Fig 1.
This map was created by the authors using publicly available shapefiles from the Homeland Infrastructure Foundation-Level Data (HIFLD) database.
Table 1.
Number of organizations interviewed (per organization type).
Table 2.
Description of network property measures and node’s centrality measures in SNA [53, 69–74].
In the equations, v represents each vertex (node), e represents each edge. For Network diameter, a(s, t) denotes the number of edges in the shortest path from a node s to a node t. For degree centrality, i is the transmitter node of e, j is the receiving node of e. For closeness centrality, d(v, t) is the geodesic distance between any node v and t (i.e. the sum of the edges on the shortest path). For betweenness centrality, gst represents the number of geodesics (or shortest paths) connecting s to any other node t in the network, and gst(v) denotes the number of shortest paths from s to t that some node v lies on. For clustering coefficient, nv is the number of alters (neighbours) of v, and mv is the number of alter-to-alter edges in the neighbourhood of v.
Table 3.
Actors categorization and related colour code.
Table 4.
Graph properties of the socio-centric network calculated on Gephi.
Fig 2.
a = out-degree, b = in-degree, c = betweenness centrality, d = closeness centrality, e = clustering coefficient; i = SNA graphs, ii = bar graphs showing the top eight centrality measure scores (Y-axis) of nodes (X-axis); the color code reflects the actors type classification.
Fig 3.
Nodes’ color code relate to the actors’ type classification; a = all BMP-messages; b = forested and grass riparian buffers messages; c = no-till and cover-crops messages; d = manure management plan messages. Node sizes are relative to the node’s degree, and edge colors correspond to the source (i.e. the actor emitting the message). Nodes 58, 59, 60, 61, 62, 63 = small-scale farms; node 64 = medium-scale farm node 65 and 66 = large-scale farms (CAFOs).
Fig 4.
Messages distribution per BMP (X-axis) and per message kind, after merging of duplicates.
Fig 5.
Messages distribution per BMP (includes only the messages received directly by farmers).
X-axis: message strength based on influence-rank assigned by farmers (1 = weak, 4 = strong); Y-axis: messages count; legend: message source (emitter of the information); ‘For-profit (animal)’: animal specialist, ‘*’: farmers-led organizations. Note: no messages were recorded for BMP nr 8 ‘Hedgerows plantation’.
Fig 6.
Relative importance of the emerging themes from the interviews content analysis.
Each theme refers to a cluster of codes, the frequency of which were computed to establish the relative importance of the theme (%).
Table 5.
Maximum frequencies of observations from interviews content analysis (Fq: frequency, Int: interview, edu.: education).