Progress report for GNE21-265
Three hypotheses on the resilience of local food supply serve as the point of departure for this research and frame the research objectives: Firstly, small farm establishments have greater product diversity and access to social capital which enable them to better respond to systemic shocks as compared to large-scale monocultures. Secondly, resilience is an interplay of proximity and redundancy. Proximity between actors in the food system suggests fewer disruptions compared to longer supply chains which increases resilience; but correspondingly such a system supports fewer actors, has fewer redundancies and results in overreliance on nodal actors, which lowers resilience. Thirdly, product diversity and spatial proximity create opportunities for local food to realize higher resilience by adopting socially cooperative, closed loop, and resource efficient practices – such as that advocated by the Circular Economy - which have the added benefit of lower carbon emissions.
Accordingly, this study states the following objectives:
Objective 1. Understand the nature of production and marketing shocks that vegetable farmers in Upstate New York have incurred due to the pandemic. Identify the forms of social capital that these farmers have access to and the ways in which this access influences their adaptive strategies.
Objective 2. Assess the implications of individual choices/adaptations on the functioning of the overall food supply chain. Measure the resulting performance of typical food supply networks relative to baseline levels before the pandemic. Suggest social and operational interventions predicted by the models to build resilience in cases of economic shock.
Objective 3. Evaluate the capacity for stakeholders in local food systems to adopt resource efficient, closed loop practices such as those advocated by the Circular Economy.
The purpose of this project is to understand how access to social capital influences the adaptation strategies and thereby economic resilience of producers in local and regional food networks during times of economic shock, using COVID-19 as an empirical case. A secondary goal is conceptualizing resilience as an outcome of resource optimization by evaluating potential for closed-loop production among farmers.
Theoretical and anecdotal rationales can be found for the project. Conventional global supply chains are efficient but emissions-heavy and vulnerable to perturbations (Garnett et al., 2020; IPES-Food, 2020; Pisch, 2020). Local supply, in contrast, typically has shorter value chains, labor-intensive operations, and is influenced by the social capital of its actors. This project hypothesizes that the unique features of local food chains, particularly the social capital embedded in them, create potential for economic and environmental resilience, ensuring continued food supply. There is some evidence for this. Preliminary media reports suggested that smallholder producers utilized social capital structures to pivot their operations during the pandemic: local farmers altered their product composition to account for reduced restaurant demand and increased direct-to-consumer demand; hired labor furloughed from the service sector; and responded to consumer demand for fresh food boxes (Robey, 2020), Community Supported Agriculture (CSA) (Mohs, 2020), and ecommerce markets (Reardon & Swinnen, 2020). How does social capital influence this adaptation? What are network-wide implications for the economic resilience of farmers, and food security of communities? These are insufficiently understood.
Key contributions of this research are as follows: (i) By recasting community solidarity and social capital as contributors to supply side resilience, this project aims to influence agriculture extension, policy, and scholarship on the social organization of agri-food structures, be it farmer cooperatives or marketing approaches; (ii) The networks-based approach imagines the local food system as composed of interrelated decision makers whose actions influence overall functioning, therefore moving the conversation from the role of individuals to that of broader farming communities.
This conceptualization of local food resilience contributes to the following knowledge areas: (i) addressing economic vulnerabilities of producers responds to food justice concerns which suggest that local food systems are playgrounds for protectionary politics, providing niche produce that is unaffordable to lower-income groups (Feagan et al., 2004; Hinrichs, 2003; Schmit et al., 2019), thereby alienating the communities they are embedded in; (ii) explores cooperative ownership of fragmented (agri-food) economic activity as an alternative to capitalistic monocultures that have reduced profit margins for small-scale agriculturists, and enabled production practices that contribute to environmental degradation (Meyfroidt et al., 2010; O’Kane, 2012) ; and (iii) empirical analysis of the potential for circularity and carbon efficiency by considering resource utilization in food systems.
We propose qualitative and quantitative methods to survey and interview farmers producing diversified vegetable crops in partnership with Cornell Cooperative Extension; evaluate econometrically the effects of social capital; use Social Network Analysis to model the operations of supply networks; and measure resilience under different scenarios. Keeping with the interdisciplinarity of regional science, we draw from economic geography, agricultural economics, sociology, and industrial ecology.
We utilize a mixed methods approach, comprising of two phases of data collection, followed by analysis, and communication of results. Phase 1 of data collection will respond to Objective 1. It will survey producers of vegetable crops to understand (i) adaptations to the shocks of the pandemic; and (ii) access to, and influence of social capital such as informal ties, their membership in farmer groups, direct and indirect connections to educational and financial resources, and their local consumer base. Data collected in Phase 2 will respond to Objectives 2 and 3. Here, vegetable supply networks in each of the three study counties will be modeled to understand how network structure and supply chain differences influence resilience. In this section, we first discuss the three study areas, commodities modeled, and then describe the methodological approach we use for each objective.
