Quantitative Tools

1. Quantitative Tools

2. Objectives • Understand why quantitative tools are important in silviculture • Know some examples of quantitative tools used • Understand TIPSY

3. Why develop quantitative tools? • Decision making • Precise information transfer • Clear delineation of options

4. Examples • Yield tables • Density management diagrams • Stock and stand tables • Computer growth models • Economic analysis packages

5. TIPSY • TIPSY (Table Interpolation Program for Stand Tables) • Ministry of Forests • Uses results from a growth model (TASS)

6. | Trees (#/ha) & Merch Volume (m3/ha) by DBH Class (cm) Top|--------------------------------------------------------------------------------------------------------------------- Age Ht | Trees | Vol | (yr) (m)| 0.0+ | 12.5+ | 0 | 5 | 10 | 15 | 20 | 25 | 30 | 35 | 40 | 45 | 50 | 55 | 60 | 65 | 70 | -______________________________________________________________________________________ 0.0 0.0 1600 1600 0 0 10.0 2.3 1547 1514 33 0 0 0 20.0 8.3 1448 64 644 712 28 1 0 0 0 1 30.0 14.0 1412 6 80 413 708 198 7 81 0 0 0 50 29 2 40.0 18.6 1401 3 67 174 471 497 165 22 2 202 0 0 0 45 93 51 11 2 50.0 22.4 1365 40 156 349 383 295 111 26 5 0 304 0 0 35 80 105 59 19 5 0 60.0 25.5 1313 14 132 321 296 261 181 76 25 5 2 403 0 0 32 65 100 106 63 27 7 3 70.0 28.1 1256 4 99 303 263 204 180 123 56 17 5 2 499 0 0 30 58 81 112 109 67 27 9 6

7. | Trees (#/ha) & Merch Volume (m3/ha) by DBH Class (cm) Top|--------------------------------------------------------------------------------------------------------------------- Age Ht | Trees | Vol | (yr) (m)| 0.0+ | 12.5+ | 0 | 5 | 10 | 15 | 20 | 25 | 30 | 35 | 40 | 45 | 50 | 55 | 60 | 65 | 70 | -______________________________________________________________________________________ 0.0 0.0 1600 1600 0 0 10.0 2.3 1547 1514 33 0 0 0 20.0 8.3 1448 64 644 712 28 1 0 0 0 1 30.0 14.0 1412 6 80 413 708 198 7 81 0 0 0 50 29 2 40.0 18.6 1401 3 67 174 471 497 165 22 2 202 0 0 0 45 93 51 11 2 50.0 22.4 1365 40 156 349 383 295 111 26 5 0 304 0 0 35 80 105 59 19 5 0 60.0 25.5 1313 14 132 321 296 261 181 76 25 5 2 403 0 0 32 65 100 106 63 27 7 3 70.0 28.1 1256 4 99 303 263 204 180 123 56 17 5 2 499 0 0 30 58 81 112 109 67 27 9 6

8. | Trees (#/ha) & Merch Volume (m3/ha) by DBH Class (cm) Top|--------------------------------------------------------------------------------------------------------------------- Age Ht | Trees | Vol | (yr) (m)| 0.0+ | 12.5+ | 0 | 5 | 10 | 15 | 20 | 25 | 30 | 35 | 40 | 45 | 50 | 55 | 60 | 65 | 70 | -______________________________________________________________________________________ 0.0 0.0 1600 1600 0 0 10.0 2.3 1547 1514 33 0 0 0 20.0 8.3 1448 64 644 712 28 1 0 0 0 1 30.0 14.0 1412 6 80 413 708 198 7 81 0 0 0 50 29 2 40.0 18.6 1401 3 67 174 471 497 165 22 2 202 0 0 0 45 93 51 11 2 50.0 22.4 1365 40 156 349 383 295 111 26 5 0 304 0 0 35 80 105 59 19 5 0 60.0 25.5 1313 14 132 321 296 261 181 76 25 5 2 403 0 0 32 65 100 106 63 27 7 3 70.0 28.1 1256 4 99 303 263 204 180 123 56 17 5 2 499 0 0 30 58 81 112 109 67 27 9 6

9. Tree Number

10. Crown Cover

11. DBH

12. Height/Diameter

13. Species Matters

14. Growth Curves

15. Basal Area • Proportional to total LA accumulation over time • Historic artifact of: site quality X stand structure • Use is underpinned by Yoda’s theory of “final constant yield”

16. Basal Area

17. Volume Curves

18. Not Every Cubic Meter Is the Same

19. Lumber Recovery

20. Another Problem • Standing Volume Yield

21. Mortality • Gross yield • Net yield

22. What number is yield?

23. Harvest Index • Harvest Index is: [(harvestable volume)/(total volume)] • Utilization • Technology • Allocation

24. Harvest Index (Cubic Volume)

25. MAI

26. Total or Merchantable?

27. Thinnings • Total yield • Final yield

28. Volume (Site 30)