Main factors deciding timber supply among non-industrial private forest (NIPF) owners

Main factors deciding timber supply among non-industrial private forest (NIPF) owners

Have you ever thought what factors determine timber supply among non-industrial private forest owners? In this post, I discuss some basic theories that drive decisions of NIPF owners, and I focus on some factors that are crucial when you are planning to start a new timber business in particular location. Enjoy! 

Private forests matter

Private forest owners have a crucial role in achieving sustainable forest management, in sustaining the productivity of forests and in satisfying the increasing demand for wood resources from wood processing manufacturers and bioenergy producers.

In Europe ca. 49.6% of forest and other wooded land is privately owned, and 50.1% publicly. On the other hand, in the U.S. about 37.9% of all forested land is represented by NIPFs, most in the East.

One of the most important decisions in forestry is determining the optimal harvest time, which is a complex issue influenced by many parameters. Someone who just finished the course in forest economics could say that the timber supply among NIPF owners depends on Faustmann theorem, called also Bare Land Value or Soil Expectation Value or its extension towards ecosystem services, i.e. Hartman theorem. Unfortunatelly, the real world differs from theoretical models, which are rarely applied in practice due to partly unrealistic assumptions, such as:

  • Constant timber prices, discount rate, or
  • Omission of the embedded option to defer the decision to harvests

>>READ MORE: Why interest rates differ in timberland investments?

So what theory drives then behavior of private forest owner?

The answer is simple: Utility maximization theory. Shortly speaking, utility is a measure of preferences over some set of goods (including services: something that satisfies human wants); it represents satisfaction experienced by the consumption of a good. The utility maximization problem is following:

How should I spend my money in order to maximize my utility?

The model is therefore:

Max u = u(x1, … , xn)

       s.t. Σ pi*xi ≤ m

            xi ≥ 0 Ɐ

, where u- utility; x1, … , xn – goods; m – income; p – prices

utility theory in timber supply

Here, for simplicity I draw two goods, X (spruce pulpwood in m3) and Y (pine pulpwood in m3), that consumer, in this case, let’s say paper mill, can buy. Paper mill has some budget (a red line) that can spend either on purchase of 100% spruce pulpwood, or 100% pine pulpwood, or on combination of those two. The green area under the red line represent feasible budget set, or in other words that within the area the pulp mill can select any combination of those two goods without worrying about the budget. Then we have two utility curves, which represent the pulp mill level of satisfaction in consuming pine and spruce pulpwood. I put many different x1, ….xn, to just mark that utility functions can have many variables and can be drawn in N-dimensions, not only 2D. The idea behind utility maximization problem is to find such combination of different species of pulpwood, given budget constraint, to maximize pulp mill satisfaction (utility). Forest owners are not differnt in that matter, they want to maximize the utility either for themselves, or for investors.

>>READ ALSO: The EU as the world’s largest wood pellet market

Every forest owner who is a utility maximizer has such a problem in mind. Let’s consider two forest owners now, who can either use their land for deer production (like in New Zealand) or for growing trees for sawtimber. Just a simple example.

The blue line represents budget constraint (5K Euro), while green and purple dashed ones show objectives of two different forest owners with differnt objectives based on their values of things. First forest owner (you can call him Forester) values more timber (50 Euro/m3) than Habitat Site Index (HSI). I put here HSI as some kind measurement of deer production, as you can have either deers or trees on the property. However, at the beginning of budget line you can see an increase. It means that at the beginning trees and deer complement each other (e.g. deer have shade from trees). At the peak possible HSI however we see that additional trees have a negative or competitive relationship with HSI. Assuming that the last part of budget (btw, this budget also shows our production possibility frontier) at very high sawtimber values is vertical, the relationship there looks independent because changes in deer habitat don’t change tree volume.

>>READ ALSO: Does an aggressive acquisition strategy always pay back? – pulp and paper industry example

The second owner (more nature oriented) has different preferences than first one, i.e. he prefers more HSI as it has bigger value to him over timber. That it is why his objective line is more horizontal than Forest Owner 1. When you shift his objective line towards budget line (max utility given budget) than naturally he will end with the solution that he will produce more HSI than timber on his property (contrary to Forester).

