Chapter 3

The Paradigm and General Systems Work

What is the over-arching concept, the approach that you are suggesting? What is the major operational concept?

I've tried many times to make the working concept or paradigm small and simple. I'm not sure it can be captured but I shall try again. Lasting Forests is more than a business, a collection of land tracts, a bunch of principles for management, and a Web-site. It is large, unified way of thinking and working. It exists for people. It is a way of managing and controlling land. Land is an all-inclusive term and will be discussed later. Nevertheless, it is held to be essential to people and their high quality of life. The emphasis of the paradigm is private and industrial or corporate lands or contractual management of public land. It is a systems approach, a way of seeing land and people as a whole, comprehensive system. More than description, it is a way of actually working with and controlling that system, striving to move it to an optimum condition and hold it there. It defines the optimum in terms of money, as profits over a long period, a period at least the life of most people's grandchildren. The scope, complexity, dynamics and ease of making marginal or sub-optimum decisions requires that computers be used throughout. Computers are so important that they are not merely a tool but are integral to the realization of the system, to its imagination, domain of work, and potentials for achievement within a human lifetime.

That's a paragraph and it is the paradigm. It may be as brief as I can state it without feeling I have omitted substantial parts. What's the big idea? Cautious, sophisticated management of wildlands for sustained profits from private wildlands. I think that statement is too brief, but perhaps it will suffice for some purposes.

For some, each word needs expansion. Wildlands bothers some people. The emphasis is on forested lands. Lakes and ponds are included, however. Forests are the emphasis as a part of the rural scene but then there are young forests, fallow fields, grassy areas within forests, and edges of fields and forests are of great importance to many types of wild plants and animals. The vista from the mountain top is part of the forests, but it includes houses, barns, powerlines, fields, and woodlots. It is all of this. We start with the forest and work outward, cautiously, looking for reasonable limits, for many things are connected. Others have said this but we mean really, practically, functionally, and we have in our computer models that " if you change this, then that will be affected."...and we can compute how much!

We have seen and analyzed carefully the relations of systems. General systems theory was written in the late '60 and many authors have contributed. It has been developed as systems analysis, as operations research, and as special disciplines within various branches of engineering. Perhaps that is what we are proposing: engineering the land. That has some unfortunate connotations. It is more than that. Systems theory has been predominant within business schools and the journal Management Science regularly advances systems theory. This is not a book about systems theory. I note that the paradigm, the systems approach is a way of thinking, a pattern. Some people see the pattern as universal, useful in many fields, a first way to get started in analyzing almost anything, then to move past analysis (tearing it apart) to design (putting things together in useful ways). The word " General" does not mean vague or imprecise but having widespread relevance, perhaps universal. The general system as conceived within Lasting Forests has the components of boundary, inputs,processes, objectives, feedback, and feedforward. The picture is as follows.

In order to teach well you must have a believable topic, one that "works" and one in which you, as teacher, personally believe. You must also know exactly what behaviors you wish your potential learners to display. Educating to change attitudes is passé because the question remains: after their attitude changes, will students' behavior be that which I intend? An emphasis on behavioral change is not new, but it is rarely seen and rarely fully implemented. Behavioral change is the context for an alternative paradigm in natural resource management, one that I call Lasting Forests.

This paradigm is not one for general discussion. It is not prepared as an academic exercise, an exploration of interesting options. There are solutions here of natural resource issues.It is the grounds for major change in the way land is used in a capitalistic, representative-government nation with a rapidly urbanizing population with extremely different economic and welfare conditions.

Teaching to achieve it will be difficult, but no more so than the difficulties we have faced for 50 years and that we still address in conferences and books.

Lastuing Forests, the paradigm is a modified systems approach (Von Bertalanffy 1968) so that is not new. Describing a systems approach has been done in so many ways that no standard exists and the unique characteristics of what I mean need to be presented. The paradigm starts with context, then an emphasis on objectives.

Context

One aspect of the systems context is that we can view every land unit as unique. Every square meter is unique, at least every 30 x 30 meter Landsat pixel. The quot;watershed" is no longer relevant as a managerial unit. It over-aggregates information that we now have at very fine resolution. The land is a volume 5 miles (3 km) above a map area and 5 miles below it. Phenomena within it are as much or more a function of things in contiguous map cells and nearby as they are of factors in the area. Migratory bird presence is a conspicuous example, just as is pollution of water above a stream-rich pixel. Nearby water influences nesting animals; shadow from nearby objects influence conditions on a site. What you measure in a quadrant may have little bearing on the processes and inputs in that square. Perhaps this is the singular meaning of "landscape ecology." Now, within geography information systems we can load, combine, and model phenomena within the cell as affected by presence or conditions outside of the cell.

The educational implications are enormous in number and scope. We must work with our students to learn about all aspects of the land volume, then, aware that gaining "mastery" of the physical-biological system is impossible (thus irrational to try) we than seek specialists with one ancillary or sub-specialty. There will be a few who will become generalists. We need not stand in their way. The majority of students will struggle to gain a C-plus knowledge status in any discipline. The recommendation for one ancillary discipline is to assure there is functional awareness of (at least), ability to work with, the relations between very different fields.

A specific implication is that we can stop classifying and teaching how to classify land because we now have data and computer power to treat every land unit as unique. We no longer have to over-generalize for cartographic or other reasons.

