This thesis explores the applicability of the resilience perspective on agro-ecosystems dynamics. It start out by using the five heuristics of the resilience perspective on intensive agricultural systems. Simulations with a dynamic farm model suggest that conventional farming short cuts the adaptive cycle leading to an ‘incremental adaptation’ trap. Panarchy is therefore claimed as a leading heuristic to understand long-term dynamics and current management characteristics. This interaction of long-term dynamics with current management leads to an asymmetry in the landscape. This asymmetry leads to windows of opportunities for farmers. However, disregarding the cross-scale nature of the asymmetry might also lead to a cascade of events that undermine the resilience of the landscape as whole. The cross-scale interactions of landscape dynamics and farm management suggest a co-evolution of production intensity and landscape pattern. Moreover trajectories of intensification might even be linked to certain tipping points of combinations of landscape characteristics and management. Therefore the landscape asymmetry might yield insight in agro-ecosystem functioning. The landscape asymmetry potentially provides a level of self-organisation above the farm. However, identifying the asymmetry appeared to be problematic. Next to scale issues, the current pattern does not necessary result from current management, leading to a de-coupling of pattern and process. A re-coupling of management and landscape asymmetry can exploit positive feedbacks. I suggest the use of identity to locate asymmetries and to use space-time substitutions to experiment with the typical slow variables that shape the asymmetry. The theory developed in this thesis is grounded on empirical farm management data and dynamical model simulation of intensive dairy farming in the Netherlands and small-holder systems in Zimbabwe.