Anticipation in Agriculture

Living reference work entry

Abstract

This chapter explores anticipation in agriculture through relational system analysis. It explains why the current trend of technological development in agriculture, creating land without farmers in developed countries and farmers without land in developing countries, is unsustainable. It starts out with a theoretical discussion on how relational system analysis can make a useful contribution to the analysis of the functioning of complex adaptive systems, such as social-ecological systems. Human societies belong to the special class of self-(re)producing and self-maintaining metabolic systems that depend on anticipation to preserve and adapt their identity in time. Anticipation is achieved through a semiotic process in which anticipative narratives associated with recorded information are used to guide action and tested for their validity. It is shown that anticipation requires the ability to establish, reproduce, and adapt a set of expected relations between functional (notional) and structural (physical) components of the metabolic system. The theoretical framework is used to identify the drivers of the evolutionary path of agriculture and explain why policies regulating its development have consistently failed to anticipate troubles. Finally, the analysis and explanation of what went wrong is used to reflect on required changes in the storytelling about agriculture for the future.

Keywords

Anticipation Agriculture Relational analysis Sustainability Biosemiotics Holon Metabolic pattern Social-ecological system 

References

  1. Ahl, V., & Allen, T. F. H. (1996). Hierarchy theory. New York: Columbia University Press.Google Scholar
  2. Allen, P. (1997). Cities and regions as self-organizing systems. Amsterdam: Gordon and Breach Science Publishers.Google Scholar
  3. Allen, T. F. H., & Giampietro, M. (2006). Narratives and transdisciplines for a post-industrial world. Systems Research and Behavioral Science, 23, 1–21.CrossRefGoogle Scholar
  4. Allen, T., & Starr, T. (1982). Hierarchy: Perspectives for ecological complexity. Chicago: University of Chicago Press.Google Scholar
  5. Brooks, D. R., Collier, J., Maurer, B. A., Smith, J. D. H., & Wiley, E. O. (1989). Entropy and information in evolving biological systems. Biology and Philosophy, 4, 407–432.CrossRefGoogle Scholar
  6. Cochrane, W. (1958). Farm prices: Myth and reality. Minneapolis: University of Minnesota Press.Google Scholar
  7. Cooper, M. (2008). Life as surplus: Biotechnology and capitalism in the neoliberal Era. Seattle: University of Washington Press.Google Scholar
  8. Dyke, C. (1988a). The evolutionary dynamics of complex systems: A study in biosocial complexity. New York: Oxford University Press.Google Scholar
  9. Dyke, C. (1988b). Cities as dissipative structures. In B. H. Weber, D. J. Depew, & J. D. Smith (Eds.), Entropy, information and evolution: New perspectives on physical and biological evolution (pp. 355–367). Cambridge: MIT Press.Google Scholar
  10. Emmeche, C. (1997). Defining life, explaining emergence. Available on line at: http://www.nbi.dk/~emmeche/cePubl/97e.defLife.v3f.html.
  11. Funtowicz, S. O., & Ravetz, J. R. (1990). Uncertainty and quality in science policy. Dordrecht: Kulwer Academic Publishers.CrossRefGoogle Scholar
  12. Giampietro, M. (2009). The future of agriculture: GMOs and the agonizing paradigm of industrial agriculture. In A. Guimaraes Pereira & S. Funtowicz (Eds.), Science for policy: Challenges and opportunities (pp. 83–104). New Delhi: Oxford University Press.Google Scholar
  13. Giampietro, M., & Mayumi, K. (2009). The biofuel delusion: The fallacy of large scale agro-biofuels production. London: Earthscan Research Edition. 320 pp.Google Scholar