Objective

The statement of objectives is in the coin of the day. "Money talks" is more than an idle expression. I am of the view that all aspects of the land base can be expressed as "constraints." "Make as much money as you can", is the objective, then followed by constraints of "subject to not cutting that stand of trees and staying a chain away from all streams." That statement of two constraints can be analyzed in terms of opportunity cost or foregone profit. The objective function is that of classic linear programming, namely, to maximize all values (converted to log (x + 1) (Green 1979), all evaluated as maximum net present expected value, all values expressed as estimates, all with probability of occurrence, all over 150 years, and with analyses run four times with interest rates set as (1) high, (2) low, (3) most likely, and (4) at the Overton and Hunt (19) discounting procedure. This formulation of the objective sounds more complex than it is. It is a rough, conservative, parametric approach to well known financial analyses. It has many variables and can be very discriminating. It is one readily implemented on the PC (e.g.,the software called Lindo). This phase-one analysis of objectives gives four answers based on the above discounting procedures. They are likely to be similar, but several are different often enough to suggest the need for alternatives.

It is well known and often repeated that optimizations are aids to making decision, that selecting the algorithm to use is itself a risky decision. Phase 2 analysis is the computation of the B index to the overall system performance where B* is the desired state of the system when owner's benefits are minimally achieved. The manager seeks to minimize that absolute value between the two for the lowest cost (C), i.e., the objective is to maximize R (achieve R*) where

R = ((B-B*)/B*) /C.

We have learned, painfully, that optimization is a complex process with many different procedures to fit special cases. It means maximization (or minimization, or stabilization) subject to some constraints. Formulating the objective is difficult and expressing all of the constraints is also difficult. Once computer have been used to obtain an optimization, a common experience has been that of realizing that it is very easy to suboptimize. That the answer was unexpected, counter-intuitive, a "little off" the expected (or the way things have been done in the past, presumably because they were optimum). The difference is worth study for even slight suboptimization over 100 years, over hundreds of acres as typical in forestry, can "mount up." Key to the concept of benefits is that there are many different kinds and that this is a total profit maximization effort over an unusually long time and usually over many, many acres.

The concept of R* is fundamental and the meaning and practice ofthis paradigm, as all do, hangs on the objective. The objective is a decision. It requires a risk (for it may be poor or wrong) but not deciding, which I perceive to be the present state throughout most wildland management (even most public decisions), is equally or more risky, i.e., leading to failure.

Our objective, the desired benefits, is

B = DVES where we analyze the units of resource demand D (that can be achieved under normal conditions in the wild) for many groups of people P, for each objective i, over a long planning period (at least 50 years). Similarly we express the relative importance of each unit of demand (a value V) that changes with age and other factors). We then estimate the risk of failing to achieve the demand levels. This gives us, when combined, an "expected value", expected estimate of the number of weighted units needed to satisfy a diverse group of people over time. The concept can be simplified for the single landowner.

The computations are run with financial as well as energetic units for comparison. Assumptions are that actions will be legal and constraints included to represent laws and policies where needed. An enforcement cost is included and a 95% effectiveness of enforcement assumed.

Citizens may be involved in quantifying all of the elements of B* and suggesting ways to reduce C to some small number, at least a value of 1.0.

The emphasis, is on total production, not just that of trees and includes items listed in Table 1.The emphasis is on producing benefits, not just things. The individual products often are economical operations and it seems naive to suggest them. When they are operated (1) within the context of the other products as joint production from land, and (2) as separate across-ownership enterprises, they can be profitable.

Never lost is the concept that the forest, the wildland volume must be managed well and rejuvinated so that it, the capital resource, can remain the productive base for the future. It along with injenuity in presenting the production from the eland, will be that which is sustained. Given elementary assumptions about supply and demand in a world with a growing population (resisting efforts to slow), the demand will stabilize or increase.

The first condition achieves economies of scale, stable employment and its associated economies, and allows yearlong use of equipment, space, and labor. The second condition is novel but not unexpected. It unifies operations, not on the farm or forest, but in the enterprise. The land in effect is rented to serve an enterprise consisting of work on many areas. A honey enterprise, for a simple example, has hives on many ownerships. Centralized marketing, expert advice, low cost supplies, etc. make the enterprise (not the marginally profitable hive owner) viable. In combination with other enterprises (e.g., cattle, ecotourism, medicinal plants, bird watching, timber) the combined enterprises and diverse, stable, supportive, share common resources, and achieve economies of scale and synergism well known by ecologists. These enterprises are akin to populations; the combined system is akin to an ecological community.

Inputs

The system inputs are neither new nor unexpected. The pattern of using the inputs, however, is spatial. Every cell, every land unit has hundreds of factors known and available for it. We do ecological and economic modeling at the land pixel scale - the intent is largely to assure production; the parallel work is to reduce losses and increase or stabilize benefits from that which is produced. The two are linked, for example, in planting the right tree species on the proper site at proper spacing to avoid moisture stress and thus mortality and associated, subsequent insect attack (H. J. Heikkenen, personal comm.).

Landscape ecology concepts are included in new nearness-to or contiguity map layers. (For example, a cell near another cell with water in it is probably as valuable to some plants and animals as actually having water present in a cell.) We load a cell with factors in the cell and also factors not observed but nearby - in three-dimensional space. We never have enough data. We never will. We believe it irrational to act as if we will (Giles et al. 1993). We use available data, of course, but our sampling strategies involve use of alpha levels of 0.2 and tolerable error of 0.10. We use expert systems concepts and attempt to provide continual feedback.

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Last revision January 17, 2000.