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Angelson et. al. 2009

Created By: Timahje Keesee


Policies to effectively reduce deforestation are discussed within a land rent (von Thünen) framework. The first set of policies attempts to reduce the rent of extensive agriculture, either by neglecting extension, marketing, and infrastructure, generating alternative income opportunities, stimulating intensive agricultural production or by reforming land tenure. The second set aims to increase either extractive or protective forest rent and—more importantly—create institutions (community forest management) or markets (payment for environmental services) that enable land users to capture a larger share of the protective forest rent. The third set aims to limit forest conversion directly by establishing protected areas. Many of these policy options present local win–lose scenarios between forest conservation and agricultural production. Local yield increases tend to stimulate agricultural encroachment, contrary to the logic of the global food equation that suggests yield increases take pressure off forests. At national and global scales, however, policy makers are presented with a more pleasant scenario. Agricultural production in developing countries has increased by 3.3–3.4% annually over the last 2 decades, whereas gross deforestation has increased agricultural area by only 0.3%, suggesting a minor role of forest conversion in overall agricultural production. A spatial delinking of remaining forests and intensive production areas should also help reconcile conservation and production goals in the future.

climate Reducing Emissions from Deforestation and Forest Degradation tropical forests protected areas yield
Most tropical deforestation results from trees being chopped down to generate space for crops and cattle (1). Reducing deforestation therefore means slowing down the expansion of agricultural land into forests. At the same time, the world needs to increase its agricultural output to feed the 923 million people who go to sleep hungry every evening (2), keep pace with a still growing population, and meet increased food demand arising from higher incomes and concomitant changes in eating habits. Are we then facing an unpleasant choice between “conserving the forests” and “feeding the hungry”?

(1)This article approaches this question from two different angles. First, we take a land rent (von Thünen) approach and ask what policies are effective to halt deforestation and how these will affect agricultural yield and thereby total output. Second, we use a modified global food equation and ask if yield-enhancing policies will reduce deforestation or make forest conversion more attractive.

Causes of deforestation at different levels can be distinguished (3). First, the deforestation agents (individuals, households, or companies) and their characteristics and activities must be identified. Second, agents’ choices are influenced by external factors (decision parameters) such as prices, market outlets, technologies, and agroecological conditions—the immediate causes. Third, these parameters are in turn affected by broader national and international macrolevel and policy instruments—the underlying causes. A different set of explanations concerns why particular policies are pursued, i.e., the political economy of deforestation. This article focuses on the policies rather than the politics, but poor governance and corruption will make even the best-intended policies ineffective.

The microeconomics of land use, dealing with the first two levels, takes as its starting point that land is allocated to the use with the highest land rent (surplus). A key determinant of land rents is location and distance to markets, which is the original von Thünen approach (4). We consider a simple model where land has two uses: agriculture and forest (5). The real world presents a continuum of land uses between agriculture and forest, e.g., agroforestry and silvopastoral systems, and including those in more disaggregated, empirical studies is important to capture their different provision of environmental services. Our dual model is therefore an analytical simplification, but sufficient to capture key policy issues.

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Agricultural Rent

Agricultural rent can be defined as: ra = paya – wla – qka − vad. Agricultural production per hectare (yield) is given (ya). The produce is sold in a central market at a given price (pa). The labor (la) and capital (ka) required per hectare are fixed, with input prices being the wage (w) and annual costs of capital (q). The fixed wage assumption implies that labor can move freely in and out of agriculture. Transport costs are the product of costs per kilometer (va) and distance from the center (d). The rent declines with distance, and the agricultural frontier is where agricultural expansion is not profitable anymore: ra = 0. Thus the frontier is defined at d = (paya – wla – qka)/va.

This model, illustrated in Fig. 1, yields several key insights into the immediate causes of deforestation. Temporarily ignoring the forest rent, deforestation will take place up to the distance A. Higher output prices and technologies that increase yield or reduce cost make expansion more attractive; i.e., they move the agricultural rent curve to the right. Lower costs of capital in the form of better access to credit and lower interest rates pull in the same direction. Higher wages, reflecting the costs of hiring labor or the best alternative use of family labor, work in the opposite direction. Reduced access cost (va), for example, new or better roads, also provides a stimulus for deforestation.

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Fig. 1. Agricultural and forest rents and forest rent capture.
This simple framework served as the basis for a number of empirical investigations. A survey of >140 economic models of deforestation finds a broad consensus on three immediate causes of deforestation: higher agricultural prices, more and better roads, and low wages and shortage of off-farm employment opportunities (3, 6).

The basic model can be extended in several directions, for example, to allow farmers to be capital and/or labor constrained, to allow some markets to be missing or imperfect, to include uncertain tenure, to permit market feedback, to include the temporal dimension, and to account for multiple production systems and their interactions (7, 8).

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Forest Rent

(2)Forest rent is more complex, reflecting the different nature of products and services generated by standing forests. We distinguish between three main types: first, private forest products, such as timber and a large number of nontimber forest products (NTFP); second, local public goods, such as water catchment and pollination services; and third, global public goods, such as carbon sequestration and storage and biodiversity maintenance. We refer to the first type as extractive forest rent and the latter two as protective forest rent. Total forest rent is given by rf = (ptyt – wlt – qkt – vtd) + plyl + pgyg.

The extractive rent increases due to higher timber and NTFP prices (pt); technological progress (yt, lt, kt); and lower labor (w), capital (q), and transport (vt) costs. Higher values of local (pl) and global (pg) forest public goods increase the overall forest rent further and should lead to less forest being put under agricultural use. However, such an outcome depends critically on that rent being captured by the actual land users, as returned to below.

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Agricultural Policies

Reducing Overall Agricultural Rent. Understanding variation in agricultural rent is key to understanding differences in forest cover and deforestation rates across the tropics. Keeping agricultural rents low can be very effective in saving forests. Wunder (9) refers to this as “the ‘improved Gabonese recipe’ for forest conservation,” where central ingredients are heavy taxation of export agriculture, neglect of rural roads, and limited support to smallholders. The oil rent that Gabon enjoyed was concentrated in urban areas, resulting in massive urbanization and forests being left alone to grow.
Such policies run counter to mainstream policy recommendation for agricultural and rural development (10) and are in conflict with objectives of reducing poverty and boosting agricultural production (11). As a general conservation policy recommendation a discrimination against agriculture is politically unacceptable, although policies for decades have had a strong antirural and antiagriculture bias in many poor countries (12).

Economic Development. Agricultural rent can be lowered by raising the opportunity cost of labor. A country's forest cover over time might follow a pattern known as the forest transition (FT) (13, 14). FT describes a sequence where forest cover first declines and reaches a minimum before it slowly increases and eventually stabilizes. A major FT driver is higher off-farm wages and better employment opportunities that pull labor out of agriculture and forested areas (out-migration), referred to as “the economic development path” (15).
Economic development is, however, not a policy instrument but the aggregate outcome resulting from, inter alia, a constellation of policies. Targeted policies can be used to stimulate nonfarm employment in rural areas, but they do not guarantee forest conservation outcomes. Higher nonagricultural incomes might be deployed to invest in foresting-depleting activities such as cattle ranching (16). A win–win outcome seems more likely in labor-intensive agricultural systems than in capital-intensive ones (17). In the latter, any stimulus to the local economy will help relax capital constraints that currently slow down an otherwise profitable forest conversion.

Targeting Intensive Agriculture. An important extension of the simple von Thünen model is to distinguish between intensive (lowland) and extensive (upland or frontier) agriculture, where “intensive” is understood to mean intensive in productive inputs other than land. Spatially targeted policies to stimulate intensive agriculture can be an effective forest conservation policy. Improved small-scale irrigation systems in the lowlands of the Philippines pushed up labor demand and wages and pulled labor out of a more extensive agricultural sector in the uplands, reducing forest clearing by almost 50% (18, 19). Additionally, an output market effect might pull in the same direction: Increased supply from the intensive sector exerts downward pressure on domestic agricultural prices, further reducing the rent of extensive agriculture (20). Policies aimed at such targeted agricultural intensification have been dubbed reduced emissions agricultural policy (REAP) by Rudel (21) and can include credit programs, subsidized fertilizers and seeds, assistance in marketing, and agricultural extension programs.
Although a favorable forest outcome might be the most likely scenario, it is not guaranteed. If the dominant crop in intensive agriculture is traded internationally, a supply increase will have small effects on the price (for the benefit of farmers in that sector). If policies promote labor-saving technological change, the labor pull effect may be weak or even reversed (17). In addition, the higher profit in intensive agriculture can be used to clear new land for extensive crops and cattle production. These conditions were met in Sulawesi, Indonesia in the 1990s: Mechanization of lowland rice cultivation freed up labor, and profits were used to expand cocoa cultivation in the forested uplands (22).

Ignoring Frontier Agriculture? The above policies can be accused of ignoring agriculture in remote forested areas, where poverty rates typically are higher (23). Is it possible to raise productivity, increase output prices by better market access, and provide input support to extensive agriculture without increasing the pressure on natural forests? A summary of more than a dozen studies on the impact of technological changes on tropical deforestation (17) concluded that “trade-offs and win–lose between forest conservation and technological progress in agriculture in areas near forests appear to be the rule rather than the exception” (page 9).
Nevertheless, potential win–win opportunities exist for certain technologies and market conditions. As most farmers face labor and/or capital constraints, new labor/capital-intensive technologies may slow rates of deforestation, even if they simultaneously increase profitability. However, precisely because farmers are labor/capital constrained, we can—as a rule—expect them to prefer labor/capital saving technologies. Thus, with some important exceptions, we are not likely to get the type of technological change that would save the forests (17). For example, pasture intensification is technically possible throughout Latin America, but is not typically adopted before forests have been depleted (24). This finding confirms Boserup's hypothesis, namely that farmers will exploit the extensive margin before the intensive one (25).

(3)A more probable win–win route to assist remote farmers would be in situations where they are involved in both intensive and extensive production systems, the extensive system being the principal source of deforestation. In Zambia, high-yielding maize varieties introduced in the 1970s discouraged extensive shifting cultivation and slowed down deforestation (26).

