capitalizing on the network structure of the modern knowledge system (Lubell et al. 2014).
Elsewhere, we have shown a positive relationship between growers’ number of knowledge-sharing relationships and their adoption of beneficial management practices (Hoffman 2013). However, before Cooperative Extension and other agricultural support organizations (e.g., commissions, marketing orders, voluntary grower associations) can develop extension strategies that harness the natural process of social learning, we must first understand the structure of these knowledge networks and identify leverage points that can rewire the network to connect those with solutions to those with questions.
Lodi wine grape growers Craig Ledbetter (left) and Aaron Lange (right) share thoughts after observing a trial pass of a prototype multirow in-row cultivator, engaging in both experiential and social learning simultaneously. In a survey of growers, field research and trials (experiential) and interpersonal relationships with other growers (social) were ranked as highly useful sources for learning about vineyard management.
The objective of our research was to find a scientific basis on which networksmart extension strategies can be based. We employed social network analysis (Wasserman and Faust 1994) to study knowledge networks in three American Viticulture Areas (AVAs) in California: Central Coast, Lodi and Napa Valley. We compared the usefulness of social learning to that of two other learning pathways: experiential learning and formal learning. The three knowledge networks in the AVAs were modeled to identify growers and outreach professionals who are optimally positioned in the network to access and share information. Next, we looked at the association between grower participation in extension activities and their number of knowledge-sharing relationships — where a positive association would suggest participation may increase their capacity for social learning. At the conclusion of our work, we were able to suggest strategies for network-smart extension.
Viticulture partnership networks
The three AVAs in our study contain many active partnerships — intentional multiyear relationships among agricultural support organizations, scientists, other stakeholders and growers with the goal of extending practical knowledge about agriculture through applied research and outreach (Warner 2007a). The numerous partnerships in California viticulture have supported grower adoption of sustainable winegrowing practices across geographical regions (Broome and Warner 2008; Ohmart 2008; Shaw et al. 2011). According to Warner, “California's winegrape growers have undertaken more partnerships to greater effect than those of any other U.S. crop” (Warner 2007b). Partnerships that have had a positive influence on grower adoption of sustainability practices in the Central Coast, Lodi and Napa Valley AVAs include the Vineyard Team (formerly the Central Coast Vineyard Team), the Lodi Winegrape Commission and the Napa Valley Grapegrowers, respectively. The California Sustainable Winegrowing Alliance is a state-level partnership.
UCCE San Joaquin County farm advisor Paul Verdegaal (right) discussing trellising systems with Lodi wine grape grower Joseph Spano (left). This boots on-the-ground approach is a hallmark of traditional extension, but is becoming increasingly difficult to implement with low ratios of farm advisors to growers. Network-smart extension strategies may be useful for extensionists who are overloaded with inquiries.
One of the defining characteristics of partnerships is their networked structure (Lubell et al. 2014; Warner 2007a). As opposed to the traditional Cooperative Extension model, which relies on vertical transfer of knowledge from universities to practitioners, partnerships are ordered horizontally and knowledge is created and shared among diverse groups of people (including those working within Cooperative Extension). Prence and Grieshop (2001) summarized the operational principles of the partnership model as local leadership, personal relationships, equal partnership, collaborative learning, responsive farmer outreach and voluntary practice adoption. The partnership model demonstrates that agricultural knowledge is extended most effectively through strategies that support learning from practical experience and from participating in a network of other growers and experts (Hassanein 1999; Roling and Wagemakers 1998; Warner 2007a).
Agricultural knowledge is extended through three learning pathways: formal, experiential and social.
The defining feature of formal learning resources is that they transfer knowledge through text from expert to learner, where the learner is strictly the receiver of knowledge (Cofer 2000). The expert determines the content to be learned and the objective of the learning process. Formal learning resources we considered in our study include agricultural journals, industry magazines, text or reference books, Internet resources and self-assessment workbooks.
Experiential learning is learning by doing. Knowledge is acquired through experiences, observations and engagement with the surrounding environment (Kolb 1984). It is continually sharpened through a repeated cycle of engagement in practice, reflection on process and outcomes, and refinement of decision making. Kolb (1984) defines experiential learning “as the process whereby knowledge is created through the transformation of experience.” Experiential learning is meaningful to growers because it has direct and tangible implications in the practice of farming. Examples of experiential learning include growers’ observations of their vineyard conditions, trial and error, on-farm research and written recordkeeping.
