Successful insecticide (IRM) and herbicide (HRM) resistance management (RM) in agriculture is contingent on the aggregation of insect and weed best management practices (BMPs) on the farm-scale to the landscape level. Endeavors such as this require significant coordination and collaboration across diverse sectoral boundaries in agricultural production, which invoke social, economic, and political forces to structure the incentives, norms, and rules regarding RM. Currently, these structures are largely designed to serve diverse farm-scale management practices, not landscape level. Therefore, research is needed to investigate alternative solutions that re-organize these structures towards the success of implementing landscape level BMPs.
One pathway to alternative solutions is through cooperative management strategies, where multi-stakeholder collaborations within an agricultural production system work together to devise best management practices, and share costs, benefits, and knowledge across diverse institutional boundaries. Critical to the organization of these systems are social conditions like trust, reciprocity, and social capital that mediate the social ties that promote exchanges of information, knowledge, and resources related to IRM and HRM. To study these conditions, I endeavor to pursue an exploratory, mixed-methods network study of Eastern North Carolina “Blacklands” agriculture. Using both qualitative interviews and social network analysis methods, I can uncover how social conditions structure and influence information and knowledge exchanges, and how they may be leveraged to enhance coordination and collaboration, and potentially lay some groundwork for the development of future cooperative management programs.
Objective 1: Investigate the diversity of farmer’s best management practices for insect pests and weeds, and how concretely these connect to shared practices in IRM and HRM.
RQ1: What role does personal knowledge, values, and identity play in how farmers make sense of resistance management?? To what degree are these knowledges and perspectives shared with other farmers?
- What are their “best management practices”?
- What are their biggest challenges?
- What are they concerned about?
One of the key questions for assessing a management system at the landscape level is understanding how farmers are implementing BMPs for insect and weed management at the farm scale. What are some common features of these management practices? How do they relate to individual farmer knowledges, perspectives, and values? Are any of these elements shared? Approaches to farm-scale management will be contingent on individual farmer perspectives and knowledges in addition to resource availability, which may result in very diverse practice. The challenge of aggregation to landscape level coordination is synthesizing this diversity of practice into a landscape level approach. Additionally, a farmer’s unique knowledge and perspectives on IRM and HRM and the degree to which it is incorporated into their own insect pest and weed management practices has implications for promoting cohesive cooperative management. How a farmer recognizes and understands their role in resistance management may differ from other farmers, and may or may not be anchored in similar values, perspective, or knowledge. Interviewing farmers about their conceptual perspectives, local knowledge, and values to understand the diversity of practices currently on the ground is therefore foundational.
Objective 2: Map out network connections between who farmers communicate with, share knowledge and collaborate with on different aspects of resistance management.
RQ 3: What role do personal networks play in how they understand, make decisions, and structure their practices related to resistance management?
- Who do farmers talk to about IRM and HRM? Who are the most trusted sources?
- What influence do different sources have on resistance management related decision making?
Each farmer will communicate their own unique management plans tailored to their specific farm characteristics and resource capacities, in addition to their personal knowledges that they use to facilitate said plans. However, farmers also heavily rely on information and knowledge from other actors when designing their management plans and making decisions concerning IRM and HRM. Who are these most trusted sources, and why do farmers trust them? Additionally, because there is a collective interest for all farmers to engage in effective insect pest and weed management practices, are their informal collaborations between entities that already exist? Who is involved and how are they organized? Mapping out social ties based on information and knowledge transfer, and management collaboration can illuminate important social factors that could be leveraged to enhance the potential for coordination in implementing landscape-level BMPs.
I will use a mixed methodological approach called qualitative network analysis (QNA) method to accomplish objectives 1 and 2 simultaneously. This approach incorporates both qualitative and quantitative methods which takes place through a protocol designed to collect “egocentric” network data alongside semi-structured interview data1. Quantitative, egocentric data gathers information on social network connections between farmers and other actors, while the qualitative interview data gathers information on famer’s personal meaning-making processes. The advantage of utilizing this approach is too capture both a respondent’s set of social ties and concretely understand how those social ties influence, and are influenced by, the perspectives, values, and practices of the individual. In network analysis terms, this means that QNA can work to understand the multi-level linkages that exist between network structural patterns, network actors personal networks, and the personal meanings actors attach to their network connections instead of just one of these levels 6,11.
Farmers are the chief decision makers and facilitators of IRM and HRM practices, making them the key stakeholders involved in any cooperative management system. A farmer-centered, and farmer-empowered collaborative approach can result in the widespread realization of BMPs tailored to a local context’s social conditions in addition to the biophysical3. Therefore, inquiries into how social networks can be leveraged to develop landscape level coordination of resistance management should take farmer-centered approach to the investigation of perspectives, practices, and social connections. Therefore, my primary study sample will consist of local Eastern North Carolina farmers. It is important to note that this is not a comprehensive, whole-network study, but rather an exploratory “landscape analysis”. Time and resource constraints inhibit me from performing a comprehensive network study, but an exploratory study can provide meaningful insights if sampling is done thoughtfully.
Farmers will be selected based on their involved with 3 major commodity crops, which are some of the main crops grown in these communities: corn, cotton, and soybean. The metric for high-impact qualitative work usually hovers around an n=30 of interviewees, therefore I will endeavor to recruit 30 farmers. Lastly, because I am interest in uncovering shared perspectives and network connections, it makes sense to limit the geographic area of sampling. I will select three interconnecting blacklands counties – Hyde, Tyrell, and Washington, – because of their geographic proximity to one another, but also because I have previously established contacts in the area that might make sampling more efficient.
