Innovation Trajectories in Honduras’ Coffee Value Chain. Public and Private Influence on the Use of New Knowledge and Technology among Coffee Growers



Frank Hartwich et al. / Int. J. Food System Dynamics 3 (2010) 237-251

Comparing the three networks we find that they differ both in terms of the type and number of agents
that facilitate the exchange of innovation-relevant information as well as the intensity with which this
information is exchanged. Table 3 compares some important properties of the three networks. The
density of the network in El Pacon, meaning the number of relationships divided by the number of
possible relationships, is significantly lower than in the two other networks. Average geodesic distance,
the shortest path from any given actor to another, is very similar between the networks: It only needs two
steps to pass from any given actor to another. Mean in-degree centrality, meaning the average number of
relationships actors maintain allowing them to absorb innovation-relevant information, is comparatively
low in El Pacon. The mean betweenness centrality, meaning the average of the number of times that
actors are located on the shortest path between any two actors in the network, again is comparatively
low in El Pacon. Mean eigenvector centrality, roughly describing the average distance of an actor to all
others in the network, is also lower in the El Pacon network. In conclusion we can say that the innovation-
relevant information exchange network in El Pacon is characterized by intensive interactions, it is more
difficult for actors to pass or receive information to and from others.

Table 3.

Common properties of information exchange networks

Network Property

El Pacon

Las

Crucitas

San
Marcos

Number of producers

25

28

25

Number of agents

15

6

6

Density

0.070

0.195

0.229

Average geodesic distance

2.089

2.095

1.959

Mean in-degree cenrality (normalized)

6.969

19.519

22.581

Mean betweenness cenrality (normalized)

0.726

2.827

2.243

Mean Eigenvector centrality (normalized)

16.393

22.616

23.906

4.5 Agents’ Sources of Information

We also collected data on how main agents engaged in the development and diffusion of knowledge and
technology in coffee production and in particularly which are their sources of information. However, the
picture is biased towards those agents that operate in the three communities studied, because we started
from these communities and, following the snowballing principle, only organizations named here were
then further contacted and interviewed with regard to their relationships and sources of information.
Further, we could only track back the source of information up till the national border; actors outside of
Honduras were not interviewed.

The resulting agent-to-agent network, a type of ego-network from the view of the three communities, is
depicted in Figure 4. Development agencies are depicted in red, buyers in grey, exporters in blue,
certifiers in pink and input providers in black. The arrow points at the actor that has been identified as a
source of information. The figure confirms the central role of IHCAFE, both its technical assistance as well
as it research branch, as a source of innovation-relevant information in the sector. Figure 4 is illustrating
the high degree of interweavement of public and development agencies and private sector agents. What
cannot be shown in Figure 4 is what agents considered being the ultimate source of information: While
some agents found IHCAFE to be the best source of knowledge and technology others also pointed to the
Certification Agencies and ultimately at CATIE’s research department, the former PROMECAFE research
unit (now taken over by CIRAD and ECOM Coffee Group), and GTZ. For the case of processing machinery
some input providers also mentioned the important role of devices developed in Brazil and Colombia.

248



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