Analysing the economic value of meteorological information to improve crop risk management decisions in a dynamic context

We evaluate the added value of a forecast service that can provide probabilistic predictions for adverse weather events for two differentiated seasons, corresponding to the same productive cycle. The paper builds on a cost-loss dynamic model, by considering the role of forecasting systems in the decision making process. We present the analytical solution for this problem which is consistent with the numerical results in the literature. However, we prove that there is a range of regions for the optimal policy depending on the cost of crop protection, the avoided loss and the quality of the information available. Finally, we illustrate the results with a numerical example.