Timber supply – some factors to consider

1. Ownership structure of forest and other wooded land

1a. In Europe

factors deciding timber supply among NIPF owners

1b. In USA

factors deciding timber supply among NIPF owners

>>READ ALSO: The most developed wood supply chain in the world. Others should learn from it!

2. Share of size classes as percentage of total number of private holdings in Europe and U.S.

3. Occupation, age and gender of forest owners as important characteristics influencing their behavior

factors deciding timber supply among NIPF owners

Main factors deciding timber supply among NIPF owners

Based on literature review, we found following main factors deciding timber supply among NIPF owners:

  • Law and policy (e.g. minimum allowed rotation age, replanting rules etc.)
  • Wood prices (own and cross price elasticities), interest rates, growing stock (Bolkesjø et al. 2010), market for wood product (e.g. biomass, pulplogs) and its price (Markowski-Lindsey et al. 2012)
  • Prestemon et al. (2000) found that NIPF were elastically responsive when they perceived prices increases as temporary, but less elastically or negatively responsive when prices increases were perceived as permanent.
  • Forester’s age, income and education (Beach et al. 2005) – mostly “university degree” (mostly a degree in forestry) not necessarily economic education matters (Sauter et al. 2016). Also gender (Kuuluvainen 2014 – women sold about 30% less m3/ha/yr than men, their harvests were less frequent but larger in quantitites)
  • Forest ownership parameters, e.g. size of forest land – economics of scale (Dennis 1989), fragmentation (Petucco et al. 2015) or, for instance, composition of tree species (Joshi 2011), age clases)
  • Amenities values, keeping forest for next generations (Hartman 1976)
  • category of NIFP owners on those who act according to profit maximization, and on those who give higher value to non-financial forest attributes (harvest less frequently) (Kuuluvainen et al. 1996). In this paper, authors found that “multiobjective owners” harvest significantly more (m3/ha/yr) than other groups (self-employed owners, recreationists, and investors). In other words, owners attitudes, beliefs and objective matter here. Some owners manage their forests in passive way, some probably do not know where their forest located is.


Before you start your forest-related business, and you have to rely on timber suppliers, you should be aware that forest owners are utility maximizers (same as you). Even if from the perspective of forest business they seem sometimes irrational in their behavior, they usually know what they are doing, and their decisions are definitely rational for themselves, and often based on their own values, which are often not clear. 

Socio-demographic characteristsics are very significant predictors of the harvesting decisions, and before you start your wood business , the proper market research among forest owners should be done to better understand who they are, what management objectives drive their decisions, or how they consider risk and incentives related to their forest.

The rule of thumb is that small parcel size significantly increases the production costs per unit in harvesting operation, planting, management, and in the same time lowers economic efficiency compared to industrial private forests for which there is a positive relationship with holding size. Based on these factors, different forest owners will behave differently on the wood market, and your forest-based business should always consider physical and socio-demographic characteristics of forest owners in the area you will operate and where you will have to deal with them in your forest business relations. 

Forest Business Analytics (FBA) – a specialized consulting and business analytical company in forest and wood industry sectors – has worked with the leading forest companies for years, and understands the challenges in wood supply management. We have been involved in research projects related to supply availability and sustainability for the volumes and raw material types required by wood-using facilities. Our clients use FBA analyses to calculate transfer prices and develop wood or fiber supply agreements; locate, close, or expand wood-using mills or bioenergy plants, or make timberland investment decisions.

FBA is changing business analytics forever by data preparation and analysis — giving unprecedented power to forest and wood industry products businesses without the need for cumbersome and expensive IT investments. Thanks to solutions, your company will be able to adjust faster to changing business environment, and will have a knowledge how to deal with risk related to oversupply and shortage in the wood supply.

Author of the post: 

Rafal Chudy Forest Business AnalyticsRafal Chudy – PhD Candidate in forest and resource economics at the Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences (NMBU). He has acquired the international experience in forestry and forest economics at North Carolina State University, Swedish University of Agricultural Sciences, Oregon State University, University of Helsinki, University of Hamburg and Warsaw School of Economics. Rafal has gained profesional experience as forest economists and analyst at United States Department of Agriculture, National Forest Holding in Poland and many other companies from private sector.





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Main photo: Rafal Chudy