Study Area: For this study, I partner with Cornell Cooperative Extension to collect data from vegetable farmers and associated agri-food stakeholders in three counties (in different agricultural districts) of Upstate New York – Broome County (Southern District), Madison County (Central District), and Washington County (Eastern District). We chose these counties by accounting for both research goals and practical considerations. With the implementation challenges brought about by the pandemic, it seemed imperative to partner with organizations like CCE which have established connections with the local farmers and a strong understanding of community needs. We prioritized counties where extension specialists were willing to assist us with implementation of this project. Further, these counties have agri-food industries that are dominated by smallholder farmers, regional specializations, expanding processing capacities, and community groups, yet exhibit different approaches and trajectories to local food development.
Commodities of interest: Our analysis focuses on diversified vegetable crops since their weight and perishability make them very typical of local food systems. Perishability of produce also influences the supply chain timeline, an important factor in modeling food distribution. Yet another consideration was that farmers vary in their approach to vegetable crops – where some have large acreage dedicated to a single vegetable, others may
Objective 1: Understanding adaptive strategies in relation to social capital.
Data Collection: We have provided a draft survey instrument that will undergo iterative refinement and condensation prior to dissemination. have a more diversified production - which enables us to explore the effects of product diversity as detailed in the research hypotheses.
This survey is divided into four sections as follows: (i) Baseline farm operations prior to the pandemic; (ii) Production and marketing adaptations since the pandemic hit; (ii) Questions to assess access to social capital, their different forms, and the community and organizational structures through which they are conveyed; (iv) Demographic questions and questions to evaluate interest in a follow-up interview.
Variables relevant to the research objectives are described here. Dependent variables that capture different aspects of economic resilience are included in Parts 1 and 2 of the survey document. These include farmer-reported impact on farm operations, shift in overall demand for products, shift in sales at different venues, and differential access to educational and financial resources. Since we hypothesize that social capital influences this adaptation, our explanatory variables include various social capital indicators requested in Part 3 of the survey. These have been classified on the backend depending on the forms of social capital they represent. Demographic variables and other indicators such as farm size will be used as control variables.
Data Analysis: Standard statistical and econometric techniques will be used to extract the influence of social capital on resilience. Depending on the number of social capital indicators that we retain in the final survey, we might reduce dimensionality by using Principal Component Analysis (PCA) to identify the main variables contributing to resilience. Regressions will then be used to quantify the effects of social capital. We also expect that the survey will yield interesting qualitative results that can be explored in detail with farmers who are willing to participate in a follow-up interview.
Objective 2: Measuring food supply network resilience.
The overlap of social and structural factors and interdependence between agents in local food supply chains make Social Network Analysis (SNA) a compelling methodological tool.
Data Collection: To respond to this objective, the expanded supply chain of diverse vegetable growers will be traced using an egocentric network. A brief description of egocentric networks is included here, following the exposition of Perry, Pescosolido, and Borgatti (Perry et al., 2018). Egocentric networks construct the social network from the point of view of individuals (egos) and their immediate social connections (alters) who satisfy a minimum threshold requirement for inclusion. The researcher recruits links through an egocentric snowball sampling approach wherein starting actors are asked to identify their main social connections. Each of these identified actors is in turn asked to do the same until the network is sufficiently developed or saturated. Egocentric networks offer certain key advantages: they do not require complete knowledge of the network which can otherwise prove cumbersome to trace, and also permit sampling of egos leading to greater generalizability of results. These benefits have led researchers to advocate for the method in supply chain studies (Borgatti & Li, 2009; Wichmann & Kaufmann, 2016), and is the approach we take in Phase 2 of data collection.
In Phase 2, producers of diversified vegetable crops - identified with the help of extension personnel using a convenience sample - will form the initial egos from whom the rest of the vegetable supply network will be traced. They will be asked to identify key upstream and downstream links within the expanded supply chain starting from the sourcing of raw materials to and ending with the sales of produce. Semi-structured interviews of each of these agents will be conducted to understand (i) their production and marketing operations; (ii) strength of social ties with other farmers and supply chain actors; (iii) resource use (further detail under Objective 3). Information on their operations and strength of ties will be used to develop a comprehensive network structure that we then evaluate for resilience and simulate for the effects of different supply chain situations. Patterns of resource use will be used to address Objective 3 in the following subsection.
