2. Timber Supply

Aggregated timber supply is dependent on individual forest owners' forest management decisions. Earlier we analyzed an important decision by the forest owner in determining long-term timber supply, namely the optimum rotation choice. A behavioural timber supply function can be derived from the (possibly extended) rotation model. For example, rotation choice is affected by (parametric) changes in the stumpage price level over a relevant planning period. The rotation choice in turn has an impact on the mean annual increment per hectare (i.e., long-term timber supply defined as VMAI=f(TF)/TF.1

In theory, a parametric increase in the stumpage price may surprisingly have a negative effect on long-term timber supply due to a shorter rotation period. However, if there is a permanent parametric increase in price, the short run timber supply increases as some of the existing stands become too mature.

It is extremely difficult to differentiate empirically between parametric and inter-temporal effects (Kuuluvainen & Tahvonen 1999). Parametric price changes are differences across individuals (i.e., regional prices) whereas inter-temporal changes are price movements across time for the same individual. Quantitative effects of parametric changes on long-term supply are poorly known, whereas impacts of inter-temporal changes on short-term timber supply are quantitatively better known. In Finland the inter-temporal effect of timber price has been positive (i.e., the short-term timber supply function is normally increasing in stumpage price).

In the short term, fluctuation of timber prices according to business cycles has profound effect on the profitability and timing of timber sales. If  a forest owner expects future prices to rise, it is optimal to postpone harvest of mature stand to the following year. Therefore, the relative increase in harvest revenue can be larger than the present year's harvest revenue (invested in alternative investment options). If, however, a forest owner expects future prices to fall, it is optimal to harvest stand earlier than at optimal age with constant prices. Consequently, the optimal cutting should concentrate in years with the highest price level.

As an empirical example, Toppinen (1998) estimated the (short-run) supply equation for sawlogs as follows:


A rise in current stumpage price, plogs, interest rate, r and timber stock, v all increased the supply of sawlogs.

Timber supply is also naturally dependent on factors other than stumpage price (i.e., on interest rate, costs, timber stock, expected future price level or owner-specific characteristics). Any change in these factors can shift the timber supply curve. It is also possible to incorporate some policy measures such as shifters in supply functions. As an example, Figure E15 illustrates the possible effect of conservation policy on supply and timber market outcome where a leftward shift in timber supply results in higher price and reduced cuttings. However, it remains unclear, how gross stumpage revenues eventually change. It is the shape of the demand curve that determines whether P2006 times Q2006 is greater or less than P2015 times Q2015.  Also, that because of elastic demand, the actual reduction in cuttings (Q2006 - Q2015) is smaller than the initial shift in supply (green arrow in Figure E15).

Figure E15: Leftward shift in timber supply caused (for example) by increased forest conservation (environmental policy)

Overall, the stumpage price was determined by the interplay between supply and demand factors (given perfect competition). The actual timber markets in many countries, have features that are not typical for competitive markets. Hence, the market form has a crucial impact on actual stumpage price determination. In addition, the development of (equilibrium) stumpage prices over time is always affected by several simultaneous shifts in timber demand and supply functions.

1. Recall that the rotation based maximum sustained yield maximized MAI, long term timber supply.

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