  14. Giampietro, M., Allen, T. F. H., & Mayumi, K. (2006). The epistemological predicament associated with purposive quantitative analysis. Ecological Complexity, 3(4), 307–327.CrossRefGoogle Scholar
  15. Giampietro, M., Mayumi, K., & Sorman, A. H. (2012). The metabolic pattern of societies: Where economists fall short. London: Routledge. 408 pp.Google Scholar
  16. Glansdorff, P., & Prigogine, I. (1971). Thermodynamics theory of structure, stability and fluctuations. New York: Wiley.Google Scholar
  17. Goedde, L., Fischer, J., Denis, N., Tanaka, M., Yamada, Y. (2016). Empowering Japanese Agriculture for Global Impact. McKinsey Japan Report, October 2016. Tokyo: McKinsey & Company.Google Scholar
  18. González-López, R., & Giampietro, M. (2017). Multi-scale integrated analysis of charcoal production in complex social-ecological systems. Frontiers in Environmental Science, 5, article 54.  https://doi.org/10.3389/fenvs.2017.00054.CrossRefGoogle Scholar
  19. Koestler, A. (1968). The ghost in the machine. New York: The MacMillan Co. 365p.Google Scholar
  20. Koestler, A. (1969). Beyond atomism and holism–The concept of the holon. In A. Koestler & J. R. Smythies (Eds.), Beyond reductionism (pp. 192–232). London: Hutchinson.Google Scholar
  21. Koestler, A. (1978). Janus: A summing up. London: Hutchinson.Google Scholar
  22. Kull, K. (1999). Biosemiotics in the twentieth century: A view from biology. Semiotica, 127(1/4), 385–414. Available at: http://www.zbi.ee/~kalevi/bsxxfin.htm.Google Scholar
  23. Lakoff, G. (2010). Why it matters how we frame the environment. Environmental Communication: A Journal of Nature and Culture, 4(1), 70–81.CrossRefGoogle Scholar
  24. Louie, A. H. (2009). More than life itself. Frankfurt: Ontos Verlag.CrossRefGoogle Scholar
  25. Louie, A. H. (2013). The reflection of life. Functional entailment and imminence in relational biology. New York: Springer.Google Scholar
  26. Margalef, R. (1968). Perspectives in ecological theory. Chicago: University of Chicago Press.Google Scholar
  27. Marshall, A. (1890). Principles of economics. London: Macmillan and Company.Google Scholar
  28. Maturana, H. R., & Varela, F. J. (1980). Autopoiesis and cognition. The realization of the living. Dordrecht: D. Reidel.CrossRefGoogle Scholar
  29. McKinsey Global Institute. (2015). Debt and (not much) Deleveraging (February). http://www.mckinsey.com/insights/economic_studies/debt_and_not_much_deleveraging.
  30. Millennium Ecosystem Assessment. (2005). Summary for decision makers. In Ecosystems and human well-being: Synthesis (pp. 1–24). Washington, DC: Island Press.Google Scholar
  31. Nicolis, G., & Prigogine, I. (1977). Self-organization in nonequilibrium systems. New York: Wiley-Interscience.Google Scholar
  32. Odum, H. T. (1971). Environment, power, and society. New York: Wiley-Interscience.Google Scholar
  33. Odum, H. T. (1983). Systems ecology. New York: Wiley.Google Scholar
  34. Patching, D. (1990). Practical soft systems analysis. Harlow, UK: Pearson Education Limited.Google Scholar
  35. Pattee, H. H. (Ed.). (1973). Hierarchy theory. New York: George Braziller.Google Scholar
  36. Pattee, H. H. (1995). Evolving self-reference: Matter, symbols and semantic closure. Communication and Cognition – Artificial intelligence 12 (1–2), 9–28.Google Scholar
  37. Pattee, H. H. (2013). Epistemic, evolutionary, and physical conditions for biological information. Special issue on information in biosemiotics, Søren brier and cliff Joslyn, eds. Biosemiotics, 6(1), 9–31.CrossRefGoogle Scholar
  38. Peirce, C. S. (1931–36). In C. Hartshorne & P. Weiss (Eds.), The collected papers. Volumes 1–6. Cambridge, MA: Harvard University Press.Google Scholar
  39. Pigou, A. (1920). Principles of welfare economics. London: Macmillan and Company.Google Scholar