Data Analysis: The three hypotheses on local food supply chains discussed under Project Objectives and its implications for network resilience can be tested using measures of network performance and structural integrity. One such algorithm advanced by Newman and Girvan to detect community structures systematically removes network links with high edge betweenness scores, that is, those links that have the highest density of shortest paths and therefore contribute the most to network functioning. The structural integrity and network performance is then measured, betweenness recalibrated, and the process repeated (Newman & Girvan, 2004). This study will adapt this approach to assesses the contribution of different supply chain links to overall functioning of the supply network by iteratively removing links in order of importance. The resulting network performance as a percentage of initial performance serves as the outcome variable which is a direct measure of supply chain resilience. Comparison of sub-chains within the network and cross-sectional comparisons across the different ego-networks generated can indicate the influence of supply chain length and redundancies within the network. More realistic functioning of typical food supply networks, that is, those which account for the peculiarities of time-sensitive distribution, or first-in-first-out inventory management for distributors, can be modeled by introducing memory into the constructed network (Rosvall et al., 2014).
Objective 3: Evaluating capacity for increased resource efficiency and closed loop production.
Data Collection: The network structure for this objective will essentially be the same as in the previous cases but populated with additional variables on resource use gathered during the data collection phase, that is, data requirements for this objective are achieved through the interviews described previously. More specifically, when we use the term ‘resource use’, we are referring to the following aspects for each stakeholder: upstream sourcing of materials and downstream distribution strategies before and during the pandemic; quantities of goods that follow different value streams; labor requirements for each stage of operations; transportation and other logistics.
Data Analysis: The baseline networks model derived from the interviews will be evaluated as a compartmental flow models to identify potential for optimizing value streams, opportunities for inter-agent linkages, waste streams and unused capacity, and thereby the potential for CE in food systems. Compartmental flow models essentially examine the rates of flows between compartments (or nodes) in the network and can therefore be used to understand the network's potential for improved efficiency. Potential analyses that we can conduct include identifying saturated nodes, underutilized capacity, or simulating the effects of new linkages between nodes. New linkages between nodes, for say recycling of resources or linking value streams through social cooperation, measures additional resource efficiency that can be developed in the local food supply chain. This latter measure, also relative to the baseline, is a quantitative indicator of the potential for the Circular Economy.
Network simulations conducted for Objectives 2 and 3 speak to the interdependencies in the local food supply chain and therefore community-wide measures of resilience. Education and policy responses might include forging community connections that modify the ways in which actors interact with each other in the real world, thereby improving the resilience of the local food supply chain.
Education & Outreach Activities and Participation Summary
This project will pursue a three-pronged approach to outreach that (i) targets farmers directly, (ii) targets farmers indirectly through Community Educators and other Extension personnel, and (iii) speaks to non-academic and academic audiences invested in agri-food systems.
Direct outreach to farmers: The survey instrument used to fulfill Objective I will include an option for participating farmers to provide their contact details if they wish to receive a copy of the survey results. Similarly, farmers and other supply chain actors who are interviewed for Objectives II and III will be sent a summary report with research findings. The aim is to directly convey the outcomes to participants and provide them the opportunity to maintain an ongoing conversation.
Although the study focuses on vegetable producers in Upstate New York, the research is generalizable and can translate well to other locations and commodities. I aim to publish 2-4 blog articles and illustrative graphics for dissemination through newsletters and mailing lists run by Cornell Cooperative Extension, farmers groups, food hubs, and other agri-food resource groups in both the Northeast and US overall.
Indirect outreach to farmers through Extension officials: A benefit of partnering with Cornell Cooperative Extension is access to their prodigious network of offices throughout New York State, which enables positive findings to be incorporated into farmer education and training programs. This can be achieved both within the three counties of the immediate study area, as well as other counties they are established in. Conversations with extension personnel suggest that social capital is indicated in several of their activities and findings will therefore be of interest to them in their other ongoing research and training projects. We envision playing a supporting role to their main activities here, that is, by providing need-based illustrative and summary content (in the form of brochures or posters) for educational purposes, and academic content that field experts convert to extension activities.
Written outputs for non-academic audiences: Since food sourcing is of as much interest to consumers as it is to farmers, the above content generated for the local agricultural audience will also be tailored to journalistic, or opinion pieces written for general, non-academic audiences of popular media outlets. The goal here is to convey the relevance of findings to stakeholders throughout the food consumption value chain and outline the role that individuals can play in creating sustainable food systems.
Written outputs for academic audiences: This dissertation will generate 3-5 journal articles, each of which tentatively corresponds to one of the research questions posed above. These will address academic audiences and contribute to ongoing scholarship in the fields of local and regional food systems, food security, supply chains, and community resilience, as discussed in other parts of this proposal. Work-in-progress will also be presented at various academic conferences in the areas of regional science, planning, and food systems, which will communicate with the larger social science research community. Some of these materials may be generated after the project end date.
The process of refining the survey instrument for dissemination generated many interesting qualitative insights on the ways in which the pandemic has changed the marketing options for growers. Moreover, it has changed the ways in which farmers meet and organize be it for the purposes of education or coordination, which in turn affects the ways in which research is conducted. For instance, a lot of the additional information that could be gained by attending a growers' meeting is now lost in the virtual environment which enhances the need for a more directed approach to conducting the research.