Roads. Establishing new or improving existing roads opens up new areas, reduces transport costs, provides market access, and thereby makes deforesting activities more profitable. Roads are among the most powerful factors contributing to deforestation across the tropics (27). In the Brazilian Amazon, 95% of all deforestation occurs within 50 km from highways or roads (28). This fact led Eneas Salati, a respected Brazilian scientist, to conclude that “the best thing you could do for the Amazon is to bomb all the roads” (29).
Although roads are critical, some caveats are in order. First, some early studies establishing a negative correlation between distance to roads and rate of deforestation tended to overstate causality. Some roads are built precisely because an area has been cleared and settled, rather than vice versa. Second, roads are particularly important at the early stages in the FT to open up new areas for human activity (30). At later stages, roads can assist in agricultural intensification and economic development that lessen the pressure on forests and provide incentives and increase the capacity for better forest management. Third, the role of state-run road building (together with other large-scale projects such as colonization programs) has weakened since the 1980s (31). Yet, no forest conservation policy can be considered comprehensive unless it provides clear guidelines on investments in transportation infrastructure.

Property Rights. The analysis of the deforestation impacts of property rights must distinguish between exogenous and endogenous tenure insecurity (5). If exogenous, the relevant question is, What is the impact of tenure insecurity on deforestation? If endogenous, the relevant question is, How do land users’ actions to increase tenure security affect deforestation?
The impact of exogenous tenure insecurity in an extended von Thünen model is straightforward, but opposite of what is commonly assumed: Land reforms that give higher tenure security increase the net present value of land clearing and therefore spur deforestation (7, 32). This effect can be modified by the “land degradation–deforestation hypothesis” (17): Insecure tenure might lead to less land investment and more soil exhaustion, thus increasing the need and/or incentives for cutting down more forest to replace degraded land. The net impact of higher tenure security is therefore context specific.

Tenure is also endogenous, and land users take actions to increase tenure security (33). Forest conversion often, according to both customary and statutory law, establishes or strengthens existing land rights. Deforestation therefore becomes a strategy for establishing title. This might lead to a “land race” or a “race to the frontier,” which refers to forest being cleared prematurely to establish property rights. The deforestation push has been discussed particularly in relation to the Amazon (32, 34, 35), e.g., where forest clearing is used to strengthen claims in conflicts between landowners and squatters (32). In Ecuador, forest land was so quickly converted to pasture to secure rights that farmers could not stock the land with cattle (36).

Impacts on Agricultural Production. For any given yield, the more successful the policy is in halting agricultural expansion and reducing deforestation, the larger the reduction in production. The central question is therefore what happens to yield under different policies (Table 1). Policies that depress agricultural rent present the strongest trade-off between conservation and production. The negative impact is smaller if discrimination can be geographically limited to frontier agriculture or to typical deforesting crops. Positive stimulus to intensive agriculture should increase yield and possibly also expand intensive production and lead to a contraction of extensive agriculture. Intensive agriculture can, however, also expand into forested areas (e.g., oil palm in Indonesia).
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Table 1. Overview of forest conservation policy options
Selective support to extensive agriculture, if successful in reducing deforestation, also has the potential to yield win–win outcomes. Higher agricultural production in itself can help to achieve both objectives as it puts downward pressure on local or domestic output prices and makes agricultural expansion less profitable. Reforms to enhance tenure security should contribute to higher yield, as farmers are more willing to invest in the land (37). This could therefore, again if successful in forest conservation, yield win–win outcomes.

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Increase and Capture of Forest Rent

Increasing forest rent over time is the second route to protect forest, “the forest scarcity path” of the FT (15). Higher demand for and limited supply of forest products stimulate forest cover stabilization and regrowth. This extractive forest rent can be influenced in similar ways to the agricultural rent, e.g., through tax policies and marketing arrangements that affect prices of timber and other forest products or through promotion of new technologies.

Large tracts of tropical forests are, however, characterized by weak, unclear, and contested property rights, making them de facto open access (38). Land users then have no incentives to include any forest rent in their decisions. If private property rights to the forests are established, we move from point A to point B in Fig. 1. Higher forest extractive rent then implies more forest will remain as forest. Factoring in degradation, the effects are more complicated. According to the standard Faustmann model, higher timber prices will shorten the rotation period and thereby reduce the average forest carbon stock (39).

Whereas the forest scarcity path historically has been linked to higher extractive forest rent, in the future it could be driven by increases in the protective forest rent. Because of its public goods nature, an increase in the protective rent has no impact on deforestation unless land users can capture some share if it. There are two principal ways of “internalizing the externalities”: (i) moving decisions to a higher scale and (ii) creating a market for the public goods.

In the popular debate assigning individual property rights to forest is commonly put forward as a solution to excessive deforestation. This reform in itself will not solve the problem of local and global public goods (externalities), but clear and secure property rights—either at the individual or at the community level—are a necessary step toward establishing systems for payments of environmental services (PES). It will also encourage more sustainable management of forests compared with an open access regime, with positive effects on degradation and carbon fluxes.

Community Forest Management (CFM). Within our framework, CFM is an attempt to move decisions from the individual level to the community level to incorporate community-level negative externalities from deforestation (point C in Fig. 1). The CFM experience is mixed. In a metaanalysis of 69 cases of CFM (40), 58% were considered successful on the basis of ecological sustainability criteria (the most typical measure was “improved forest condition”). Another large comparative study of 80 forest commons in 10 countries found that greater rule-making autonomy at the local level is positively correlated with high forest carbon levels (41). However, an analysis of the central Himalaya in India finds no difference in forest cover between village- and state-managed forest, although the costs per hectare are seven times higher for the latter (42).
Nobel laureate Elinor Ostrom has for the past 2 decades been demonstrating how different attributes of users, institutions, resources, and context may or may not facilitate local cooperation (43, 44). There are several reasons why communities might be effective managers. They have better knowledge about the local forest and its users and uses compared with the state, making policing easier. Communities may also apply a different set of sanctions, as resource management is embedded in larger social systems (45, 46). However, achieving collective outcomes is difficult, particularly when the user group is large, heterogeneous, and poor and the forest benefit flow and economic environment are unstable (47). In addition, central government policies often have not been supportive, and the most valuable forest resources tend to remain outside community control (48).

Reducing Emissions from Deforestation and Forest Degradation in Developing Countries (REDD) and PES. The current international debate focuses on REDD as the main vehicle for forest conservation. The key idea of REDD is to create a multilevel (global–national–local) PES system for the carbon sequestration and storage services of forests (49). Whereas REDD promises to offer significant, cheap, and quick reductions of greenhouse gas emissions from forests (50), a number of obstacles must be overcome to have a significant impact on the ground. At the international level and in global climate negotiations, questions of funding and carbon market integration; reference levels (including developing country responsibilities); and standards for monitoring, reporting, and verification (MRV) must be agreed on (51). Similarly, at the national level effective institutions must be established and policies implemented to channel payments to effectively incentivize and compensate forest users for opportunity and transaction costs (52).
Many actors will be seeking REDD rents, and “rent seeking” is the root cause of corruption (53). Governance problems and widespread corruption will limit the effectiveness and the scope of possible actions for REDD, as it will for the other policies discussed. Implementing effective PES schemes also assumes that the land and carbon rights have been settled. At least in the short to medium term, using PES as an instrument to achieve REDD will be more difficult than commonly assumed among policy makers (52). National REDD strategies will have to rely heavily on non-PES policies (such as those discussed in this article).

Impacts on Agricultural Production. Policies to increase forest rent are likely to have negligible direct effects on yield from existing agricultural land. But the supply effect from less land being available for agriculture may partly be offset as prices can be pushed up and encourage intensification. Further, average yield can be expected to increase because the least productive land areas are taken out of production (or not included through continued deforestation). Such a search for the most productive land has played an important role in forest transition in Europe (14).
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Protected Areas (PAs)

Forest protected areas (PAs) within International Union for Conservation of Nature (IUCN) categories I–VI make up 13.5% of the world's forests (54), the share being significantly higher (20.8%) for rainforests (tropical lowland evergreen broadleaf forests). There is a broad consensus in the literature that (i) the degree of protection is <100%, but (ii) rates of deforestation within PAs are lower than outside them, also after controlling for “passive protection” (PAs are often located in remote areas with lower deforestation pressure) (55, 56).

A study of PAs in Costa Rica found substantial passive protection: Without controlling for observable covariates, PAs reduce deforestation by 65%; the degree of protection drops to 10% after controlling for differences in location and other characteristics (57). A methodologically similar study from Sumatra finds the difference between deforestation rates in PAs and wider areas during the 1990s to be 58.6%; this difference falls to 24% after propensity score matching (58). None of the studies finds any significant leakage (deforestation activities shift from inside to outside PAs), although the methods required to estimate leakage are complex and go beyond simple comparison of the (adjusted) deforestation rates inside and outside PAs.

Various types of PAs have also significantly reduced deforestation in the Amazon. Indigenous lands occupy one-fifth of the Brazilian Amazon, and Nepstad and coauthors (59) find the inhibitory effect for the period 1997–2000 to be 8.2 (the deforestation ratio between 10-km-wide strips of land outside and inside the PA border). These and other results led a World Bank forest policy review (60) to suggest that “protected areas may be more effective than is commonly thought” (page 126).

There is less consensus on other aspects related to PAs, e.g., the livelihood benefits and to what extent an inclusive or an exclusive approach of local communities is more effective when it comes to conservation effectiveness (61). This lack of consensus also holds for the integrated community development programs (ICDPs), which can be seen as a mix of a traditional “park and fence” approach and an attempt to provide alternative income opportunities to reduce agricultural rent and nonsustainable forest extraction. One study (62) concludes that “it is not that the principle of linking protected area management with local social and economic development is flawed, [but] the expectations and implementation that have been problematic” (page 514). The alternative livelihoods created were often small compared with the income from deforestation and forest degradation, and the benefits were not made conditional on forest conservation (as they are in a PES system).