  40. Poli, R. (2017). Introducing anticipation. In Handbook of anticipation. Springer International Publishing.Google Scholar
  41. Polimeni, J. M., Mayumi, K., Giampietro, M., & Alcott, B. (2008). The Jevons paradox and the myth of resource efficiency improvements (p. 200). London: Earthscan Research Edition.Google Scholar
  42. Prigogine, I. (1978). From being to becoming. San Francisco: W.H. Freeman and company.Google Scholar
  43. Prigogine, I., & Stengers, I. (1984). Order out of chaos. New York: Bantam Books.Google Scholar
  44. Rashevsky, N. (1954). Topology and life: In search of general mathematical principles in biology and sociology. Bulletin of Mathematical Biophysics, 16, 317–348.CrossRefGoogle Scholar
  45. Rayner, S. (2012). Uncomfortable knowledge: The social construction of ignorance in science and environmental policy discourses. Economy and Society, 41(1), 107–125.CrossRefGoogle Scholar
  46. Rosen, R. (1958a). The representation of biological systems from the standpoint of the theory of categories. Bulletin of Mathematical Biophysics, 20, 317–341.Google Scholar
  47. Rosen, R. (1958b). A relational theory of biological systems. Bulletin of Mathematical Biophysics 20(3), 245–260.Google Scholar
  48. Rosen, R. (1977). Complexity as a system property. International Journal of General Systems, 3, 227–232.CrossRefGoogle Scholar
  49. Rosen, R. (1985). Anticipatory systems: Philosophical Mathematical and Methodological Foundations. New York: Pergamon Press.Google Scholar
  50. Rosen, R. (1991). Life itself: A comprehensive inquiry into the nature, origin and fabrication of life (p. 285). New York: Columbia University Press.Google Scholar
  51. Saltelli, A., & Giampietro, M. (2016). The fallacy of evidence-based policy. In Science on the verge (pp. 31–69). Tempe: Consortium for Science, Policy & Outcomes, Arizona State University. http://cspo.org/publication/the-rightful-place-of-science-science-on-the-verge/.Google Scholar
  52. Saltelli, A., & Giampietro, M. (2017). What is wrong with evidence based policy, and how can it be improved? Futures, 91, 62.  https://doi.org/10.1016/j.futures.2016.11.012.CrossRefGoogle Scholar
  53. Salthe, S. N. (1985). Evolving hierarchical systems: Their structure and representation. New York: Columbia University Press.Google Scholar
  54. Schrödinger, E. (1967). What is life & mind and matter. London: Cambridge University Press.Google Scholar
  55. Simon, H. A. (1962). The architecture of complexity. Proceedings of the American Philosophical Society, 106, 467–482.Google Scholar
  56. von Uexküll, J. (1957). A stroll through the worlds of animals and Men: A picture book of invisible worlds. In C. H. Schiller (Ed. & Trans.), Instinctive behavior: The development of a modern concept (pp. 5–80). New York: International Universities Press, Inc.Google Scholar
  57. Von Schilling, C., & Straussfogel, D. (2008). Introducing the research. Entropy debt: a link to sustainability? In: Proceedings of the 52nd Annual Meeting of the International Society for the Systems Sciences (ISSS), University of Wisconsin, Madison (WI), USA, 13-18.Google Scholar
  58. Weber, B. H., Depew, D. J., & Smith, J. D. (Eds.). (1988). Entropy, information and evolution: New perspectives on physical and biological evolution. Cambridge: MIT Press.Google Scholar
  59. Whyte, L. L., Wilson, A. G., & Wilson, D. (Eds.). (1969). Hierarchical Structures. New York: American Elsevier Publishing Company, Inc.Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Institut de Ciència i Tecnologia AmbientalsUniversitat Autònoma de BarcelonaBellaterraSpain
  2. 2.Institució Catalana de Recerca i Estudis Avançats (ICREA)BarcelonaSpain

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