In recent years, farmers in Beja, an agricultural governorate in northwestern Tunisia, have expressed their willingness to use urban sewage sludge as agricultural fertilizer, especially with the unavailability of chemical fertilizers and the soil type of the region that is poor in organic matter. However, there is an imbalance between the important farmers’ demand versus the limited quantity of sludge produced by the Beja wastewater treatment plants (WWTPs). In the face of this, this study aims to identify the problems related to the agricultural reuse of sludge in Beja and propose solutions to solve them. The quality of the sludge produced by the five Beja WWTPs was assessed based on physicochemical and microbiological parameters. The data were collected using the Delphi method, with 15 experts representing different positions on the issue treated. The SWOT-AHP methodology was used to define the strategies promoting the sustainable use and management of urban sewage sludge for sustainable agricultural development in Beja. Results showed that there were no problems with compliance with the Tunisian standards NT 106.20 for the sludge produced. A set of twelve practical conclusions was identified, constituting the strategies of Strengths–Opportunities, Strengths–Threats, Weaknesses–Opportunities, and Weaknesses–Threats deduced from the SWOT-AHP.

Climate change poses major challenges to Mediterranean dairy farming, where adaptation depends largely on peer-to-peer information exchange among farmers. Yet the structural conditions governing climate adaptation information diffusion remain poorly understood in North African livestock systems. We analyzed the climate adaptation information network of 239 actors reconstructed from interviews with 60 dairy farmers in the Bizerte Governorate, northern Tunisia, using a dual analytical framework that distinguishes nomination asymmetry/ (directed metrics) from social proximity (undirected metrics and bidirectional diffusion analysis). The network was extremely sparse (density = 0.00482), highly unequal in information access (in-degree Gini = 0.797), and strongly geographically segmented. Although the degree distribution was heavy-tailed, formal goodness-of-fit testing did not support a scale-free model. Community detection and four convergent geographic association statistics confirmed that adaptation knowledge is largely confined within administrative zones (81.4% within-zone ties; Cramér’s V = 0.729; p < 0.001). The dual framework revealed only 30% overlap between structural bridge and nomination intermediary rankings. Broker-targeted seeding reached 53.6% of the network versus 31.2% for random seeds ( p < 0.001), confirmed by cascade simulations. Brokerage was unrelated to socio-demographic characteristics but associated with greater adaptation information exposure and lower digital dissemination capacity — a structural double bind. Our findings show that the principal obstacle to climate adaptation diffusion is not information scarcity but structural lock-in arising from sparse cross-community connectivity, geographic fragmentation, and a shortage of boundary-spanning actors. We recommend identifying structural brokers through network mapping, equipping them with communication infrastructure, and replacing demographic targeting with network-informed extension strategies.