Drylands are one of the most diverse yet highly vulnerable social–ecological systems on Earth. Water scarcity has contributed to high levels of heterogeneity, variability and unpredictability, which together have shaped the long coadaptative process of coupling humans and nature. Land degradation and desertification in drylands are some of the largest and most far-reaching global environmental and social change problems, and thus are a daunting challenge for science and society. In this study, we merged the Drylands Development Paradigm, Holling's adaptive cycle metaphor and resilience theory to assess the challenges and opportunities for livelihood development in the Amapola dryland social–ecological system (DSES), a small isolated village in the semi-arid region of Mexico. After 450 years of local social–ecological evolution, external drivers (neoliberal policies, change in land reform legislation) have become the most dominant force in livelihood development, at the cost of loss of natural and cultural capital and an increasingly dysfunctional landscape. Local DSESs have become increasingly coupled to dynamic larger-scale drivers. Hence, cross-scale connectedness feeds back on and transforms local self-sustaining subsistence farming conditions, causing loss of livelihood resilience and diversification in a globally changing world. Effective efforts to combat desertification and improve livelihood security in DSESs need to consider their cyclical rhythms. Hence, we advocate novel dryland stewardship strategies, which foster adaptive capacity, and continuous evaluation and social learning at all levels. Finally, we call for an effective, flexible and viable policy framework that enhances local biotic and cultural diversity of drylands to transform global drylands into a resilient biome in the context of global environmental and social change.

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The tremendous variability in physical conditions of forest enterprises as well as attitudinal aspects of their managers is seen as a major impediment to the understanding and optimization of forest management. For this reason, former studies using several methodological approaches-including meta analysis of econometric studies, binary choice models and stochastic frontier models-in many cases remained on a qualitative and more holistic level. This paper assesses the applicability of Bayesian Belief Networks (BBN) for the analysis of net income based on detailed 2006 economic data from the German federal accountancy network of forest enterprises larger than 200 ha. A network with one dependent (target) and 30 independent (explaining) variables was designed. The BBN has proven helpful for qualitative and to some extent quantitative analysis of economic data. It has become obvious that the completeness of populating the BBN model must be seen as a constraint. The speed of the calculations and the use of dependent probabilities can be seen as benefits of the BBN approach that reduce the risk of misinterpretation in comparison with traditional analysis methods such as the comparison of different strata. The visibility and presentability of the BBN approach facilitates its use in controlling and optimizing processes.