Successful PAs can be expected to have similar effects on agricultural yield as policies to increase and capture forest rent. However, one can hypothesize that a PA approach will lead to higher loss of agricultural production per hectare forest saved, because there is less assurance that the least productive land is saved from agriculture.

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The Global Food Equation

The global food equation (GFE) is a simple identity that links population and food consumption per capita with agricultural yield and land area:

(4)Put simply, without an increase in yield, agricultural area must expand to feed a growing population and meet higher per capita food consumption. GFE has been used to draw conclusions about the need for higher yield to spare forests (63). Waggoner and Ausubel (64) refer to it as “the popular image of farming's encroachment on forests” (page 241). This line of reasoning also underlies the Borlaug hypothesis (17), which suggests that the Green Revolution has had a positive effect on forest cover.

Using the GFE logic, Balmford and coauthors (65) predict that the agricultural land area in developing countries will increase by 2–49% between 2000 and 2050, depending on assumptions of population growth (23% being the medium variant scenario). This scenario assumes an extrapolation of current yield trends (with a mean of 1.13% per year). A more optimistic scenario with an annual yield increase of 1.53% virtually eliminates the agricultural area increase.

The GFE provides no direct link between agricultural and forest areas, nor does it account for two facts: Much agricultural production is not food and countries trade. Moving to the national level and further decomposition gives a national deforestation equation (NDE):


(5)Agricultural yield is just one of many factors affecting deforestation, and changes in yield have indirect effects on these factors. First, countries increasingly trade in agricultural products. The trade intensity (trade/GDP ratio) has increased from 60 to >100% since the early 1970s (66). Developing countries as a group have over the same period moved from being net agricultural exporters to net importers. Higher yield boosts the competitiveness of domestic agriculture and raises self-sufficiency.

Second, a lower share of agricultural output being for foodstuffs (Δ food share) can boost deforestation, as illustrated by the boom in biofuel crops. Oil palm expanded by 1.9 and 3.0 million ha in Malaysia and Indonesia, respectively, during the period 1990–2005 (67). Most of the smallholder crops on forests cleared in Indonesia, following the economic crisis in the late 1990s, were not food crops (e.g., rubber) or not staples (e.g., cocoa, pepper, and coffee) (68). Higher yield can reduce the food share, as food demand is typically more price inelastic than demand for nonfood commodities.

Third, forest, cropland, and pasture are not the only land uses; large areas of fallow, savannah, bush, and other land categories are available for agricultural expansion (Δ ag/forest ratio is not stable). Waggoner and Ausubel (64) find changes in cropland and forest area to be uncorrelated in the period 1900–1995, although this might partly be due to poor data for many countries. Their average “encroachment factor” (share of agricultural expansion into forests) is assumed to be 1/3, but is also highly variable across crops and countries. Fifty-five to 60% of the recent oil palm expansion in Indonesia and Malaysia was at the expense of forests (67).

Other potential impacts of higher yield include a price effect on food consumption per capita (inelastic food demand suggests this effect will be small) and a Malthusian effect (higher population growth due to increased food consumption).

The GFE, NDE, and similar identities are useful in providing a consistent accounting framework, but are also potentially dangerous to use as predictive models and for policy analysis if they do not factor in how a yield change impacts the other factors through behavioral and market changes. This change by moving from a mechanical simulation to empirical analysis using a regression model is illustrated by the results of Ewers and coauthors (69), using country-level data for the period 1980–2000. If “perfect land-sparing” yield change were occurring, the land-yield elasticity should be −1; i.e., all other factors in NDE remain constant. The authors find a much lower elasticity: −0.152 (t = −1.78) for developing and −0.089 (t = −0.57) for developed countries due to effects such as those discussed. The impact on forests (not included in their analysis) would be even smaller as long as the encroachment factor is below unity.

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Discussion and Conclusion

The starting point of the von Thünen model is the plot level, and deforestation is framed as a contest between agricultural and forest rents. The GFE starts at the other end of the scale and asks how much land we need to feed the global population. The von Thünen model is at one extreme where demand is perfectly elastic (prices fixed), whereas the GFE assumes demand to be perfectly inelastic (quantities fixed). They present two contrasting views on the forest impact of higher agricultural yield, but they converge when modified to include behavioral and market effects. Whereas overall food demand may not respond much to price changes, this does not necessarily hold for particular crops or for nonfood agricultural products where substitutes are available.

The demand elasticity and thereby the forests impact of higher yield also depend critically on the scale of analysis. Angelsen and Kaimowitz (17) conclude that “situations that are win–lose [agricultural production and forest conservation] at the local level may be win–win at the global level” (page 400).

An illustration of the limited trade-off between production and conservation at higher scales is given by comparing recent agricultural production and area increases in developing countries. Crop and livestock production grew by 3.3–3.4% per year during the period 1985–2004 (66). Gross annual deforestation (1990–2005) for agricultural uses represents ∼0.3% of the total agricultural area (66, 70, 71). Because productivity of cleared forest land can be expected to be well below average productivity (production is less intensive, and most productive land is already cleared), these numbers suggest that only a small share (<<10%) of the agricultural output increase has come from deforestation.

REDD is currently being promoted as a low-cost climate mitigation option. The report that underlies the Stern review (50) and the Eliasch report (72) finds the opportunity costs (foregone agricultural rent and logging revenue) of completely eliminating deforestation in eight countries (accounting for 6.2 million ha of annual deforestation, about half the global number) to be approximately USD 6.5 billion per year (73). Due to increasing marginal costs, spreading a 50% reduction across all deforesting countries is significantly cheaper. Other studies such as Kindermann and coauthors (74) have cost estimates in the range of USD 17–28 billion for a 50% global reduction. These numbers include REDD rents to developing countries, which are not true economic costs but transfers and typically inflate cost numbers by a factor of ≥3 (51). Yet, the relatively low opportunity costs of avoided deforestation, particularly for the initial reductions, suggest the conflict between production and conservation is modest.

At the national level, higher volumes of agricultural trade have delinked domestic and local consumption from production and deforestation. Moreover, high rates of deforestation for several decades have made forested areas recede, frequently into relatively inaccessible areas. The issues of forest conservation and agricultural production are therefore becoming increasingly spatially delinked.

(6)In summary, at global and national levels policy makers are only to a limited degree presented with a trade-off between conserving the forests and feeding the hungry. Potential conflicts between production and conserving forests do, however, exist at the forest margins. Stimulating agriculture in forest-rich areas through, for example, better technologies, improved roads, and more secure tenure to “reduce the need for new agricultural land” is a highly risky conservation strategy. Agricultural policies that target low-forest areas, or crops and production systems that are unsuitable at the agricultural frontier, are more likely to reduce pressure on forests. Such policies are complementary to, and will increase the effectiveness of, efforts that more directly target forest conservation: protected areas and institutional arrangements and payment mechanisms that enable land users to capture a higher share of the local and global benefits provided by tropical forests.

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Materials and Methods

This article is based on an extensive review of the deforestation literature, in particular several metaanalyses and comparative studies. These studies include a review of economic deforestation models by Angelsen and Kaimowitz (3, 6), two comprehensive metaanalyses of deforestation studies by Geist and Lambin (27) and Rudel (31, 75), a policy analysis by Chomitz and coauthors (60), and a comparative study on the impact of agricultural technologies on deforestation by Angelsen and Kaimowitz (17).
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Manson et. al. 2007

Created By: Timahje Keesee


We combine mixed-methods research with integrated agent-based modeling to understand land change and economic decision making in the United States and Mexico. This work demonstrates how sustainability science benefits from combining integrated agent-based modeling (which blends methods from the social, ecological, and information sciences) and mixed-methods research (which interleaves multiple approaches ranging from qualitative field research to quantitative laboratory experiments and interpretation of remotely sensed imagery). We test assumptions of utility-maximizing behavior in household-level landscape management in south-central Indiana, linking parcel data, land cover derived from aerial photography, and findings from laboratory experiments. We examine the role of uncertainty and limited information, preferences, differential demographic attributes, and past experience and future time horizons. We also use evolutionary programming to represent bounded rationality in agriculturalist households in the southern Yucatán of Mexico. This approach captures realistic rule of thumb strategies while identifying social and environmental factors in a manner similar to econometric models. These case studies highlight the role of computational models of decision making in land-change contexts and advance our understanding of decision making in general.

agent-based model bounded rationality decision making land change landscape management
Land-change science (LCS) is critical to research on sustainability in coupled human–environment systems (1, 2). Land change results from interactions among social systems, ecological dynamics, and actors, such as households or firms whose behavior is the proximate cause of land change. LCS therefore relies on social science studies, field-based studies of the environment, and remote sensing of land change. The LCS community stresses the importance of developing integrated computer models that combine empirical data with theories of actor behavior to explore land-change processes. These models give insight into the drivers of land-change processes and offer a mechanism to study plausible future trajectories of change and their social and environmental implications.

(1)Unmet challenges in developing integrated models of land change suggest the need for a greater emphasis on individual or household-level decision making. Methodologies that aggregate microlevel behaviors may not capture important aspects of individual decision making (3, 4). Models must work with sufficiently fine-scale data, such as the combination of remote sensing and household interviews, to describe actor practices on the ground, but capture regional land change (5). Few modeling methods effectively represent interactions among actors, society, and the environment at multiple spatial and temporal scales (6). Similarly, many models do not easily bridge the gap between quantitative and qualitative aspects of individual decision making. In sum, land-change models face challenges in micro–macro integration, handling spatiotemporally explicit data, capturing human–environment relationships, and bridging the qualitative–quantitative divide.

Beyond these immediate needs, a greater challenge lies in integrating differing perspectives on individual decision making to enhance our ability to model land change (7, 8). In particular, rational choice theory, expressed as perfect rationality, is being extended through alternatives, bounded rationality in particular. Perfect rationality offers elegance and analytical tractability by assuming decision makers are utility maximizers who use perfect computation and possess complete information on alternatives (9). Bounded rationality weakens these assumptions to better model individuals who face limits on information and computation (10). Boundedly rational agents satisfice, or make suboptimal yet acceptable decisions, or maximize under limits (11–13). These limits imply that agents use decision strategies of limited complexity, such as “rules of thumb” (14, 15) and learn from experience by extending current strategies to new situations (16, 17). A key challenge in comparing theories of perfect rationality and bounded rationality against empirical data is developing testable models. Perfect rationality is typically mapped by econometric research onto statistical approaches (18, 19). Less attention has been given to developing testable models for bounded rationality given its relatively recent emergence, but various viable approaches exist (11, 13, 16, 20, 21). A final challenge is reconciling models of bounded rationality and perfect rationality in a way that recognizes that each approach captures different facets of the same decision-making process.