I addition, I will also recruit a small number of extension agents and researchers that focus on HRM and IRM to collect data from. The inclusion of this minor sample is to provide a broader view of the larger knowledge networks that exist around resistance management practices, which I will draw from to contextualize farmer’s own personal networks, and the influence that other actors have in these networks.
Specific Methods and Research Questions (Details about Recruitment and Data collection logistics in “Timetable” section)
Objective 1: I will employ semi-structured interview methodology to elucidate details about farmer’s individual resistance management practices. Questions in these interviews will ask about various aspects of farmer perspectives, knowledges, and values related to insect pest and weed resistance, which will later be used to understand how farmers construct their management plans from their own meaning-making practices. Interview methodology has a long history in social science, specifically the fields of anthropology and sociology, and has been used to complement network studies in past research11. It is not a method that is traditionally applied to the topic of resistance management, which is a novel element to this approach.
Objective 2: The way that this QNA method works is through an interview methodology known as concentric circle methodology (CCM). It is utilized as an interactive tool that allows the survey respondent to actively construct a mental model of their own social network in front of the researcher1. Interactivity and active construction will produce a “subjective network map” that functions as the “ego-network” for each individual participant. The researcher can use these maps to detect any patterns between farmer’s mental models, and whether there are any influential actors that exist across their ego networks 9. Influence of certain actors in these embedded networks should become apparent if multiple individual farmers describe social ties to the same knowledge source, and significant patterns that are apparent across different mental models can suggest insights into how farmers think about resistance management. Other interview questions pertaining to the nature of relationships between farmers and their contacts can also be asked through this active construction technique, and can be positioned appropriately in terms of that farmers mental model. In this way, the subjective network maps function as an interview tool as well as a data collection output, and can address information and knowledge exchanges simultaneously by mapping different elements of social ties1.
Objective 1: Qualitative interview data will be analyzed using abductive analysis coding techniques12. This is different than traditional grounded theory techniques that seek to establish sentence-by-sentence meaning making patterns within interviews, and instead seeks out broader thematic meanings from more aggregated data. This is an important methodological choice, because preforming grounded theory takes an exponentially longer time table to accomplish, and treats each interview subject as their own unique extended case which involves a substantial amount of qualitative data. Specifically, my coding scheme will pull out the important perspectives, knowledges, and values communicated by farmers, and analyze these data to detect elements of shared experience and values, which is important for future possibilities of coordinating shared practice.
Objective 2: Using standard social network analysis software, UCINET, I will construct the personal knowledge networks from the collected ego network data for each farmer in the study. I will map each social tie to each actor that interacts with farmers in a resistance management context to first understand who farmers communicate and exchange knowledge with in a resistance management context, and then aggregate all the ego-networks of every respondent and there social ties in order to uncover shared social connections, and broader patterns of trust and influence across the personal networks 9. Relating this back to the perspectives of farmers expressed in interviews, I will look for the social ties that are most highly correlated to trust, influence, and practice. It is important to assess the ego-networks in conversation with the interview data, because there could be actors that are a) are very well connected to farmers, but exert little influence on their practices via knowledge exchange and b) actors who are not well connected to farmers, but exert a lot of influence on practice via knowledge exchanges at key points in the network. A comparison like this should provide insights on how missing important details about influence and farmer practices, and how these network connections can be leveraged for greater collaboration on the basis of different factors relating to influence and trust.
Results expected in Fall 2021; Discussion in Spring 2022.
Educational & Outreach Activities
- Consultations ( 10 Expected)
- Over the course of research, I plan on consulting with 5 extension agents and 5 extension/university researchers on resistance management norms, practices, and network science.
- Educational Tools (1 Expected)
- Research will culminate in a comprehensive project report that will provide recommendations for the social coordination of resistance management practices across different farming systems in Eastern North Carolina. The report will not only act as an informational resource for growers, but also a toolkit for conducting further research and outreach activities through extension.
- Journal Articles ( 3 Expected)
- Publication on social networks and resistance management in agricultural systems in agricultural science/social science journal (e.g. Agricultural Systems ; Journal of Agriculture and Social Research ; Agriculture and Human Values)
- Publication on relationship between knowledge, identity, and practice in AgBiotech stewardship (e.g. Science, Technology, and Human Values ; Social Studies of Science ; Engaging Science, Technology, and Society)
- Publication on methodological approach in social science journal (e.g. Journal of Mixed Methods ; Qualitative Sociology ; Journal of Rural Sociology)
- Webinars, Talks, and Presentations (5 Expected)
- Presentation at Academic Conferences (e.g. Society for the Social Studies of Science)
- Presentations at NC State (e.g. Genetic Engineering and Society Center; Graduate Student Research Symposium)
- Talks through NC State Extension at Growers meetings in Spring 2022
- Webinar/Lecture on using Social Network Analysis in Agricultural Systems
Estimated Participation: 10 Agricultural Professionals, Farmers
- Farmer participation is estimated based on planned future education talks given at 2-3 NC State extension grower meetings. In the researcher’s experience, participation at these meetings can very between 15 – 30 farmers.