Holistic Modeling Approaches for Supporting Sustainability Transitions

Agricultural and environmental decisions in agroecological systems are made under profound uncertainty, with complex trade-offs between productivity, ecological integrity and human well-being. This cumulative habilitation thesis brings together 32 peer-reviewed studies that develop and apply holistic modeling approaches to support such decisions and, more broadly, sustainability transitions. The first part advances decision analytical methods in agroecology. Structured decision models, Bayesian networks and Monte Carlo simulations, informed by expert-elicited probabilities, are used to evaluate interventions under incomplete information. Applications span agroforestry adoption and carbon offset schemes, horticultural management strategies, water resource management, flood-based farming systems, agricultural policy and household nutrition, and climate change adaptation and mitigation, with case studies in Africa, Asia, Latin America and Europe. These models identify key trade-offs, risks and leverage points, and value-of-information analyses show where further measurements would most improve decisions. The second part advances methods in transdisciplinary research. It integrates participatory approaches, stakeholder engagement, local and Indigenous knowledge, and holistic sustainability assessment frameworks to co-create context-specific solutions and to bridge science, policy and practice. Together, the studies show that combining probabilistic modeling with participatory processes produces decision support that is both scientifically rigorous and practically relevant. The thesis concludes with implications for policy and with future research directions, including hybrid models that combine expert knowledge with emerging data, multi-criteria trade-off assessment, and the scaling of agroecological practices.