ABSTRACT
Extreme events, such as those caused by climate change, economic or geopolitical shocks, and pest or disease epidemics, threaten global food security. The complexity of causation, as well as the myriad ways that an event, or a sequence of events, creates cascading and systemic impacts, poses significant challenges to food systems research and policy alike. To identify priority food security risks and research opportunities, we asked experts from a range of fields and geographies to describe key threats to global food security over the next two decades and to suggest key research questions and gaps on this topic. Here, we present a prioritization of threats to global food security from extreme events, as well as emerging research questions that highlight the conceptual and practical challenges that exist in designing, adopting, and governing resilient food systems. We hope that these findings help in directing research funding and resources toward food system transformations needed to help society tackle major food system risks and food insecurity under extreme events.
ABSTRACT
Periodic assessments of the risk of water contamination by pesticides help decision makers improve the sustainability of agricultural management practices. In Canada, when evaluating the risk of water contamination by pesticides, 2 main constraints arise. First, because the area of interest is large, a pesticide transport model with low computational running time is mandatory. Second, some relevant input data for simulations are not known, and most are known only at coarse scale. This study aims to develop a robust methodology to estimate the evolution of the risk of water contamination by pesticides across Canada. To circumvent the 2 aforementioned issues, we constructed a stochastic model and coupled it to the 1-dimensional pesticide fate model Pesticide Root Zone Model (PRZM). To account for input data uncertainty, the stochastic model uses a Monte Carlo approach to generate several pesticide application scenarios and to randomly select PRZM parameter values. One hundred different scenarios were simulated for each of over 2000 regions (Soil Landscapes of Canada [SLC] polygons) for the years 1981 and 2006. Overall, the results indicated that in those regions in which the risk increased from 1981 to 2006, the increase in risk was mainly attributable to the increased area treated by pesticides or an increase in the number of days with runoff. More specifically, this work identifies the areas at higher risk, where further analyses with finer-scale input data should be performed. The model is specific for Canadian data, but the framework could be adapted for other large countries.
