As the impact of climate change increases it is more likely that we will see an increase of extreme weather events leading to significant food production losses. Therefore, understanding the complexities of how production losses impact on policy (through export or import restrictions) and prices (through markets) is important for the governance of the global food system in the future. In this paper our aim is to understand the variability of food prices utilizing a statistical methodology relating to the detection of extreme values and change points in the decomposed time series of food price indices (change-point analysis). These change points are identified using the FAO total food price index and also the indices for meat, oil, cereal, dairy and sugar. The results of the study highlight for the first time specific change points within these food categories when these changes occur and also the duration of these periods before the next change.

随着气候变化影响的增加，我们更有可能看到极端天气事件增加，导致粮食生产大量损失。因此，了解生产损失如何影响政策（通过出口或进口限制）和价格（通过市场）的复杂性对于未来全球粮食系统的治理至关重要。在本文中，我们的目的是利用统计方法来了解食品价格的波动性，该方法与检测食品价格指数的分解时间序列中的极值和变化点有关（变化点分析）。这些变化点是根据粮农组织的总食品价格指数以及肉，油，谷物，乳制品和糖的指数确定的。