Assessing the impact of climate change on agricultural systems requires whole farm optimization models that can be used to simulate the behaviour of farmers and evaluate future adaptation strategies within scenarios of climate change. A good representation of the multi-objective nature of farmer’s decision making is essential for accurate model predictions. Often in existing studies for reasons of simplification the multi-objective nature of the farmer’s decision making is ignored and the existence of a single economic objective that drives the decision making process is assumed. In these studies, calibration techniques like Positive Mathematical Programming are used to recover unknown parameters of a non-linear cost function based on historical decisions. However, the existence of multiple objectives in farmers decision making is ignored which might affect the predictive capacity of whole farm optimization models. We use a novel multi-objective calibration technique to recover the unknown parameters of a non-linear Compromise Programming model. The proposed calibration method accounts for multiple conflicting objectives, improves the predictive capacity of the model and relaxes assumptions underlying the calibration process. We apply the calibrated model to evaluate the impact of climate change scenarios and future adaptation strategies of arable farmers in the Netherlands.
|Publication status||Published - 2015|
|Event||27th European Conference on Operational Research, Glasgow, UK - |
Duration: 12 Jul 2015 → 15 Jul 2015
|Conference||27th European Conference on Operational Research, Glasgow, UK|
|Period||12/07/15 → 15/07/15|