The role of uncertainty and expectations in modeling (range)land use strategies: An application of dynamic optimization modeling with recursion

Abstract This paper presents a bio-economic optimization modeling approach for the simulation of land use decision making by farmers faced with climatic uncertainties. The approach is applied to the study of land use strategies on commercial ranches in Namibia. First, we compare two models differing in their structure: the first one is an inter-temporal optimization model (forward-looking with perfect foresight) while the second is recursive and it explicitly incorporates uncertainty in the decision making process. Second, we point out the structural advantage of the recursive optimization model in its ability to simulate how decision makers' perceptions on the occurrence of stochastic events alter land use strategies and their economic and ecological outcomes. Both models make use of a State-and-Transition conceptual framework to depict the bio-economic feedback. We found that the incorporation of rainfall uncertainty in decision making is crucial when modeling land use strategies in highly variable ecological–economic systems such as ranches in arid rain-fed areas. Where knowledge of rainfall distribution is inaccurate (due to lack of experience or climate change) both, farmers and rangelands, would be better off by precautiously expecting low rainfalls. Finally, our results show that minimizing herd size adjustment costs would support the establishment of sustainable land use strategies.