This study evaluates normative (NMP) and positive (PMP) mathematical programming methods for the recursive dynamic agent-based sector model SWISSland, which determines production decisions for 3400 farm-level models for the ex-post period 2005 to 2012. This study clearly shows that PMP for crop production activities improves the forecasting performance of farm based agent-based models compared to NMP. It also shows that combining PMP and NMP could be a suitable approach for agent-based sector models. For short-term forecast PMP for all production activities and PMP combined with NMP lead to similar results. The results either show that PMP calibration based on revenues and PMP calibration based on the entropy approach lead to similar results. By combining PMP with NMP some limitations of PMP could be reduced. In branches where the adoption of new production activities is expected due to market, the NMP approach could be an appropriate solution.


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