(2)We examine how agent-based modeling provides a framework for combining modeling and mixed-methods research to represent different forms of rationality, integrate micro–macro processes, use spatiotemporal data, represent human–environment interactions, and blend qualitative and quantitative approaches. An agent-based model simulates adaptive, autonomous entities (or agents) that draw information from their surroundings and apply it to decisions and behavior. Agent-based models of land change are used in contexts ranging from urban growth to deforestation (22, 23). In contrast to modeling approaches that aggregate decisions of many actors, agent-based models examine the decision making of separate actors, such as individuals or households, as locally interacting, autonomous, and heterogeneous entities (24).

(3)Here, we describe the results of using mixed methods and integrated modeling to examine household decision making in land change. In a reforesting landscape in Indiana, we examine the effects of uncertainty, limits to information, preferences, and future time horizons. For a deforesting landscape in Mexico, we explore the use of agent-based modeling to shuttle between representing decision making as individual rules of thumb versus examining broad social and environmental factors. We discuss the implications of these findings, trace future research directions, and complement the discussion of methods and materials developed throughout this article.

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(4)We examined the benefits of combining integrated modeling and mixed-methods research to examine decision making by analyzing forest and agricultural dynamics in two regions that share the same time zone, but are largely dissimilar. First, we explored household-level land-management decisions in south-central Indiana, an area that experienced large-scale deforestation following initial settlement in the 19th century, but then experienced net reforestation from ≈1900 onward. Second, we examine the southern Yucatán peninsular region (SYPR) in Mexico, home to semihumid tropical forests undergoing “slash-and-burn” or extensive agriculture against a background of globalization, neoliberal national transformation, and locally conflicting goals of conservation and development.

There is a substantial amount of research regarding land use in both foresting and deforesting systems. Common proximate causes of deforestation have been identified for tropical regions, typically characterized as the economics of resource extraction coupled with mixed market and subsistence agriculture (4, 25). Alternatively, reforestation has been linked to the abandonment of marginal agricultural areas and increases in prices for timber products (26) along with changes in landowner preferences (27). In both study areas, we find evidence of reforestation and deforestation for specific forest types and, using mixed methods within an agent-based model framework, we demonstrate that, while bounded rationality is a key form of decision making for individuals, we can also usefully make assumptions that fall under the aegis of perfect rationality. In the Indiana case, we started with the assumption that each household maximizes utility and explored how households vary in boundedly rational ways. In the Mexico case, we found that models of bounded rationality and perfect rationality produce similar results in aggregate, whereas the former can also disaggregate the rules of thumb used by individuals.

Actors who were represented as satisficing and possessing imperfect information and cognition produced good model fits against actual multitemporal land-cover data in each study area. While researchers have long known that actor heterogeneity produces complex local landscapes and that household decision making modeled as perfectly rational will ignore aspects of individual decision making (13), it is a challenge to capture these features of decision making in a computer model. Agent-based modeling represents features of bounded rationality, such as heterogeneity, learning, and limited information and computational capacity (28, 29). Agent-based models of land change can extend LCS by explaining and replicating real-world land-change dynamics at the level of individuals while also dealing with the challenges of scalar integration, spatiotemporal data, human–environment interaction, and the qualitative–quantitative divide. Agent-based modeling provides an explicit basis for the comparison of results gleaned from multiple methods and sources, such as contrasting the results of laboratory experiments with historical land use or linking rules of thumb, econometric modeling, and computational intelligence. This integrated approach allows us to understand how individual decision making exists at the interface of individual traits and broader environmental and human contexts. This work also demonstrates the importance of linking data and theory via empirically specified, theoretically driven models of human decision making.

Reforestation in South-Central Indiana. South-central Indiana has seen net reforestation, but, importantly and in contrast to literature focused on linkages between population and deforestation, this reforestation occurred during a period of increasing population density (see also ref. 30). Analysis of aerial photography (used in lieu of satellite imagery to go further back in the historical record) for Monroe County shows that forest cover increased from 39% to 60% from 1939 to 1997 (Fig. 1). Our analysis of the drivers of reforestation included economic, biophysical, and institutional dynamics, but particularly focused on the land-management decisions of households that reside on parcels ranging from 1 or 2 hectares to hundreds of hectares. Our analysis is motivated by the considerable heterogeneity among land-change processes during this period. Two parcels with nearly identical biophysical properties may exhibit vastly different land-change trajectories. Agent-based approaches are effective means of exploring these heterogeneities and the interactions between actors and the environment that produce this aggregate pattern of reforestation.

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Fig. 1. Reforestation and deforestation in Indian Creek Township, Monroe County, Indiana: 1939–1997.
As in the research described below on deforestation in Mexico, we integrated findings from multiple methods. We used laboratory-based decision-making experiments and household surveys to complement the agent-based model (3, 31). Individually, each of these methods is valuable, but when used together, they provide a particularly rich understanding of land-change processes. We collected empirical data from contemporary land managers through household surveys, explored the spatial patterns that emerge from diverse decision makers by using laboratory-based experimental research, and simulated the behaviors of decision makers drawing on the basis of these empirical approaches with agent-based models.

Land-use changes in the Midwest (LUCIM). The LUCIM model uses a utility-maximization approach whereby a set of household level land-use preference parameters are fitted to the land-change record derived from historical aerial photography. When we calibrated the model to fit land-change data from 1939 to 1997, we found that it produced agents with a diversity of land-use preference parameters. This finding demonstrates that two land managers faced with the same land portfolio (parcel size, accessibility, land suitability) may make dramatically different land-management decisions. In the south-central Indiana study area, this actor heterogeneity causes both deforestation and reforestation, although the net land-change trajectory is one of reforestation.
Similarly, the fitted parameters demonstrate that no single set of parameter values applied to all actors produces the best fit to the observed data, indicating that different households use different land-management strategies. Even agents with similar land attributes exhibited this diversity in parameter values, emphasizing the importance of household contextual factors, such as household size, wealth, and experience, in the decision-making process. A key finding of the research is that models that focus solely on biophysical factors, such as topography or soil fertility, underemphasize the importance of social factors in local-level patterns of land change.

(5)Another key difference is the distinction between agents who converted forest area to crops/pasture and agents who did not. Despite the economic potential of timber and agricultural production, a substantial subset of agents did not choose this land use. In fact, the number of agents who allowed forest to regrow on their parcels exceeded the number of agents who removed forest. We interpret this result as an indication of a change in the labor market (greater off-farm labor opportunities) and landowner preferences. Although landowners working off-farm could benefit economically from agricultural activities on their parcels (either by using household labor or through leasing), land cover in the area shifted from agriculture to forest. Qualitative data suggest that selective timber harvesting was practiced by many landowners early in the study period, but contemporary household survey data indicate that a majority of residents do not harvest timber within their forested land.

Integration of surveys and laboratory experiments. Surveys are valuable tools for identifying relationships between household attributes and land-management decisions, and data from these surveys can be linked to land-cover data via parcel boundaries (32). The household data showed a weak positive correlation between income and likelihood of reforestation, but also indicated that numerous cases of reforestation occurred on parcels owned by low-income households. In aggregate, household/parcel attributes commonly used in LCS, such as demographic characteristics, distance to markets, and wealth, explain only a small amount of variation in land-change trajectories (e.g., refs. 33 and 34). Household attributes that are more difficult to measure with standard survey approaches, such as learning, information/knowledge, risk aversion, and social networks, are hypothesized to play an important role in the heterogeneity of land-management decisions.
The ability of surveys to provide insight into land-change processes from several decades ago is limited because of the fallibility of memory and the incidence of out-migration and mortality in households. In addition, household surveys have greater reliability when questions are focused on discrete events (tree planting) or metrics (household size) rather than more intangible characteristics, such as risk aversion or learning. Thus, alternative methodological approaches are needed to bridge the gap between decision science and LCS.

(6)Laboratory experiments are a valuable tool for exploring fundamental aspects of natural resource management decision-making in a spatial context (3, 27). We used a spatial experiment to assess the diversity of resource allocation decisions. In the baseline experiment, subjects allocated land to one of two resources and received revenue according to a monotonically increasing price trend for the first resource and a monotonically decreasing price trend for the second. Despite the apparent predictability of the revenue trend, considerable heterogeneity existed in the resource allocation decisions made by subjects. A “perfect” decision maker should simultaneously change his or her entire land portfolio from one resource to another as the prices change. At the nexus where this land change should have occurred, however, the majority of subjects took many rounds to complete the reallocation of their land portfolio, and some persisted in allocating land to the disadvantaged resource through the rest of the experiment.

Next, we extended the baseline experimental design so that each subject had a land portfolio in which some cells were more suitable for one resource than another and revenue was a product of the resource price and the cell suitability. In this experiment, we also saw considerable heterogeneity among subjects' abilities to predict the revenue trend and learn the land suitability patterns. One indicator of landscape complexity is the spatial heterogeneity of the landscape. Landscape edge measures spatial heterogeneity as the sum of the perimeter of all contiguous land-cover patches. For example, in a landscape composed of equal proportions of forest and agricultural area, a checkerboard-type mosaic would have greater edge than a landscape where all forest area was in a single contiguous patch. In the laboratory experiments, the landscapes produced by subjects, for example, had more landscape edge than those that would be produced by utility-maximizing decision-makers with complete information.

This finding is supported by empirical data from both household surveys and laboratory experiments. We find evidence of actor heterogeneity corresponding to diverse land-change trajectories and importantly find theoretical support from laboratory experiments for landscapes with greater land-cover heterogeneity than a utility-maximizing decision maker would produce. Overall, these results suggest the importance of acknowledging that diversity among local-level actors is responsible for diverse land-cover change trajectories.

