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To receive news and publication updates for International Journal of Forestry Research, enter your email address in the box below. This is an open access article distributed under the Creative Commons Attribution Licensewhich permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Logging in natural forests is a vital economic activity in the Brazilian Amazon.
However, illegal and unplanned logging is exhausting forests rapidly. To support public forest planning efforts, we combine spatially explicit data on logging profits, biodiversity, and potential for community use for use within a forest planning optimization model. While generating optimal land use configurations, the model enables an assessment of the market and nonmarket tradeoffs associated with different land use priorities. The datasets used span the entire Brazilian Amazon, implying that the analysis can be repeated for any public forest planning effort within the region.
Forests are being depleted rapidly as adoption of sound forest management practices is still in the incipient stage [ 12 ]. The Brazilian government seeks to manage public forests as a critical component of forest-based sustainable development through implementing a complex balance of multiple and often competing uses [ 3 ].
The Brazilian government estimates that up to 13 million ha of forest concessions could be established within the first 10 years of implementation of the Public Forest Management Law PMFL. Inthe Brazilian Forest Service SFB concluded the first inventory of public lands, a starting point for detailed land use planning within public forests.
The major challenge facing public planners now lies in allocating land across multiple uses to meet multiple objectives, often with little information about socioeconomic and biological conditions.
The objective of this paper is to develop an analytical tool to support this decisionmaking.
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To meet this end, we combine the best available spatially explicit data on expected logging profits, biodiversity, and potential for community use for use within a land use optimization model. While generating optimal land use configurations conditioned upon policy objectives, the model enables an assessment of the market and nonmarket 2060 associated with different land use priorities.
After developing the model conceptually and discussing the data used within the model, we demonstrate the model on aha public forest.
The importance of this study is demonstrated in two ways. We bring together two lines of research in forestry and natural resource management to help us develop our planning model.
First, we 20066 upon the mathematical programming literature on the conservation reserve site selection problem, which generally aims to conserve the maximum number of species at a minimum cost or using a minimum number of reserves [ 4 — 9 ]. Second, we draw upon harvest scheduling and tactical planning problems, such as the optimization of infrastructure e. We begin leei establishing the basic selective logging timber supply problem, where the decision variables determine 206 amount of volume per ha to harvest.
In a given public forest, government seeks to maximize profits from logging. The volume harvested is constrained by the available merchantable timber volume in the forest and the milling capacity of the logging centers surrounding the public forest.
In mathematical terms, The subscript represents individual forest stands within a given public forest. Each stand has an annual lri area denominated in lek. If the stand is harvested, the timber will travel to ds least one of mills located in logging centers denoted by.
There are commercial species which, for simplification, we categorize into timber value classes, denoted by. Represented by 3the total volume annually harvested and directed toward a given center cannot exceed the milling capacity for each logging centerin m 3.
Next, we need to incorporate land uses other than timber concessions into the model. Each stand within a public forest is assumed to have an exclusive land use—a strong assumption given that many areas might have overlapping uses. However, as a result of diminishing returns from management effort for multiple activities within the same stand, landscape-scale multiple use planning is perhaps most efficient when each stand has a unique use [ 13 ].
Equation 1 is then modified and the objective of the program becomes maximizing profits from concessions constrained by a minimum number of stands—or a minimum score—assigned to alternative land uses. In our study, ldi consider community use and biodiversity conservation as possible alternative uses.
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Each stand is also assumed to have a potential coefficient for each land use alternative. In generalized mathematical terms, In this formulation, represents mutually exclusive land use alternatives for each stand. The land use choice for each stand is represented by a series of binary variableswhich take on a value of one if a particular land use is chosen, zero otherwise.
The potential of each stand for each land use is assumed to be known and is denoted by. The parameter is assigned a priori to be either binary 0, 1 or some other score assigned by the public planner, in the case the planner has information about the relative value of different stands for a given land use alternative. This is important for planners because it can accommodate values of noncommercial land uses in a more meaningful way.
Equation 4 maximizes profits from logging within a single year. Equation 5 imposes a constraint guaranteeing no more than a single land use choice per parcel. For simplicity, we assume that constant returns to scale across all land uses and no agglomeration effects between any stand and the spatial composition of the forest. However, we do permit the planner to specify the minimum number of stands allocated to each land use within the forest. Alternatively, the planner can impose a minimum score to be achieved for a given land use by assigning different weights for each land use.
In either situation, expressed in 6represents the policy-driven minimum number of stands or the minimum score across alternative nonlogging land uses.
Many logging centers are located along the southern and western edges of the region. As ofCalha Norte contained 64 million hectares of protected areas. FSF is covered by dense forests and displays little evidence of use and human occupation in the extreme eastern and southern portions [ 14 ]. Sales and Souza Jr. From this map, we estimated the harvestable volume of each timber value class i. As well-planned and executed harvesting on public lands is intended to replace illegal logging on these lands, this capacity estimate is a reasonable starting point for analysis until better data become available.
In the first set of model runs that follow, we first assume that the spatial distribution of mills remains static. In the second set of simulations, we assume that sawmills move into 14 urban centers close to the forest which currently have no lfi, assuming new mills would be built to better take advantage of the legal timber supply from FSF.
