FORECASTING COMMERCIAL HARVEST OF MARINE SHRIMP USING A MARKOV-CHAIN MODEL Academic Article uri icon

abstract

  • The ability to forecast harvest levels is a prerequisite for affective management of shrimp and other commercial fisheries. One of the more recent advances in crop yield forecasting is a method based on Markov chain theory. The method provides more information concerning the yield and requires less stringent assumptions than more traditional regression approaches. The Markov model provides forecast distributions of final crop yield depending on the state of the system at selected times prior to the end of the harvest season. In addition to standard point estimates, such as the mean and standard deviation or median and interquartile range, a forecasted nonparametric harvest distribution, with its associated probability interval predictions, and predicted probabilities of exceeding or falling below specified harvest levels also are available. Application of the Markov model to forecast total annual commercial harvest of brown shrimp in the northwestern Gulf of Mexico suggests that good predictions can be made by June or July, with some predictive capabilities present as early as April. 1988.

published proceedings

  • ECOLOGICAL MODELLING

author list (cited authors)

  • GRANT, W. E., MATIS, J. H., & MILLER, W.

citation count

  • 3

complete list of authors

  • GRANT, WE||MATIS, JH||MILLER, W

publication date

  • November 1988