New Forests

, Volume 50, Issue 4, pp 663–676 | Cite as

Prediction of post-thinning stem volume in slash pine stands by means of state and transition models

  • Santiago FiandinoEmail author
  • Jose Plevich
  • Juan Tarico
  • Marco Utello
  • Javier Gyenge


Predicting growth and production is the key to effective forest management, especially in those stands where silvicultural treatments are more intensive, such as silvopastoral systems. The aim of this study was to fit a state and transition model (STM) to predict the stem volume of slash pine silvopastoral systems under different management strategies. Volume growth was modeled by using the dominant height and the Relative Density Index, which can be related to other density indices (such as the Height Factor) through a proportionality factor. This link between density indices is what makes it possible to develop the transition functions, which are used to predict post-thinning stem volume. The transitional functions were established through three different approaches. Although all of them are good predictors of the Relative Density Index pattern, the best results in volume prediction were obtained when fitting the Weibull model to predict the Relative Density Index as a response of the Height Factor. By using this transition function, the differences in the mean volume between the predicted and observed data were less than 7% for all cases. We conclude that the proposed models are valuable management tools to predict the stem volume accumulated in the post-thinning period, and therefore, this finding may improve the management planning of the plantations of the region.


Density indices Stand volume Silvopastoral systems Thinning Transition functions 



This research was funded through a PPI (SECyT-UNRC) and CONICET (doctoral scholarship). The authors thank to the FAV-UNRC Agrometeorology research group for providing meteorological data of the study area. We also thank to the people in charge of “Las Guindas” rural establishment for their help and support.

Supplementary material

11056_2018_9688_MOESM1_ESM.pdf (17 kb)
Summary statistics obtained from the inventories, presented according to the stand density (150, 250 and 450 TPH) (PDF 16 kb)


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Copyright information

© Springer Nature B.V. 2018

Authors and Affiliations

  1. 1.Department of Plant ProductionNational University of Río CuartoRío CuartoArgentina
  2. 2.CONICETCABAArgentina
  3. 3.AER TandilINTA EEA BalcarceBuenos AiresArgentina

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