We drew upon previous efforts to estimate the spatially explicit profitability of logging [ 15 — 17 ]. These studies created maps identifying forests financially profitable for logging, based on wood prices and logging costs i. From this database, we used interviews from the Calha Norte and surrounding centers.
It is important to note that, because of data limitations, prices and costs are assumed to be constant throughout the analysis, even though regional and local timber markets will be affected if large quantities of legal timber become available.
Three maps of profitability of logging were generated for the three timber value classes considered: Areas forbidden for logging were assigned for harvesting in the leu.
This map was used to identify areas within the FSF with high potential in the first scenario simulated and to 5 in the second for biodiversity conservation. The latter were assumed to be forests potentially being used by forest dwellers in their livelihood strategies in the first scenario simulated and or 3 in the second. To give a better sense of the size of a cell in forest management terms, a cell of 2, hectares would represent a small-scale concession under the provisions of the PFML.
In other words, the economic model maximizes the annual profits originated from the annual allowable area for harvest of For simplicity, we assume that the growth and yield of the harvested forest would be sufficient to restore the same merchantable volume in each timber volume class within each stand at the beginning of the second cutting cycle. Three competing land uses were considered: We simulated the production possibility frontier PPF and investigated the tradeoffs among different land uses for three scenarios using existing logging centers and also when the centers move to cities closer to FSF: The second scenario investigates the impacts of varying the weights for stands potentially assigned to nonlogging land uses.
In the case of community use, we assigned to stands in which older forest fires were identified — a weight equal to two-thirds the weight assigned to stands indicating more recent forest fire activity — equal to 2 and 3, resp.
We also assigned an increasing gradient of weights from 1 to 5 for biodiversity conservation as one moves from the western to eastern portions of FSF, since the eastern portion of the forest contains higher biodiversity, according to the ISA biodiversity priority map. In a full-blown application of the planning model within an actual forest planning context, it would be possible to use participatory techniques from multicriteria decision analysis to determine the relative weight of competing land uses.
In the two multiple use scenarios investigated, the potential for logging was considered equal to 1 for any stand in which the estimate of logging profitability was greater than zero, and zero otherwise. All scenarios considered in this work assume a planning horizon equal to 40 years, which is the length of the contracts that have currently been established between the Brazilian Forest Services and concessionaires. Currently, annual profits from logging represent one of the primary variables considered by the Brazilian Government in decisionmaking to grant concessions in public forests.
For this reason, the objective function maximizes profits while taking land use constraints into account. However, to simulate the PPF curves and investigate the tradeoffs among competing land uses, we used annual profits i. We then use the NPVs in the different scenarios simulated to determine the marginal opportunity arising from decreasing the area logged due to increasing requirements for alternative land uses.
Every three years the interval represented byBrazilian law requires independent audits of concessionaire performance within the concessions. Due to the lack of suitable data, audit costs were assumed to be equivalent to certification audit costs, which vary depending on the size of the management unit. We also estimated the rent distribution between concessionaires and the government.
The remaining profits are assumed to go to the government via a royalty mechanism that does not affect harvest decisionmaking. The software used in the analyses was GAMS For the largest problem that includes all land uses, the model solves for 13, continuous variables and binary variableswith 14, constraints. In this section, we report a series of results from the model applied to FSF. While the results are useful for planning in FSF, our primary intention is to demonstrate the capability of the model to provide useful information to forest planners.
The remaining stands cannot be harvested due to high slopes. In the remaining area, only high-value species would be logged. Processing would be performed by mills located in only three centers: Obviously, as the number of stands assigned to alternative land uses such as biodiversity conservation and use by communities is increased, the number of stands logged, timber volume harvested, and profits from logging decrease. Figures 2 and 3 show what happens to the number of stands assigned for logging and harvested volume when logging is performed by firms located in the current logging centers Figures 2 a and 3 a and if mills move to closer cities Figures 3 a and 3 b.
For decisionmaking purposes, it is important to be able to estimate the royalty rate that permits the logging firm to just satisfy participation constraints. Our assumption in the model is that the government is able to use an unspecified nondistortionary royalty instrument that does not influence harvest behavior to extract rents to the point that concessionaires just earn profits equivalent to those of operating legally on private lands. Figures 4 a and 4 b show the share of the profits for loggers and government under this assumption.
Figures 4 c and 4 d show results when weights are differentiated across stands with noncommodity use potential. NPV decreased with an increase in the number of stands dedicated to communities. Same effect is valid for biodiversity conservation.
Figures 5 c and 5 d show the frontier by applying differentiated weights for stands under these alternative uses, respectively, for community use and biodiversity conservation, using as a starting point the same NPV in the UL scenario.
The curves traced out mainly Figures 5 a and 5 b due to the similar comparative scale are useful to assess the effects of nonlogging land use alternatives over the NPV generated by logging. In the case of FSF, the decrease in the NPV caused by an increase in the proportion of the State Forest dedicated to community use is larger than the decrease caused by biodiversity conservation, ceteris paribus.
This is consistent with reality in the case of FSF since areas with higher potential for community use are located near roads and rivers that help access the forests roads and riverswhich also creates higher profitability levels for logging.