The advanced state of land degradation worldwide urges the large-scale adoption of sustainable land management (SLM). Social learning is considered an important precondition for the adoption of innovative and contextualized SLM. Involving farmers and researchers in participatory monitoring and evaluation (PM&E) of innovative SLM such as regenerative agriculture is expected to enable social learning. Although there is a growing body of literature asserting the achievement of social learning through participatory processes, social learning has been loosely defined, sparsely assessed, and only partially covered when measured. Here, we assess how PM&E of regenerative agriculture, involving local farmers and researchers in southeast Spain, enabled social learning, effectively increasing knowledge exchange and shared understanding of regenerative agriculture effects among participating farmers. We measured whether social learning occurred by covering its social-cognitive (perceptions) and social-relational (social networks) dimensions, and discussed the potential of PM&E to foster SLM adoption and out-scaling. We used fuzzy cognitive mapping and social network analysis as graphical semiquantitative methods to assess changes in farmers¿ perceptions and shared fluxes of information on regenerative agriculture over approximately three years. Our results show that PM&E enabled social learning among participating farmers, who strengthened and enlarged their social networks for information sharing and presented a more complex and broader shared understanding of regenerative agriculture effects and benefits than pre PM&E. We argue that PM&E thereby creates crucial preconditions for SLM adoption and out-scaling. Our findings are relevant for the design of PM&E processes, living labs, and landscape restoration initiatives that aim to support farmers¿ adoption and out-scaling of innovative and contextualized SLM. This work was supported by “la Caixa” Foundation (ID100010434) through a PhD fellowship to RLS (LCF/BQ/ES17/11600008), and by the projects DECADE (Seneca Foundation, 20917/PI/18), XTREME (Ministry of Science and Innovation PID2019-109381RB-I00/AEI/10.13039/501100011033), and COASTAL (EU H2020 grant agreement 773782). For the Portuguese coauthors, this work was partially funded by National Funds through FCT - Foundation for Science and Technology under the Project UIDB/05183/2020