Deforestation in the Southern Yucatán Peninsular Region. Like LUCIM, the human–environment integrated land assessment (HELIA) model uses agents to represent real-world actors, namely agriculturalist households in the southern Yucatán (35–38). HELIA combines several methods: multicriteria evaluation, symbolic regression, and evolutionary programming. This work is part of the SYPR Project (39).
Multicriteria evaluation and symbolic regression. Along with many other land-change models, HELIA uses multicriteria evaluation, or the process of assigning the suitability of or likelihood that a given location will undergo land change as a function of spatial factors, such as soil quality or rainfall (40). Households in the southern Yucatán choose locations for agriculture as a function of environmental factors, such as soil quality or precipitation, and social factors, such as land ownership or distance to market. The SYPR Project identified factors relevant to these households via field interviews and in accordance with various land-change theories (41–44). In general terms, theories of relative space consider the importance of distance to key markets and infrastructure (e.g., Alonso, Von Thünen, Christaller, and Lösch models), whereas theories of absolute space see decision making as a function of heterogeneous in situ landscape characteristics (e.g., the Ricardian view) or as economies of scale and agglomeration (19, 36, 44).
HELIA represents real-world households and their land-use strategies as virtual agents equipped with multicriteria evaluation strategies. Multicriteria evaluation determines a function f(x) that assesses the likelihood or suitability in a given location for land change (represented by response variable Y) as a function of spatial predictor variables X = {X 1,… X n}. The form of f(x) varies from statistical equations to more complex approaches, such as neural networks or cellular models (7, 24, 45). HELIA uses land use derived from remotely sensed imagery for the response variable and predictor variables based on data for soils, elevation, slope, aspect, precipitation, surface hydrology, distance to roads and markets, and socioeconomic, political, and demographic factors (see specifics in Materials and Methods).

Symbolic regression and decision making. Many land-change models use symbolic regression to estimate the form of the multicriteria evaluation function f(x). Symbolic regression inductively estimates the ideal function f(x) as an approximate function f̂(x) by treating Y and X as random variables given by observations at discrete locations. Symbolic regression minimizes error between observed Y and the value predicted by f̂(X) (38). In land-change models, the locations of these observations often correspond to land parcels or pixels in a remotely sensed image. HELIA agents sample discrete points in a virtual landscape based on the real spatial data noted above.
Land-change models can use many different symbolic regression approaches to approximate f̂(x), but, ideally, the method should satisfy theoretical imperatives. One strong argument for the use of econometric models is that they represent perfect rationality with statistical forms of symbolic regression, such as ordinary least-squares or maximum-likelihood estimation, that are directly derived from the mathematical expressions of these theories (18, 19). Correspondingly, HELIA agents solve their multicriteria evaluation problem with a symbolic regression method termed evolutionary programming that represents features of bounded rationality (46, 47). Evolutionary programming is a computational analog to natural selection that creates software programs that solve specific problems. In particular, it acts as a symbolic regression method when programs evolve to estimate function f̂(x) (48). In essence, programs compete to create offspring programs and parent programs are selected in proportion to their fitness in solving f̂(x).

(7)More broadly, evolutionary programming can represent bounded rationality. Agents possess a set of programs that approximate real-life multicriteria evaluation strategies. An agent uses its fittest strategy to make land-use decisions, but also possesses alternatives for different circumstances (49). Agent computational abilities are restricted by limiting the number and complexity of programs (38, 50). Information is limited by the extent to which offspring carry portions of their parent programs (46, 51). Boundedly rational learning is modeled in how offspring exploit existing strategies (by copying all or most of their parental programming) and create better strategies (by combining parts of different parent programs) (52, 53). Finally, agents also learn by imitating and communicating with other agents by sharing well performing programs (54).

Comparison and complementarity of approach. In addition to representing bounded rationality, evolutionary programming allows agents in aggregate to replicate some characteristics of statistical models of perfect rationality while also individually deriving strategies that are typically identified through household interviews and qualitative research. In particular, evolutionary programs embody realistic rules of thumb while identifying the direction and magnitude of relationships between land change and social and environmental factors in a manner similar to that of an econometric model. The ability to shuttle between models of bounded rationality and perfect rationality illustrates that these models are not necessarily antagonistic because they simplify complicated real phenomena (i.e., human decision making in coupled human–environment systems), and, as such, each approach captures different facets of the same decision-making process.
We compared evolutionary programs with an econometric model of land change and example rules of thumb. We sampled the fittest program of 3,200 randomly selected agents over 100 runs of HELIA. Each program represents a multicriteria evaluation strategy for agricultural land use as a function of the environmental and social predictor variables noted above. We compared these programs with example rules of thumb described by other SYPR Project studies (41–43) and with the results of an econometric model developed by the SYPR Project and applied to the same variables (described in ref. 44). Further econometric research by the project is described elsewhere (55, 56).

(8)In terms of rules of thumb, some evolutionary programs are long and complex, but many are quite short (38). These short rules correspond to rules of thumb used by actual households, simple and effective real-world strategies like “clear secondary forest when primary forest is too far from my current location” or “plant new fields adjacent to current fields.” While these rules are generated inductively in HELIA via evolutionary programming, they are based on real land change and spatial factors and therefore reflect real household strategies that reduce travel time between fields, minimize walking time to the nearest road or village, and keep fields in locations that have served well in the past (41–43).

In terms of the econometric model, HELIA largely agrees on the importance and effect of factors predicted under theories of relative space. Table 1 compares the evolutionary programs with econometric model results by using a measure of frequency and directionality (U) that is analogous to the sign and coefficient of a Z-score in statistical models (36). The likelihood of deforestation decreases with distance from roads, markets, or settlements. The probability of agents cultivating land is negatively related to distance to existing cultivation, which is explained by the fallow-cycle dynamics of extensive swidden agriculture (fields are replanted for several years and then left fallow) and the fact that land is assigned to a household for years. Diversity is important for both models, likely because mixed land uses give easy access to the forest interior (important for expanding fields and hunting game) and may also indicate agglomeration efficiencies in agricultural production (57).

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Table 1. HELIA evolutionary program frequency and directionality analysis (U-score) vs. econometric sign and coefficient (Z-score)
In terms of absolute space, agriculture is sited on secondary succession and upland forest. This siting is related again to fallow-cycle dynamics and the need to move onto new land. Linkages between population and land use are seldom simple, but population variables control for local agricultural product demand (44). Both models find agriculture is positively related to population and population density. Prevalence of extensive agriculture and relatively abundant land likely account for this relationship. Agricultural land use is less likely with increased elevation because higher areas tend to be more rugged and have thinner, rockier, and drier soils. Agriculture is negatively related to the soil's dummy variable because it reflects generally poor soils, such as rocky soils (lithosols) and clays (gleysols and vertisols) (58). The models differ in two respects. HELIA agents preferred, in aggregate, to site agriculture on upland forest, whereas the econometric model found a weak negative relationship. Agents also uniformly ignored slope, whereas the econometric model found a positive relationship between slope and the probability of deforestation. These differences are the subject of ongoing inquiry.

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(9)The research findings from Mexico and the United States point to the importance of household factors in landscape outcomes and the potential drawbacks of methodological approaches that aggregate these local processes. In particular, an explicitly household-level approach captures complexity and heterogeneity that is lost at higher levels of aggregation. Similarly, much LCS research focuses on identifying descriptive proximate variables that explain predominant land-use trajectories, such as population density or distance to roads. Our research emphasizes the role of using multiple approaches to understand decision making and, critically, the variability of decision strategies used in both abstract and real-world contexts. The research presented here found that actor heterogeneity produces complex landscape patterns at the local level. Critically, we found evidence of households in both study areas that do not fit the homo-economicus model of the decision maker who has perfect information and makes decisions that yield the greatest economic benefit. Just as importantly, methods that omit household factors and focus on physical attributes, such as soils, topography, and accessibility, underemphasize the role of household characteristics, such as demographics, experience, and access to information, that clearly influence land-management decisions. Together, the combination of complexity and heterogeneity in decision making suggests that single-policy prescriptions designed to target landowners are unlikely to effect broad-scale changes in land-management practices without reference to specific landowners and their circumstances. To effect the greatest change, a diversity of policies (or policies targeting households with different socioeconomic contexts) is more likely to achieve desired environmental outcomes.

In the Indiana case, each method gave insight into the decision-making processes of actors and supported the notion that households are boundedly rational decision makers whose choices are affected by diverse preferences, strategies, or attributes. Although the household surveys found general associations between household attributes and land-use decisions, the correlations were far from perfect. The laboratory-based experiments clearly found considerable heterogeneity among subject decisions despite a relatively simple decision-making context. The agent-based model found that two households with parcels of identical biophysical context may pursue vastly different land-use strategies. The results from each of these methods highlight the connection between actor heterogeneity and landscape heterogeneity and the important role this heterogeneity played in producing the pattern and trajectory of land cover in the south-central Indiana study area.

In the southern Yucatán, comparison of rules of thumb, HELIA, and econometric modeling demonstrates the utility of using evolutionary programming and agent-based modeling to represent a key feature of bounded rationality, the use of rules of thumb, and capture other aspects, such as limits to information and learning over time (36, 38). The evolutionary programming approach is also in keeping with the econometric model by identifying the importance and direction of theoretically important predictor variables for land change. More broadly, evolutionary programming complements and confirms features of econometric modeling and qualitative research. Although evolutionary programming cannot match these other approaches in many respects, such as the analytical power and history of econometric approaches or the depth and nuance of qualitative methods, it does offer a useful alternative.

