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Modeling Stem Diameter Increment in Individual Pinus Occidentalis, Sw. Trees.

Based on the flexibility to provide for a range of stand circumstances, it is proposed that this future diameter model be used to estimate future diameter on individual trees of P. occidentalis in La Sierra, Dominican Republic.<br>

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Modeling Stem Diameter Increment in Individual Pinus Occidentalis, Sw. Trees.

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  1. Modeling Stem Diameter Increment in Individual Pinus Occidentalis, Sw. Trees in La Sierra, Dominican Republic Santiago Bueno & Eddie Bevilacqua Department of Forest and Natural Resources Management SUNY-ESF

  2. Range of Pinus occidentalis (Critchfield and Little 1966) Modeling Stem Diameter

  3. Area of Pinus occidentalis through the years Modeling Stem Diameter

  4. Forestry in the Dominican Republic Modeling Stem Diameter

  5. La Sierra Modeling Stem Diameter

  6. Objectives • Fit linear regression models for predicting DBH change over time, using as predictor variables single tree, elapsed time, stand attributes and different indices of competitive status. • Determine which statistical technique is best. • OLS • LME • Fixed Effects • Fixed + Random Effects • Determine from three response variables, which is best • dt • id5 • ln(id5+0.01) Modeling Stem Diameter

  7. Our model • Classification • Characteristics • Predict tree attributes individually • Are flexible to forecast growth regardless of age, species mixture or silvicultural system • Detailed description of stand structure and dynamics • Permit simulation of different silvicultural treatments • Design • The data • The stands • Intended use Modeling Stem Diameter

  8. What to model? DIAMETER OR BASAL AREA? GROWTH OR YIELD FUNCTION? Modeling Stem Diameter

  9. Data Modeling Stem Diameter

  10. Data Modeling Stem Diameter

  11. DBH projection over time Modeling Stem Diameter

  12. General approach in model development • Y = f1(tree size) + e • Y = f2(tree size, stand) + e • Y = f3(tree size, stand, competition) + e where Y = dt, id5, or ln(id5+0.01) tree size = d0 and/or BA0 stand = TPH, BA, SDI, and/or SI competition = d0/dq and/or BAL0 Modeling Stem Diameter

  13. Testing the goodness-of-fit of the response d5 Modeling Stem Diameter dt id5 • ln(id5+0.01) GOODNESS-OF-FIT (Bias, MSE, MAD, etc.) BEST ESTIMATION OF FUTURE DIAMETER

  14. Statistical techniques Modeling Stem Diameter

  15. Modeling Stem Diameter

  16. Modeling Stem Diameter

  17. Modeling Stem Diameter

  18. Observed versus predicted Values Modeling Stem Diameter

  19. Residual versus predicted Values Modeling Stem Diameter

  20. Modeling Stem Diameter

  21. Parameter estimates of the best model to predict future diameter (LME fixed + random) Modeling Stem Diameter

  22. Conclusion dt = f(t, d0, BAL0) + e • Use is simple • Biologically consistent according to forest growth expectations • Reasonable when projecting for at most 5 years • Sufficiently accurate: • LME including fixed and random parameters provided a better fit for both growth and yield functions. • In estimating future diameter, accuracy of predictions is within two centimeters for a five-year projection interval. • The dt model presents negligible bias in estimating future diameter, although the model slightly over predicts the response variable. • Based on the flexibility to provide for a range of stand circumstances, it is proposed that the future diameter model be used to estimate future diameter on individual trees of P. occidentalis in La Sierra, Dominican Republic. Modeling Stem Diameter

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