Crop production archetype - Global analysis
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Journal Title
Journal ISSN
Volume Title
School of Engineering |
Master's thesis
Authors
Date
2024-11-18
Department
Major/Subject
Mcode
Degree programme
Master's Programme in Water and Environmental Engineering
Language
en
Pages
82
Series
Abstract
Crop production greatly influences global food security and the environment. To understand the complexity of the crop production system, knowledge about its characteristics is needed to provide integrated policy to ensure food security and sustainable agricultural intensification worldwide. However, there are no studies that analyze the characteristics of the crop production system on a global scale. In this thesis, archetype analysis has been performed using the Two-Stage Self-Organizing Maps method. Twenty-five archetypes, categorized into eight groups, have been identified, explaining the characteristics of the crop production system globally at a 5 arc-minute resolution. The archetypes are based on 17 variables from Biophysical, Management, Socio-economic, and Crop-field functional types, representing attributes of the crop production system. The inputs were normalized to balance the influence of each variable equally. The results show similarities and differences between regions across the globe and define the systems into simplified characteristics. Global maps of each group were created with the distribution percentage of each archetype to illustrate the characteristics of each location. Moreover, the number of unique archetypes in each country was calculated to represent the complexity of the crop production system in each country. With these results, policymakers can create targeted policies for food security and resilience while fostering cross-regional learning and dialogue. Ultimately, this aids in developing more sustainable policies that support agricultural intensification.Description
Supervisor
Kummu, MattiThesis advisor
Niva, VenlaChrisendo, Daniel
Keywords
crop production, archetype analysis, two-stage self-organising maps, global crop systems, food security, sustainable agricultural intensification