(10)More broadly, this research points to areas of further exploration. Agent-based modeling has evolved from very abstract formulations to being more closely tied to empirical data, but with this evolution comes research challenges and a greater need for rich, real-world data. Although agent-based models help address the challenge of micro-macro integration, for example, they require data at multiple organizational scales, ranging from individuals through households, communities, and nations. Two particular areas that require attention are the roles of social networks and institutions in individual decision making. Agent-based models ably handle spatiotemporally explicit data, but these data must first exist. For example, the LUCIM modeling effort is one of very few that can lay claim to such a long-term, spatially explicit, time series data (59). Similarly, although agent-based models are in many respects ideal for capturing human–environment relationships, they are built on a broad foundation of research on individual human and environmental systems and the interconnections among them. HELIA, for example, could not exist outside of the large and interdisciplinary SYPR Project (39). Additionally, although these models can link qualitative and quantitative approaches, further research is necessary to match the elegance offered by quantitative approaches, such as mathematics or statistics, while also capturing more of the nuance and sophistication of qualitative approaches.

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LCS joins social, ecological, and information sciences. One important medium for this integration is the use of spatially explicit agent-based models of land change. These models give insight into decision making that defines the well-being of individual households and their communities. Agent-based models illustrate local-level dynamics, but importantly, also complement other methods. The value of modeling in general is heightened when used in an integrative manner, bringing together theories of decision making instantiated via different models and combining them with empirical data gleaned through approaches ranging from personal interviews and laboratory experiments to interpretation of remotely sensed imagery. Fortunately, land change is an ideal venue for exploring a mix of theory, method, and data given the larger LCS focus on using multiple approaches to understand land change, the very tangible and therefore measurable effects of land-change processes, and the fact that land change directly or indirectly affects people around the world.

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Materials and Methods

Indiana. LUCIM fits household land-use preference parameters to the land-change record derived from historical aerial photography [full model specification available elsewhere (27, 31)]. Seven time points of aerial photography were interpreted to produce a multitemporal data set of digital land-cover data (1939, 1958, 1967, 1975, 1980, 1987, and 1993). Parcel-level land ownership boundaries were derived from hard-copy cadastral maps and integrated with the land-cover data with a geographic information system. Household characteristics were derived from household surveys conducted in 1998 and 2003. The historical time series of crop and timber prices were created by a combination of state and federal economic data sources. The household survey included a series of questions related to past land-use decisions, demographic structure, sources of information related to land-use practices (e.g., media, neighbors, relatives), and awareness of incentive programs targeting conservation. The laboratory experiments included five replications of two experimental designs (nine subjects per session for a total of 45 subjects per experimental design). The experimental research used a custom experimental software platform developed in ArcGIS. All research activities were approved by the Human Subjects Committee at Indiana University.
Southern Mexico. General characteristics of HELIA are given where pertinent for discussions of results but the data, structure, and parameterization of HELIA are described in full elsewhere (35–38). Further information is also available from the SYPR Project (http://earth.clarku.edu), which provided most of the data for the model, including land-use/cover maps from Thematic Mapper imagery for 1987, 1992, and 1995; elevation, aspect, and slope from a 1:50,000 digital elevation model; soil types from a 1:250,000 map (provided by the Mexican National Institute for Statistics, Geography, and Information; Instituto Nacional de Estadística, Geografía e Informática); a road network and surface hydrology from 1:50,000 topographic maps; precipitation from 21 federal weather stations; and federal censuses of demographic and socioeconomic data.
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Nix 2012

Created By: Timahje Keesee

(1)Of all the professions, forestry may be the most misunderstood of the lot. Many kids and adults asking me about becoming a forester haven't a clue that it takes a four year degree. The stereotypical picture is of a job spent in the forest, or in fire towers, or hunting and fishing and saving campers lost in the wilderness. I want to put a more realistic face on the profession of forestry.

(2)A bachelor's degree in forestry is the minimum educational requirement for professional careers in forestry. In the Federal Government , a combination of experience and appropriate education occasionally may substitute for a 4-year forestry degree, but job competition makes this difficult. Still, for industrial employment or becoming a registered forester, you must have a degree.

(3)Fifteen States have mandatory licensing or voluntary registration requirements which a forester must meet in order to acquire the title "professional forester" and practice forestry in these states. Licensing or registration requirements vary by state, but usually demands completing a 4-year degree in forestry, a minimum period of training time, and passing an exam.

(4)Most land-grant colleges and universities offer bachelor's or higher degrees in forestry; 48 of these programs are accredited by the Society of American Foresters . The SAF is the governing authority for curricula standards -

"The Society of American Foresters (SAF) only grants accreditation to specific educational curricula that lead to a first professional degree in forestry at the bachelors or masters level. Institutions request SAF accreditation and offer curricula that have been found to meet minimum standards for objectives, curriculum, faculty, students, administration, parent-institution support, and physical resources and facilities."
SAF approved curriculums stress science, mathematics, communications skills, and computer science, as well as technical forestry subjects. (5)Just loving working in the woods is not a very good reason for becoming a forester (although it should be considered a necessity). You have to like scientific course study and be willing to develop your science skills. Foresters generally must enjoy working outdoors, be physically hardy, and be willing to move to where the jobs are. They must also work well with people and have good communications skills. You probably ought to realize as well that you may work your way out of the woods as you gain more experience and knowledge.

Desirable electives include economics, wood technology, engineering, law, forestry, hydrology, agronomy, wildlife, statistics, computer science, and recreation. (6)Forestry curricula increasingly include courses on best management practices, wetlands analysis, water and soil quality, and wildlife conservation, in response to the growing focus on protecting forested lands during timber harvesting operations. Prospective foresters should have a strong grasp on policy issues and on the increasingly numerous and complex environmental regulations which affect many forestry-related activities.

Most colleges require students to complete a field session either in a camp operated by the college or in a cooperative work-study program with a Federal or State agency or private industry. (7)All schools encourage students to take summer jobs that provide experience in forestry or conservation work.
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Wasson 2011

Created By: Timahje Keesee

We are standing on the knife’s edge of a ridge in Oregon’s rugged Coast Range, peering down into a steep draw with a gurgling creek at the bottom. The hillside, pockmarked with tree stumps, also bears a patchy covering of shrubs and soft-stemmed plants: evergreen huckleberry, Oregon grape, Ceanothus, bracken fern, trailing blackberry, beargrass. Among them, tiny conifer seedlings unfurl their tips into the sunshine.

Bisecting this recently logged site is a band of big old trees and tall dead snags with a thicket of shrubs in its moist understory. This remnant, left behind by the loggers, as Tim Vredenburg explains, is the “lifeboat” intended to sustain the resident animals into this forest’s next life.

“The latest science tells us that typical industrial forestry doesn’t provide enough habitat for early successional wildlife species.” Vredenburg is referring to research by University of Washington forest ecologist Jerry Franklin and others, which shows that habitat needed by young-forest mammals and birds is in short supply on most industrially logged forestlands. Says Vredenburg, “We try to leave enough vegetation on the ground to carry them through the first ten years or so, until the trees start to close in.”

(1)To some, the site might look like the product of typical industrial forestry. It has, after all, been clear-cut, which is the most common (and most controversial) way to harvest timber in the forests west of the Cascades.

But Vredenburg, head forester for the Coquille Indian Tribe, invites his visitor to take a closer look. He points out subtle differences on the ground. The generous cover of live trees, snags, and shrubs left on the site is more than the law requires. Industry foresters prefer not to have any unnecessary vegetation competing with the young Douglas firs, which they typically harvest on a short rotation (when trees are between 35 and 50 years old). But the Coquille foresters have made room for a little competition by using longer harvest cycles. “We aren’t planning to harvest these trees for a long time, 80 to 100 years,” says Vredenburg. Furthermore, the planted seedlings in the Coquille’s forest aren’t a Douglas fir monoculture, as one might expect; red cedar and the rare Port Orford cedar are also in the mix. Both of these conifers are cultural treasures for the Coquille Tribe: their ancestors made houses and canoes out of the wood and wove the inner bark into garments and ceremonial blankets.1

Vredenburg is director of the Land, Resources, and Environmental Services Department of the Coquille Indian Tribe, headquartered in North Bend, Oregon. This 36-acre site is part of the tribe’s Chu-aw Clau-she timber sale—the first logging conducted by the tribe since the Coquilles gained control over a small fraction of their ancestral lands in 1996.

(2)Coquille tribal chief Ken Tanner said a blessing over this site, as he always does, before the first chainsaws snarled to life. “Any time I talk to one of our foresters, I try to stress the idea of balance,” Tanner says. “We don’t own the forest; it’s a part of our organic being, which we share with all the other creatures and creations. Anything we take, we honor with prayers. We make sure those forest spirits—the spirits of the tree or the salmon, as it might be—tell their relatives that we’re good people, so they’ll continue to be there for us.”

A Different Kind of Forestry

Much of the Coquille Forest lies in the watershed of the middle fork of the Coquille River. The ridges east of Coos Bay represent a patchwork of diverse ownership, an artifact of nineteenth-century federal land grants. The tribe’s closest neighbors are a large timber company, whose recent harvest sites look like clean dinner plates, and the federal Bureau of Land Management, whose forests are dog-hair thick with conifers between 80 and 120 years old.

The Coquilles, like many other forest-owning Indian tribes today, are attempting a different kind of forestry.

Vredenburg was trained as a biologist. His sandy hair, blue eyes, and surname hint at his European ancestry, but he’s devoted his career to the Coquilles’ vision for their ancient homelands. “There’s a tendency in today’s culture,” Vredenburg says, “to fixate on a particular set of values within a forest, or an ecosystem: jobs, or timber receipts, or old growth, or fish habitat—you name it, there’s a position. The tribe’s position is to value all of it. The idea of reserves, of drawing a line around a forest to keep people out, doesn’t make sense to the Coquilles. But neither does the idea of taking everything away and leaving nothing for future generations.”

Coquille Forest timber is keeping local loggers employed and supplying regional family-owned sawmills. And in the summer of 2011, the Coquille Forest received certification under the Forest Stewardship Council (FSC), a worldwide certification body that upholds rigorous environmental, social, and economic standards for managed forests.

(3)Around the world, about 353 million acres of forest are certified under the Forest Stewardship Council. They include forestlands of three other Indian tribes: the Menominee in Wisconsin, the Hoopa Valley in northern California, and the Confederated Tribes of Warm Springs in central Oregon. Certification is a major milestone for the Coquilles, says tribal chairman Ed Metcalf. “Until these forests were taken from us, our ancestors managed them for the long-term welfare of the land and the people. It’s fitting that the FSC recognize our efforts now.”

