Pruning Pine

A Spreadsheet Analysis of Pruning White Pine Butt Logs

For decades, research foresters have made periodic attempts at determining the rates of return earned by investments in pruning Eastern white pine.  Various types of analyses with varying results have contributed somewhat to the understanding of field foresters and forest landowners, but systematic analytical tools have been lacking until just recently.  Early in 1992, researchers at the U.S. Forest Service Pacific Northwest Research Station published a computer spreadsheet program and user's guide called PP Prune.  The purpose of the program is to allow users to conduct financial analyses of pruning Ponderosa pine.  Writing of the program was motivated by the increasing scarcity of large, old growth trees from which high grade, clear lumber can be produced.  Pruning of young trees was seen as the best alternative to maintaining the long-term supply of high quality logs for the regional lumber industry. 

While Ponderosa pine is silviculturally and anatomically different from Eastern white pine, the lumber recovery from trees of the same sizes and histories (pruned and unpruned) should be quite similar.  This means that the lumber recovery studies upon which PP Prune was based are reasonably applicable to the analysis of pruning Eastern white pine.  Until similar lumber recovery studies become available for pruned and unpruned Eastern white pine, we can use the PP Prune data and analysis with modification for the growing conditions and lumber prices of Eastern white pine to better understand whether, and to what extent, investments in pruning are profitable. 

The sample spreadsheet from the PP Prune program used input data (notes 1-15; see below)  from a hypothetical pruned Eastern white pine stand typical of Western Massachusetts.  The pruning regime assumes that the cost of pruning per tree is actually the net cost of pruning after cost-sharing from the Stewardship Incentives Program.  The regime also assumes interest/discount rates of 4% and 6% in order to show the present values added from pruning at two different rates; these values are shown in the third column of the rows designated "PNW of butt log" and "PNW per acre."  Both interest/discount rates are real, i.e., without inflation.  The current and future lumber prices are net of the costs of harvesting, milling, drying, and marketing.  The future prices are based on conservative estimates of real market value increases over the next 30 years.  Improved export markets for high grade pine logs and lumber could mean higher prices in 30 years.

A significant assumption made by the hypothetical regime is that periodic thinnings will be carried out to maintain fast growth rates on the pruned trees.  Thinnings should be done at least every 10 years, depending upon the volume of unpruned trees not removed in the initial thinning (which will eventually compete with the pruned trees), and depending upon the landowner's aesthetic values.  Neither the value of these future thinnings nor management and carrying costs are included in the PP Prune analysis; the former should at least cancel out the latter.

Notes on Input Data for PP Prune Running Eastern White Pine Regime

(1)   The cost of pruning per tree
(2)   The first interest/discount rate to be used in the analysis
(3)   A second interest/discount rate to be used in the analysis
(4)   Age of tree when pruned
(5)   Diameter at breast height of tree to be pruned
(6)   Total height of tree at time of pruning
(7)   Age of pruned tree at time of harvest
(8)   Diameter at breast height of pruned tree at time of harvest
(9)   Total height of pruned tree at time of harvest
(10)  Number of trees pruned per acre
(11)  The proportion of pruned trees surviving to harvest
(12)  Length of butt log with trim
(13)  Predicted small end diameter inside bark
(14)  Predicted cubic foot volume for average butt log in regime
(15)  Percentage of clear wood resulting from pruning

It is important to note that in calculating the internal rates of return (IRR), the pruning simulator only considers the value added to the trees by the pruning.  The simulator assumes that unpruned trees, to which it compares the lumber recovery of pruned trees, are growing at the same rates as the pruned trees in the indicated regime.  Thus, the IRR analysis is not for the overall management regime of pruning and thinning, but only for the pruning.  The IRR for pruning and thinning would in most cases be slightly lower than the 15.3% rate shown for the return to pruning alone because the initial thinning contributes to the final result, but is not counted as an initial cost.  However, as with the management and carrying costs referred to above, the returns from intermediate commercial thinnings should more than offset the initial cost of the first non-commercial thinning.

Unfortunately the spreadsheet does not explicitly show the future value per acre  to be expected from pruning and thinning.  However, this value may be easily calculated for this regime by multiplying pruned trees per acre (50) by the success rate (.70) and then multiplying the product (35) by the value per pruned log ($117).  The final product value of $4,095 per acre is what can be expected in 30 years from the butt logs; additional value of at least $1,000 per acre can be expected from the upper logs.  Higher success rates are of course possible, but since 22" DBH trees have crowns approximately 40' in diameter at the specified age and height, only 25 to 30 can fit on an acre.  The higher number of 35 per acre indicated above assumes that 5 to 10 large trees per acre are harvested in the commercial thinning preceding the final harvest and that the compounded value of those trees is added to the final harvest value.  The other 15 pruned trees per acre that are considered "unsuccessful" are those that show slow growth or are damaged in thinnings and are therefore removed before the final harvest.

Please call if you would like to have the results of a PP Prune analysis with different  input data of if you have questions about the program itself or the input data used  in this hypothetical analysis.

November, 1993