Abstract: The risks of food consumption are primarily linked to those of food production due to stochastic weather. Other sources of risk are associated with break-down of food trade or transport for weather or political reasons. Hopefully, future cures against increased risk due to climate change may be found with new agricultural technologies, systems of storage from favorable to unfavorable periods, more flexible trade-arrangements between favorable and unfavorable places. However, in the short run one has to rely on the available technology, storage facilities and trade agreements. With a realistic model of the stochastic global food system, it should be possible to measure risks of certain extreme unfavorable events.A realistic case will have countries with different climate in different growing seasons. Markets will be open for trade at a number of points per year, in which decisions of production, storage, trade and consumption can be coordinated as a static equilibrium. Determinants of this equilibrium are the weather up to this date reflected in the state of crops, the available harvested stocks and the decision-maker’s preferences. With a global stochastic process of weather, a stochastic sequence of equilibria follows. No Label

Abstract: Multi-model forecasting has drawn some attention in crop science for evaluating effect of climate change on crop yields. The principle is to run several individual process-based crop models under several climate scenarios in order to generate ensembles of output values. This paper describes a simple Bayesian method – called Bayes linear method- for updating ensemble of crop model outputs using yield observations. The principle is to summarize the ensemble of crop model outputs by its mean and variance, and then to adjust these two quantities to yield observations in order to reduce uncertainty. The adjusted mean and variance combine two sources of information, i.e., the ensemble of crop model outputs and the observations. Interestingly, with this method, observations collected under a given climate scenario can be used to adjust mean and variance of the model ensemble under a different scenario. Another advantage of the proposed method is that it does not rely on a separate calibration of each individual crop model. The uncertainty reduction resulting from the adjustment of an ensemble of crop models to observations was assessed in a numerical application. The implementation of the Bayes linear method systematically reduced uncertainty, but the results showed the effectiveness of this method varied in function of several factors, especially the accuracy of the yield observation, and the covariance between the crop model output and the observation. (C) 2015 Elsevier B.V. All rights reserved.