Profit, People, and Planet

(4)The Pacific Northwest has been embroiled in a conflict over forest use and management for three decades. Politically, you might envision it as a sort of seesaw, with your stereotypical tree-sitting hippies at one end and your grizzled chainsaw-toting Paul Bunyans at the other. Which end of the seesaw is up at any given moment depends on the state of the Northwest’s economy. Both factions claim the label of “sustainable.”

(5)To be sustainable in the eyes of the Forest Stewardship Council, forestry has to be environmentally appropriate, socially beneficial, and economically viable. It must achieve a “triple bottom line” that honors “profit, people, and planet,” as the Economist magazine puts it.

Here in the Northwest, forestry’s profit column is generally in the black, and the planet column is more solvent than it used to be. (6)The forestry industry has been profitable ever since the first timber barons arrived on the West Coast in the late nineteenth century, but at the cost of a bruising boom-and-bust economy, social instability, and environmental damage. Thankfully, timber harvest is kinder to the forest than it used to be. Timber operators are bound by a host of laws and practices aimed at protecting the environment on public and private forestlands.

But as for the third bottom line, the people one, there’s little consensus within the larger Northwest community about whether environmental protection of forests is too heavy-handed, or shamefully lenient, or about right.

Public opinion remains divided over clear-cutting, old-growth reserves, forested buffers along streams, management of wildlife habitat, and spraying of herbicides. The debate is particularly sharp when it concerns logging on public lands, but private-land practices also face regular criticism.

Agencies like the Oregon Economic and Community Development Department and the Oregon Department of Forestry are partnering with universities and nonprofits like Ecotrust and Sustainable Northwest to develop statewide strategies for a forest industry that meets the “triple bottom line.” And, in the past five years, the Forest Stewardship Council has certified more than 200 forest-related enterprises in Oregon.2

Holistic Stewardship

(7)“We get many things from the forest—canoes, baskets, clothes, shelter, fir, cedar, spruce, beargrass, camas—and we use all of these things,” explains Chief Tanner. “But they also have a spiritual value which we honor as we honor our ancestors. What we take, we try to give back. What we don’t need, we try not to take.”

Gail Wells
(8)To help sustain wildlife into the next forest rotation after an area has been clear cut, Coquille foresters leave mature trees, standing dead snags, fallen wood, and living shrubs and other plants on the forest floor.
Ten thousand years ago, ancestors of today’s Coquille Indians lived along the southern Oregon coast from Coos Bay to Cape Blanco and along the inland valleys of the Coquille River drainage. A common misconception among European Americans is that Indians lived passively within their environment, “at one with nature.” On the contrary, aboriginal peoples actively managed their landscape for their own objectives, using the technologies available to them.4,5 For coastal tribes and others, the key management tool was fire. The people regularly set fire to meadows and valleys to maintain grassy cover, keep brush at bay, improve habitat for deer and elk, and cultivate fire-adapted plants that were important sources of food and fiber.

Hence, the land the first Euro-Americans took to be a pristine, park-like wilderness was in fact the product of thousands of years of indigenous land management. “This Countrey must be thickly inhabited by the many fiers we saw in the night and culloms of smoak,” wrote Robert Haswell, Captain Robert Gray’s first mate, as he viewed the land from his ship off Cape Blanco in August of 1788. Haswell also noted that “the land was beautifully diversified with forists and green verdant launs.”6

One important reason for burning was to prevent the vigorous Douglas fir from invading clearings. “The Douglas fir timber they say has always encroached on the open prairies and crowded out the other timber,” recalled Lucy Thompson (Che-na-wah Weitch-ah-wah), a Yurok woman of northern California, in 1991. “Therefore they have continuously burned it and have done all they could to keep it from covering the open lands.”1


(9)In the middle decades of the 1800s, the Indians of the Oregon coast were abruptly cast out of their lands, and European American settlers moved in. Prairies became pastures, valleys became farm fields, forests were cut down, wild animals and plants were replaced with domestic ones. Property lines were inked on maps, the new owners halted Indian burning, and trees started to encroach on the meadows.

In 1851 and 1855, the Coquilles and neighboring tribes signed treaties that would have allowed them to keep some of their ancestral homelands. Congress never ratified these treaties. Instead, it passed land claim laws in the 1850s and 1860s that opened the door to white settlement of Indian lands. By 1856 most Coquilles had been forcibly removed to the Coast Reservation, north of the Umpqua River.7

The next hundred years were ones of diaspora for the Coquilles. As the reservation’s lands were nibbled away piece by piece and offered to white developers, some Coquilles made their way back to their old homes, where they discovered that their traditional fishing and gathering places were now on private property. They joined remnant, mixed-blood families living around Coos Bay and up the Coquille drainage, descendants of Coquille women who had married white men in the 1850s and had not been transported to the reservation.1

In the mid-1950s, in a policy thunderbolt, Congress terminated the trust relationship between the United States and 109 tribes and bands, including the Coquilles. The stated goal of termination was to reduce Indians’ dependence on the government and to hasten their assimilation into mainstream American life.

For Indians, it was a disaster. Lands held in common were split into individual allotments, forests and other assets were sold off. Access to ancestral hunting and fishing grounds was cut off, and tribes lost the means to support themselves.

Termination nearly annihilated his people, says Chief Tanner. “We were invisible. No one could see us, and we couldn’t see ourselves.”8


In the early 1980s, Coquille tribal members mounted a long struggle to regain their tribal status and some of their ancestral lands. Their champion in Congress was Oregon’s Senator Mark Hatfield, who also spearheaded restoration proposals from two other terminated Oregon tribes, Grand Ronde and Siletz. On June 28, 1989, President George H.W. Bush signed the law that restored the Coquilles as a federally recognized Indian tribe.

Gail Wells
Tim Vredenburg (left), the head forester for the Coquille Tribe, talks with tribal forest engineer Ed Vaughn. Behind them is a newer forest stand, planted about 15 years ago.
The 5,400-acre Coquille Forest was created by another act of Congress in 1996. The restored land, carved out of the Bureau of Land Management’s Coos Bay District holdings, comprises 14 parcels in the Coquille River drainage, interspersed with other BLM lands and privately owned forestlands. The Coquilles do not own fee title to the land, but have exclusive management rights in perpetuity.

The tribe had initially petitioned for 59,000 acres, but congressional compromises and deal-cutting whittled their portion down to less than one-tenth that amount. In addition, the terms of the restoration require the Coquilles to abide by the 1994 Northwest Forest Plan, which governs National Forest management in Oregon, Washington, and northern California.

The Northwest Forest Plan requires broad, untouched buffers of forest along streams. Because the Coast Range is spiderwebbed with streams, and because Coquille forestland is in small pieces—the biggest being 1,380 acres—this provision effectively restricts more than half of Coquille Forest timber from commercial harvest.

Tribal leaders say they intend to recover more of their ancient homelands in the future. In the meantime, they’re doing their best with what they have. (10)“Our intent,” says Chief Tanner, “is not only to protect our cultural resources, but to lead by example—to display the best, most responsible, most respectful resource management not only on tribally owned lands, but within our much larger ancestral territory.”

Coming into Its Own

(11)Over the past two decades, Indian forestry has been gaining in both profitability and environmental performance. There is also growing collegiality and mutual respect between tribal foresters and the Bureau of Indian Affairs, the federal agency entrusted with Indians’ welfare. A boom in tribal enterprises, particularly casinos, has generated capital for further economic development. And more Indians are pursuing degrees in forestry, fisheries, wildlife science, ecology, and other land-management disciplines.

Nationwide, there are 302 forested Indian reservations, containing 7.7 million acres of timberland and another 10.2 million acres of woodlands.9 (12)A panel of experts who reviewed forest practices on Indian reservations in 1991 and again in 2001 found that ecological conditions and management practices on tribal forests had improved. Tribes were paying more attention to wildlife habitat and forest diversity and complexity, and they were integrating these environmental goals with timber production.

The panel concluded that, while Indian forests nationwide still face major challenges—invasive insects, high fire risk, lack of home-grown management expertise, lack of funding, and limited marketing opportunities—Indian tribes can provide a model for forest management.

The panel’s chair for both reports, John Gordon, is an emeritus forestry professor from Yale and one of academic forestry’s most respected scholars. “Because the cultural identity and continuity of tribal communities are so dependent on forests,” he and his colleagues write in a summary of the panel’s findings, “tribal governments have a profound sense of stewardship for the land and its resources.”9

Gordon consulted with Coquille tribal leaders in the mid-1990s as they developed a sustainability strategy for the ancestral forestland they were then petitioning to recover. “The Coquilles are the only forest managers in their neighborhood who are meeting both their timber-production and their environmental targets,” Gordon told me. “They are very committed to long-term, environmentally sound forest management.”


(13)It’s not necessary to invoke the noble savage stereotype to grasp that land has a different meaning within an indigenous culture than it does within an industrial culture. The struggle of today’s Indians to regain their lands is part of their struggle to regain and remember themselves as indigenous peoples.

“The Coquille people have been in this place since time immemorial,” says Metcalf. “Not only do our forests provide us with food, fiber, and shelter, but they’re a critical part of our identity.”

Courtesy of the Coquille Tribe
Tribal foresters harvest trees in the Coquille Forest. The standing trees in the background were left untouched in order to maintain wildlife habitat.
In the Siletz language, the word for Earth may be translated roughly as “made for you.”8 People who depend on the land learn to pay attention to cycles of birth and death, of plenty and scarcity. Over many generations, the Indians of the Oregon coast learned where the best beargrass patches were and how to keep them flourishing, when to burn the high meadows of the summer campground to attract the elk and to keep the Douglas fir at bay, how to harvest and process the inner bark of the Port Orford cedar for making blankets. “I come from a people who did not have a word for preservation, or for the environment, or for ecology,” says Esther Stutzman, a storyteller of the Komema Kalapuya people of western Oregon and a member of the Confederated Tribes of Siletz. “The word they had was respect.”10

Indian approaches to land management are different because they reflect a different social contract between people and the land. By social contract, I mean a broad understanding of how wealth is created, and by whom; who gets how much of what kinds of resources; and who decides how to distribute these resources. Our larger society is still under the influence of the post–World War II social contract, which was negotiated at a peculiar and unprecedented point in history. That contract, heady and expansive, was a decisive about-face from the gloomy penury of the Great Depression. It called for American businesses to use nature in the service of creating and selling products, maximizing prosperity, and making a middle-class standard of living available to more Americans than ever before. The very term natural resources highlights this understanding.

