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It is well established that a person's productivity is strongly related to interactions among their network cycles. Despite this, much of the literature concerning farmers' productivity describes only their formal relationships with extension officials, ignoring their personal relationships. This study was motivated by the need to understand how farmers can use their social networks to promote information diffusion and learning among themselves. The study assessed whether farmers' information networks can positively influence their productivity and performance through the study of their network structure. We used survey data from 200 Ghanaian farmers. The data was then analyzed using descriptive statistics and an exponential random graph model (ERGM). The results of this study showed that farmers' information networks had a positive effect on their performance and productivity. The study also revealed that farmers seek information from key and influential farmers while key farmers also seek information from other key farmers. A further finding of the study showed that many factors contributed to the successful diffusion of information, such as popularity, activity spread, reciprocity, multiple connections, and homophily of experience. However, on the other hand, transitivity was not significant in this network. Hence, this study clarifies the effect of the structural characteristics of farmers' information network which influence information diffusion for higher performance and productivity. This study argues that agricultural managers should take measures to ensure that farmers understand how their information network can benefit their peers, as well as their own performance and productivity. [ABSTRACT FROM AUTHOR]

• Theoretical framework measuring and evaluating participatory policy structures. • Deriving separate network based measures for lobbying vs. informational influence. • Apply MCMC simulations to draw statistical inferences on influence measures. • Test for different influence mechanisms applied by specific types of stakeholders. A policy network approach has been developed to assess participatory policy processes in Ghana, Senegal, and Uganda. Theoretically, political influence measures correspond to two different influence mechanisms: vote-buying and informational lobbying. For each mechanism, separate measures were defined and empirically derived from corresponding policy networks. Exponential random graph models were used to assess the network-generating process, which allows for analysis of the robustness of the derived influence measures and the determinants of network structure. The results imply that participatory policy processes are dominated by donor and research organizations, while farm interests and civil society organizations have only a negligible influence. The design of stakeholder systems appears to be difficult since the observed network structures are mainly determined by relational factors that result from organizations' social networking activities or other organizational attributes, such as reputation power, which cannot be easily controlled. Institutional factors such as membership in committees have only a minor impact. [ABSTRACT FROM AUTHOR]