Social learning is learning from others, a social process of knowledge distribution among a network of individuals who share a common set of practices, knowledge and decision-making contexts (Wenger 1998). Knowledge networks are the social infrastructure that support social learning (Phelps et al. 2012). An individual's ability to engage in social learning activities such as the generation, access and spreading of ideas is either constrained or enabled depending on the structure of the network and the individual's position in that network. Examples of social learning considered in this paper include knowledge sharing between growers and pest control advisers (PCAs), UC Agriculture and Natural Resources Cooperative Extension (UCCE) staff, and vineyard sales representatives.
Knowledge network theory
We couch social learning in three theoretical viewpoints: diffusion of innovation, cultural evolution and social capital (Lubell and Fulton 2008; Shaw et al. 2011; Tomich et al. 2011). These theories help explain human behavior as a function of one's position in the knowledge network. They provide a framework for understanding how and why information is or is not legitimized, vetted and ultimately adopted by individuals within a social network. These theories serve as a basis for designing network-smart extension strategies.
Diffusion of innovation theory argues that knowledge about the relative benefits and costs of innovations spreads through social networks over time (Rogers 2003; Rogers and Kincaid 1981). Early adopters of agricultural innovations bear the costs of experimentation and risk the chance of failure. Late adopters avoid these risks, but they may be slow to reap the rewards of successful innovations. The diffusion of innovation perspective sheds light not only on how new technologies and ideas are spread through a community but also on how their economic and practical worth is vetted among community members and on who benefits most from adoption of successful innovations. In the long run, the community adopts only successful innovations.
Cultural evolution theory posits that beliefs and behaviors spread in a network through social mechanisms, mechanisms such as an imitation of prestigious and successful individuals or a conforming to the most widespread behaviors in the network (Henrich 2001; Richardson and Boyed 2005). It suggests that social learning reduces the individual costs of knowledge development because lessons learned by one individual do not have to be learned personally by others in the network, leading to faster diffusion of innovations and understanding of their costs and benefits. These social processes of imitation and conforming have positive implications for extension when sound information and beneficial practices are spread through the network. However, they pose an extension challenge when prestigious or successful individuals, or a large group of people, in the network accept unfounded information or adopt ineffective practices. Hence, Cooperative Extension and other academic institutions have an important role in bringing science to bear on the ideas being spread through knowledge networks.
Social capital theory addresses the role and value of social connections in a community (Coleman 1990; Putnam 2000). Social capital among community members, and their shared trust, is key for solving collective action problems that require cooperation (e.g., reducing agricultural nonpoint source pollution requires adoption of proper irrigation and nutrition management practices from most growers in a watershed) (Ostrom 1990). Two types of social capital interest us.
Bonding social capital, the tight social ties among locals, is important when community cooperation and information sharing are necessary for solving local problems. Bridging social capital, the loose social ties to individuals outside of a community, is key for accessing information to solve new or otherwise challenging local problems (Flora and Flora 1993; Flora and Flora 2008). Social capital theory helps explain why some agricultural communities are able to solve local problems by sharing information locally and accessing information globally while other communities fail (Flora and Flora 1993).
We collected our data with a mail survey that we customized for each of the three regions of study: Central Coast, Lodi and Napa Valley. The Lodi survey was delivered during 2010 and 2011. The Central Coast and Napa Valley surveys were delivered during 2011 and 2012. An advisory committee of 25 growers and outreach professionals was consulted during all stages of the research process. We compiled lists of growers by using the 2010–2011 wine grape pesticide use reports from the 10 counties in the Central Coast region (Alameda, Contra Costa, Monterey, San Benito, San Luis Obispo, San Mateo, Santa Barbara, Santa Clara, Santa Cruz, Ventura), the two counties in the Lodi region (Sacramento, San Joaquin) and the one county in the Napa Valley (Napa). As mandated by the California legislature, growers are required to report their use of pesticides, including those approved for organic certification, to their county agricultural commissioner office. Growers not applying any pesticides to their vineyards would not be captured by these reports; however, due to the pervasiveness of powdery mildew (Erysiphe necator) in wine grapes, few growers refrain from applying fungicides. These lists are therefore representative of our grower population. We supplemented these lists and corrected inaccuracies using Internet searches of publicly available information.
