Food insecurity and malnutrition are related; major global concerns are embedded in many UN sustainable development goals. Food insecurity predicts all forms of malnutrition including stunting, wasting, micronutrient deficiencies, and overweight and obesity. Hunger, measured by prevalence of undernutrition, is a key indicator of food insecurity. All those can be addressed and in achieving food and nutritional security, we need cooperation and correlation between people because, essentially, people run the food system.
Relational methodologies such as complex systems and social network analysis (SNA) in particular provide a suitable framework to understand the multidimensional nature of food security and nutrition (FSN) as well as the interplay of actors. So, how SNA can be helpful in mapping FSN network?
Different levels of SNA can be helpful in mapping FSN.
Micro-level Measures
One of the main objectives of SNA is to assess node centrality. Finding out which is the most central node in FSNS analysis is important as it could constitute the channel through which for example information on food prices or agricultural prices or good agricultural practices could be disseminated faster in the relevant network. In principle, actors who have more ties may have multiple alternative ways and resources to reach goals and thus be relatively advantaged.
The micro level focuses on the relational and socio-economic characteristics of the actors and nodes within the network. Network actors can be defined as economic agents directly involved in the FSNS such as households, food dealers, farmers, food processors, farmers’ associations, trade unions, public administration, NGOs, service providers to FSNS, hospitals, schools; while nodes can be defined to include also other territorial units such as police offices, roads, railway, airways, waterways and water network, and electricity network.
Meso-level measures
These measures allow researchers to understand the structure and components of the network and to identify groups, clusters, and locations that may constitute bottlenecks to or opportunities for inclusive and efficient food systems. For example, it can inform on the monopolistic situations of some actors (producers, processors, retailers, etc.), exclusion of communities (religious groups, ethnic groups, etc.). The number, size, and connections among the subgroupings in a network can tell us a lot about the likely behaviour of the network as a whole.
This level of analysis can be used, for instance, to identify the presence of hidden communities, or those communities within which connections are dense, but between which connections are sparser. In other words, they identify more cohesive groups of actors. As opposed to hidden communities, explicit communities are those groups that share same attributes such as, their belonging to a same ethnic group or their adopting the same agricultural practices or accessing a same water source.
Macro-level measures
These measures differentiate food sheds on the basis of their overall configurations. The overall configuration of the network defines the inherent form of coordination in the food system. For instance, to be part of a cohesive network can result to be a trap due to the limited inflow of new information. On the contrary, the small world network characterized by a number of cohesive sub-groups linked one another by random ties is generally considered to be more conductive to innovation.
For example, a group of farmers belonging to the same cohesive local social group share the same knowledge and opportunities, whereas farmers with connections to other groups of farmers are in a better position to have access to new knowledge and a wider range of opportunities.
The underpinning assumption of the proposed application of social network analysis (SNA) to Food Security and Nutrition Systems (FSNS) is that the performance of a food system in terms of inclusiveness and healthy diets depends on the collective behaviour of individuals. SNA reveals some fundamental insights about the influence that individual actors’ behaviour has on the patterns and properties of the networks (e.g., food systems networks) and vice versa about the influence of networks structures on individual behaviour. By investigating into the multiple relationship patterns between socio-economic actors and the physical space within which actors operate, it also helps to understand the circumstances that affect actors’ behaviours.
Moreover, SNA as an analytical tool helps to measure the socio-economic context and interaction in the food and nutritional system rather than just following the financial aspects of the system.
