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Activity Number: 414 - Models for Environmental Processes
Type: Contributed
Date/Time: Tuesday, July 30, 2019 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistics and the Environment
Abstract #306815
Title: Characterizing Global Spatio-Temporal Patterns of Crop Production Using Multilevel Network Analysis
Author(s): Srishti Vishwakarma* and Vyacheslav Lyubchich and Xin Zhang
Companies: University of Maryland Center for Environmental Science and University of Maryland Center for Environmental Science and University of Maryland Center for Environmental Science
Keywords: random network; community detection; trend synchronism; time series; precipitation; climate

With changing climate and rising population, it will become more challenging to grow more crops to satisfy food demand. Hence, it is highly important to develop strategies for sustainable intensification of agricultural production. Establishing such strategies requires an understanding of historical trends of crop yields to identify potential yield level and its rate of increase. We approach this problem by studying yield patterns across different temporal and spatial scales. Specifically, we use multilayer network structure of countries, where layers of network connections are defined based on synchronism of long-term trends or short-term fluctuations, common climate, etc. We apply community detection algorithms to find clusters of countries. Overall, the clustering results will characterize global patterns of yields for different crops, identify drivers behind the increase/decrease/stagnant yield in groups of countries, and facilitate the reduction of the disparities in access to food resources.

Authors who are presenting talks have a * after their name.

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