Following the Dillman method (Dillman 2007), we sent an invitation letter followed by a first survey, a reminder, a second survey, a second reminder and a final reminder. We collected a total of 822 completed surveys out of 2,085 eligible respondents, a response rate of 39.42%. By region, we achieved response rates of 32.52% in the Central Coast (358 collected of 1,101 eligible), 53.41% in the Lodi region (227 of 425) and 42.40% in Napa Valley (237 of 559). We calculated response rates using AAPOR guidelines (AAPOR 2009).
Most useful information resources
We asked survey respondents to rate on a scale of 1 to 3 the usefulness of 21 information resources for learning about vineyard management, with “not useful” equaling a value of 1, “somewhat useful” equaling 2 and “very useful” equaling 3. We subsequently grouped the information resources by learning pathway (experiential, social or formal) and examined the ratings of the individual resources and each pathway.
Table 1 reports the percentage of respondents who selected each rating within each pathway. A majority of respondents rated information resources in the experiential (68%) and social (68%) learning pathways as very useful. Noticeably fewer respondents rated information resources in the formal (45%) path way as very useful. Only a small number of respondents rated the experiential (3%) and social (4%) pathways as not useful, but a larger number reported those in the formal pathway (10%) as not useful.
Table 1 also reports the mean usefulness scores for each learning pathway. The mean usefulness scores for information resources in the experiential and social learning pathways (2.66 and 2.64, respectively) were slightly higher than the average score of those in the formal pathway. The modal usefulness score (not shown) for each learning pathway was 3 (very useful).
Table 2 breaks down the learning pathway data to show the percentage of respondents who ranked each of the 21 information resources as being very useful. The resources are sorted in decreasing order of usefulness (as rated by all respondents in the three regions) and are color coded by learning pathway. The top 10 resources per region are listed.
Some standout regional differences were found among growers’ preferred learning resources. First, pest control advisers (PCAs) were ranked much lower in Napa Valley (10th) than in the Central Coast (fourth) and in Lodi (second). The Napa Valley vineyards are frequently farmed by for-hire management companies, who may do their own pest monitoring and pesticide recommendations. In contrast, it is more common for Lodi growers to manage their own vineyards and hire a PCA. Another noticeable difference was that in Lodi, other growers were ranked as less useful (seventh) than they were in the Central Coast (second) and Napa Valley (third). One possible explanation is that Lodi growers rely less on other growers and more on PCAs and UCCE for advice, both of which were ranked as more useful in Lodi than in other regions. Overall, many of the same learning resources appeared in each region's top 10 list; though other growers (family) was absent in the Napa Valley list, viticulture consultants was absent in the Lodi list, and UCCE county farm advisors was absent in the Central Coast list.
The regional similarities in the data tell an interesting and useful story in terms of identifying network-smart extension strategies with universal application. Across the regions, respondents reported that observations of their own vineyard was the most useful learning resource, with 90% of respondents rating the resource as very useful (table 2), which points to the geographically universal power of experiential learning. PCAs, vineyard field crew and other wine grape growers (not family) — all social learning resources — were the second, third and fourth most useful learning resources, respectively, across the regions (table 2). The process of trial and error, and research trials conducted in a grower's own fields — experiential learning resources — were the next most highly ranked. All the top 10 resources across the regions were experiential or social. No formal learning resource appeared on any region's top 10 list.
These results validate the argument that grower learning is grounded primarily in personal experience and knowledge-sharing relationships, and the data is consistent with the findings of similar studies (Hood and Shearer 2001; Knapp and Fernandez-Gimenez 2009; Korsching and Malia 1991).
Position in network, knowledge agents
Since an individual's ability to access and spread knowledge is dependent on his or her position in the knowledge network, we modeled the networks in the three AVAs to identify how growers and outreach professionals are positioned in them. The three knowledge networks include growers and 12 types of outreach professionals: for-hire vineyard managers, PCAs, viticulture consultants, vintners, vineyard sales representatives, UCCE staff (farm advisors and specialists), winery representatives, labor contractors, research scientists, partnership staff, Natural Resources Conservation Service (NRCS) staff and county agricultural commissioners.
Using conventional network data collection methods that rely on survey respondents’ recollection of their recent social interactions (Knoke and Yang 2008), we asked growers to provide the names of other growers and outreach professionals with whom they communicated for advice about vineyard management. Matrices of relational data were constructed from this survey question. The matrices were non-directional. Even though survey respondents were asked only to nominate others with whom they communicated about vineyard management, we assumed that knowledge-sharing relationships were reciprocal.
