From local to global: understanding linkages between climate shocks and food trade
There is increasing interest in identifying and understanding how and why certain countries and their communities are prone to extreme weather events as well as cataloging their coping strategies. A critical component in building knowledge surrounding these extreme events is recognizing that defining these events varies across disciplines and stakeholders. Presently, there is a current lack of a unified, systematic framework to evaluate extreme weather events to agriculture across several spatial, time, and crop domains. The lack of consistency impedes the ability to understand how the occurrence of these events affects the global food trade network. The overarching goal of this dissertation is to determine if food trade mitigates or exacerbates the impact of extreme weather events to agriculturally-relevant areas. Prior efforts to understand what types of events affect agriculture and the food trade network have relied upon datasets that are either time-invariant for agricultural land use or crop-invariant with respect to the extreme events themselves. To overcome these deficits, this dissertation outlines a novel integrative approach that incorporates dynamic land-use modeling, high-resolution crop-specific estimates of extreme event exposure, and global trade data to understand linkages between events and trade across time, space, and crop. We draw upon open access climate, land use, and economic data. First, we have developed a new algorithm to spatially allocate national-level data to estimate likely sub-national crop-specific areas globally over time. We integrate these land-use estimates with crop and climate data to create a uniform framework for evaluating thermal (excessive heat and cold) and hydrologic (drought and floods) extreme events in agriculturally-relevant areas. Lastly, we contribute to the literature by providing an improved understanding of how extreme events in crop-specific agricultural areas affect the global food trade network in terms of trade openness and risk. In particular, this work highlights a differentiation in response by the global food trade network depending on the crop, event type, and openness measure considered. In this dissertation, we focus our efforts on 17 major crops (including maize, rice, soybeans, and wheat) for the period 1961-2014. We begin by providing estimates of likely changes in annual crop-specific spatial distributions; these estimates have also been made available publicly as the Probabilistic Cropland Allocation Model (PCAM). Next, we quantify crop-specific exposure rates to temperature and hydrologic extrema by fusing PCAM with climate and agronomic data to form the Agriculturally-Relevant Exposure to Shocks (ARES) model. Of the 17 crops included in this study, 13 had an increase in the exposure to extreme heat, while 9 increased their exposure to extreme cold. The ARES model has resulted in a multi-scale database that spans gridded-, country-, and global scales for all crops, event types, and years. Lastly, we integrate ARES with global food trade to develop multiple trade openness metrics and risk profiles. Importantly, we find that 55.4% of all crop-trade openness-extreme event combinations show an increase in trade openness as a result of a one percent change in production risk. This work enables an improved understanding of the complex relationship between trade and extreme weather events over the past last half-century.
agriculture, land use, trade, extreme events, hazards, sustainability
University of Illinois - Urbana-Champaign
Networked Digital Library of Theses and Dissertations
University of Illinois at Urbana-Champaign