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Economic policy, "alternative data" and global agriculture: from the trans-Atlantic slave trade to agroecology

Chang, Marina; Huang, C. H.; Mian, I.S

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  "publisher": "Zenodo", 
  "DOI": "10.5281/zenodo.3443677", 
  "title": "Economic policy, \"alternative data\" and global agriculture: from the trans-Atlantic slave trade to agroecology", 
  "issued": {
    "date-parts": [
  "abstract": "<p>We show that narrative visualisation can contribute to identifying financial, legal, political, trade and other mechanisms capable of serving the diverse needs of practitioners of agroecology (primarily small scale farmers) and advocates of food sovereignty. Using financial and non-financial public information (re)sources such as historical macroeconomic data from the Bank of England and open source software tools, we paint portraits of (a) the trans-Atlantic slave trade and European Empires, (b) 21st century large-scale land acquisitions, and (c) traditional farming systems, agricultural biodiversity, and climate change. This triptych of background notes plus autonomous yet complementary cartograms and timelines overlaid with events provides long historical and large geographical lenses for understanding how the web of institutional and social structures of the United Kingdom, Europe, and the United States of America were and remain central to the international political ecology of agriculture, particularly food and fibre. We propose that policies that support, strengthen and scale agroecology can increase financial stability by reducing climate change-related physical, liability and transition risks thereby making macroeconomies more resilient to crises and prepared for adverse shocks. This exploration of human and natural systems affords also a window on the emerging fields of structural one health and planetary health.&nbsp;</p>", 
  "author": [
      "family": "Chang, Marina"
      "family": "Huang, C. H."
      "family": "Mian, I.S"
  "id": "3443677", 
  "event-place": "London", 
  "type": "article", 
  "event": "Data for Policy 2017: Government by Algorithm (Data for Policy)"
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