We calculated individuals’ centrality in the networks. Centrality is a measurement of how connected an individual is to the rest of the network. Individuals with high centrality have great potential to be aware of others’ opinions, insights or expertise and to rapidly spread information throughout the entire network because they are connected to many others who themselves are connected to many others.
In our analysis, we used total degree centrality, which represents the total number of knowledge-sharing relationships as reported by respondents (Wasserman and Faust 1994). Note that we are not claiming this is an exact measure of an individual's actual (i.e., real-world) number of knowledge-sharing relationships. We believe total degree centrality is an underestimate of knowledge sharing relationships. For example, our data shows UCCE staff have on average 6.44 knowledge-sharing relationships with growers and PCAs have an average of 3.45. The actual number of relationships these outreach professionals have is likely larger. What is important in this analysis is not an individual's actual number of knowledge-sharing relationships but his or her relative degree of connectedness to other individuals in a knowledge network.
Figure 1 visualizes Lodi's knowledge network. Nodes represent individuals and ties represent knowledge-sharing relationships. Nodes are color coded: Green nodes represent individuals who are exclusively growers, aqua nodes represent individuals who are exclusively outreach professionals and blue nodes represent individuals who are both growers and outreach professionals (boundary-spanning professionals). Nodes are scaled by total degree centrality, with higher centrality represented by larger diameter nodes.
Fig. 1. Lodi's knowledge network. Nodes represent individuals and ties represent knowledge-sharing relationships. Green nodes represent individuals who are exclusively growers, aqua nodes represent individuals who are exclusively outreach professionals and blue nodes represent individuals who are both growers and outreach professionals (boundary-spanning professionals). Nodes are scaled by total degree centrality, with higher centrality represented by larger diameter nodes.
The figure yields cursory insight into which types of individuals are best positioned to access and spread knowledge. Nodes that have high centrality measures tend to be located close to the center of the network. Boundary-spanning professionals clearly tend toward the center. The patterning of those who are exclusively outreach professionals and exclusively practitioners is more difficult to discern. The Central Coast and Napa Valley knowledge networks were qualitatively similar to the Lodi knowledge network and expressed the same general patterns.
Table 3 reports the mean total degree centrality for the three types of individuals (growers, outreach professionals, boundary-spanning professionals) by region. On average across the regions, boundary-spanning professionals reported 5.51 knowledge-sharing relationships, which was 2.19 times more than growers and 3.65 times more than outreach professionals. By virtue of their relatively high number of knowledge-sharing relationships, coupled with their practical and expert training, these individuals with dual professions are likely some of the richest resources of viticulture knowledge. They are likely aware of other growers’ needs and challenges, are able to broker knowledge across the boundaries of science, industry and practice, and are well positioned to rapidly spread knowledge throughout the network.
TABLE 3. Mean total degree centrality of growers, outreach professionals and boundary-spanning professionals, by region
Outreach professional types, as groups (e.g., PCAs, farm advisors), have varying degrees of knowledge-sharing relationships relative to the number of individuals making up that group. Therefore, different outreach professional types have more or less potential coverage. Coverage is the average number of knowledge-sharing relationships of an outreach type (mean total degree centrality) multiplied by the total number of individuals within that type (n). Coverage represents the number of growers that a given outreach type, as a population, can potentially connect with.
Based on the measurement of coverage, we found a distinct set of outreach professional types who have high potential to efficiently access and spread knowledge throughout the networks. Across the three regions, the top outreach professionals in terms of coverage were for-hire vineyard managers (table 4). Vineyard managers are great in number and their relatively high centrality ranks them highest in terms of coverage. They engage in a broad scope of vineyard activities during the entire growing season and commonly do so for multiple vineyard operations. Consequently, vineyard managers are influential knowledge agents (individuals well positioned in the network to access and spread knowledge)
TABLE 4. Mean total degree centrality, population size and coverage of 12 types of outreach professionals, by region
PCAs, viticulture consultants, vintners and sales representatives round out the top five (table 4). These outreach professionals are involved in vineyard management through advising growers on fundamental vineyard activities such as pest control, nutrient management, equipment selection, and wine grape quality and yield enhancement practices. They too work with multiple growers. Vintners are a special case because individually they communicate with a relatively small number of gr