The monoculture vs. rotation strategies in forestry: formalization and prediction by means of Markov-chain modelling

J Environ Manage. 2005 Oct;77(2):111-21. doi: 10.1016/j.jenvman.2005.03.005.

Abstract

The monoculture strategy of forest management, where the same tree species (e.g., Picea abies) is cultivated in a number of successive planting-growing-felling cycles, is generally considered to be economically efficient, yet not sustainable as it reduces biodiversity in the forest. The sound alternative suggests a long-term strategy of forest management in which different forest types rotate either with planting after clear cutting, or by natural forest succession, yet the commercial output remains dubious. We suggest an approach to formalization and modelling forest dynamics in the long-term by means of Markov chains, the monoculture strategy resulting in an absorbing chain and the rotation one in a regular chain. The approach is illustrated with a case study of Russkii Les, a managed forest located in the Moscow Region, Russia, and the nearby forest reserve having been used as a data source for undisturbed forest dynamics. Starting with conceptual schemes of transitions among certain forest types (states of the chain) in the monoculture and rotation cases, we estimated the transition probabilities by an original method based on average duration of the corresponding states and on the likelihood of alternative transitions from a state into the next one. Formal analysis of the regular chain reveals an opportunity to achieve particular management objectives within the rotation strategy, in particular, to get the distribution of forest types in accordance with an adopted hierarchy of their commercial values, i.e. more valuable types have greater shares.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Biodiversity
  • Conservation of Natural Resources / economics
  • Conservation of Natural Resources / methods*
  • Forestry / economics
  • Forestry / methods*
  • Markov Chains*
  • Models, Biological
  • Time Factors