In this research, we investigate whether real-world agricultural land-use systems can be meaningfully approximated by emergent – complex systems – behavior. We do so by constructing an innovative pattern-oriented individual-based land-use transition model. The model exhibits complex systems behavior by combining simple yet plausible temporal and spatial mechanisms. These operate on cellular automata – abstractions of farmers – and allow automata to maximize utility at varying levels of complexity, rationality, and foresight generated by using Markov chains. By systematically combining mechanisms, we construct different process-based filters generating different emergent behavior and land-use patterns in statistical equilibrium states. Results show that if automata have foresight, emergent behavior can be interpreted as intensification. Furthermore, two different types of intensification can emerge: increasing yield by increasing inputs at constant total agricultural area or increasing yield by substitution of land for inputs. Overall, the results suggest that real-world agricultural land-use systems can be meaningfully approximated by emergent – complex systems – behavior.