In the Pacific Northwest, timber companies, as the region’s economic engines, were the chief mediators of this contract. They faithfully carried out the contract’s provisions, fulfilling what most people then saw as the greater good.

The industrial social contract held valid for two generations. But now, from the perspective of our tellingly named postindustrial society, we’re reckoning with its environmental and social costs. And we have lost trust in the old social contract to deliver the goods that are now needed.

What are those goods? For the forest industry, it’s raw materials in sufficient and consistent quantities, a stable policy environment, and society’s permission to keep practicing profitable forestry. For the environmental community, and indeed for everyone who cares about the future, it’s assurance that the forest will be sustained for generations to come. For the larger society, it’s confidence that the legal framework around forest management is fair and functioning. For forest-based communities, it’s confidence that the forest-products economy will stick around and share its prosperity with the whole neighborhood.

Imagine Ourselves

(14)Indigenous values, as reflected in modern Indian land management, could help the rest of us imagine what a new social contract might look like. Keeping the Coquilles’ approach to forest management in mind, perhaps we could picture an economy that doesn’t prize unlimited growth, doesn’t consider land as just another fungible asset, doesn’t revere the iron law of the market, doesn’t conflate profit with prosperity, and doesn’t subscribe to the peculiar accounting that keeps environmental costs off the books.

“The two ways of seeing the land—the Euro-American way and the indigenous way—are complementary, and they have a lot to share with each other,” says forest ecologist David Perry. “But they are different. And I believe the indigenous way is more likely to lead us into a sustainable future.” Euro-American science and technology are preoccupied with analyzing and generalizing, Perry notes. “Yet, as Wordsworth said, ‘We murder to dissect.’ Western science must generalize—that’s one of its key strengths. But as we move into management of whole ecosystems, we can only generalize so far. Place becomes crucial. That’s why native knowledge, rooted in place, is important in finding successful ways to manage forests in the future.”11

Thoroughly Modern

The Coquilles’ forest-management plan calls for the tribe to manage “intensively for spiritual, cultural, biological, recreation, aesthetic, and economic values” (italics mine). This requires balancing modern land-management tools and techniques—rooted in the scientific tradition that brought us the industrial social contract and all its blessings and shortcomings—with the traditional ethic of tending, harvesting from, living within, and caring for the land over many generations.

The plan calls for fairly conventional silviculture, although tilted several degrees toward environmental protection. It identifies a sustainable harvest of timber and other forest products, based on a careful periodic forest inventory. It calls for protecting wildlife habitat and retaining ecologically valuable components of the forest, such as big trees, snags, and chunks of dead wood. Tribal foresters take special measures to protect nest sites of great blue herons, osprey, hawks, and golden eagles, which are culturally important to the tribe. They thin younger stands, 20 to 40 years old, to encourage fast growth in the remaining trees and to promote a mix of shrubs and other plants to provide food and shelter for deer, elk, and other wildlife.

Gail Wells
A Douglas-fir seedling in the Coquille Forest. While it is standard in industrial forestry to harvest Douglas firs when they are between 35 and 50 years old, Coquille foresters wait 80 to 100 years to harvest.
The foresters leave a wide swath of conifers along streams as riparian reserves, as the Northwest Forest Plan requires. They take out some of the streamside alders, willows, and other deciduous trees and shrubs and plant cedar, spruce, and Douglas fir. Eventually, these conifers will mature and fall into the stream, improving habitat for coho salmon and other fish and amphibians.

(15)Through all this, foresters do what they can to protect ancestral occupation sites and important gathering places and to encourage plants and animals that were important in ancestral times for food, fiber, tools, and medicine.

At Home in a Place

(16)The Coquilles are thoroughly modern forest managers. They use chainsaws and skidders and, when they need to, herbicides. They clear-cut, because clear-cutting is both profitable and biologically appropriate in most Douglas fir forests west of the Cascades. They treat forestry as a moneymaking enterprise, selling their logs to sawmills in Coos and Douglas counties. Yet their mission and values come from 10,000 years of being rooted in a bountiful land.

To be sure, having values and acting on them are two different things. Indian tribes are just beginning to manage forests in a twenty-first-century economic context. In today’s global marketplace, American forest products must compete with cheap wood from countries that lack basic environmental safeguards and worker protections. The Coquille Tribe will undoubtedly feel pressure to cut too much timber too fast, to chase profit in the name of prosperity. Time will tell whether traditional cultural values will be an effective counterweight against such blandishments.

That said, being at home in a place tends to produce a long view.(17) “The Coquilles have to live with their decisions,” says John Gordon. “They can’t just say, ‘We love wilderness,’ and then go home. If they clear-cut, they have to look at it. If they burn, they have to breathe the smoke. If they set aside portions of their forest away from management, they have to live with the reduced income. They’ve been at the same address for thousands of years, and they intend to be there for many more. If anything is conducive to long-term management, that’s it.”

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Buenzow 2012

Created By: Timahje Keesee

(7)Even though I grew up as a “city kid” in Milwaukee, Wisconsin, I always loved the outdoors. I would play outside all summer, climbing trees, picking dandelions, catching worms and checking out ant nests. I loved to make tents out of lawn furniture and blankets to pretend I was camping. One breezy spring day, I noticed thousands of tan-colored whirligigs spinning down from my neighbor’s tree. I picked them up by the handfuls and threw them into the air to watch them twist and twirl. The neighbor lady saw me and came out from her house. “Do you know what those are?” she asked. I had no idea. “Those are the seeds from this silver maple tree,” she pointed.

“Seeds?” I asked. “You mean like the kind we plant in the garden?”

“Yes, that’s right. Let me know when you want to help me sweep them all up!" she laughed.

She went back inside. I picked up another handful, but this time I examined them closely. There was a hard, oval shaped pod with a delicate wing attached to one end. I glanced to be sure the neighbor wasn’t looking and ran back to my yard with the stash of seeds. I kneeled down in front of my mother’s flower bed, and carefully poked a row of holes a few inches apart into the soil with my index finger. I pushed a seed, wing up, into each hole, and filled them back with soil.

I checked the garden every day for several weeks, but nothing happened. I had almost forgotten about my experiment when I heard my mom grumbling about all of the weeds that had sprouted where she was trying to plant flowers. I ran to find a neat row of identical seedlings, whose leaves looked suspiciously like those on the neighbor’s tree. And that was when the seeds for my career in forestry were planted!

So you think you'd like to be a forester…

Here are a few tips.
First of all, you need to love trees! (1)Trees are the most fascinating and miraculous plants on our earth! There’s so much to know that I’m still learning new and wonderful things about trees every day. (2)Besides knowing about trees, you need to learn about all of the other parts of the forest ecosystem. We wouldn’t have trees without soil (DON’T call it dirt!), so I studied soil science. And (3)we wouldn’t have soil without rocks and wind and rain and ice, so I also studied geology and meteorology. And we wouldn’t have pollination if it weren’t for birds and insects, so I learned about those, too. And I learned about the animals of the forest so I could help protect and improve their home. I also needed to study math, so that I could measure the trees. I need to know how big around and how tall and how old and how crowded the trees are in a forest. That information helps me to make management decisions, like is it time to cut some trees? (It IS okay to do that sometimes!) (4)Do we need to prune the trees so they grow straight and tall? Do we need to plant more trees? Do we need to take action against an insect or disease that threatens the trees? These are all decisions a forester helps to make.

(5)Most professional foresters have at least a bachelor’s degree in forestry. Many also have a Master’s Degree. So if you want to be a forester, you should plan to go to college. While you’re in high school, take lots of science, math and English classes. Summer jobs or volunteer work in natural resource management give you valuable real-life experiences!

(6)Some foresters work for the government including Federal, State, County or City. Other foresters work for companies like lumber companies or paper companies. Some foresters are private consultants, who work for themselves or a consulting company. Some foresters specialize in urban forestry. An “arborist” is trained to care for individual trees, usually in an urban setting.


I can wear bluejeans and boots to work almost every day!
I get to spend time outdoors all year round!
I get to work with landowners who want to learn about and care for their forests!
I get to talk to children about how wonderful trees and forests are!
I show people how to properly plant and care for tree seedlings.
I help to decide when the time is right to cut trees down.
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Anonymous 2009b

Created By: Timahje Keesee


(1)Pennsylvania State University, School of Forest Resources - The School of Forest Resources' mission is to provide educational opportunities and science-based information to protect, manage, and use natural resources for sustained benefits. This is accomplished through educational, research, and service programs in forestry, wildlife and fisheries, forest products, and related areas. Penn State’s School of Forest Resources represents one of the most highly regarded forestry, wood products, water resources, and wildlife and fisheries programs in the nation.

South Carolina

(2)Clemson University, Department of Forestry and Natural Resources was formed in 2003 with the merger of the Department of Forest Resources and the Department of Aquaculture, Fisheries and Wildlife. Faculty from the Clemson Institute of Environmental Toxicology also joined the new department. The Department now offers B.S., M.S., M.F.R., and Ph.D. degree programs in Forest Resources, B.S., M.S. and Ph.D. programs in Wildlife and Fisheries Biology, and heavily supports the B.S. degree in Environmental and Natural Resources.


(3)University of Washington - School of Environmental and Forest Sciences - holds a position of national and international leadership in both instruction and research. The College is dedicated to generating and disseminating knowledge for the stewardship of natural and managed environments and the use of their products and services through teaching, research, and professional and public outreach.ural Resources.
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