Agroforestry economics uses models to help farmers, educators, researchers, and policy makers, among others, make their decisions. This chapter presents the concepts of economics and explains how they apply to natural resource management decisions and subsequently those related to agroforestry. It discusses many tools used by economists to measure and determine how choices are made at the farm level. To give a flavor of how the tools of economics can be used to analyze agroforestry, the chapter describes and provides examples of using two of the approaches: enterprise or farm budget models and nonmarket valuation models. It provides an overview of policies and incentives to encourage landowners to adopt agroforestry systems. There are several types of farm budgeting, and the chapter discusses three, namely whole-farm, enterprise, and partial budgeting. The use of net present value and other financial indicators to evaluate agroforestry alternatives is also explained.

Rural communities and lands are particularly vulnerable to climate change (Lal et al. 2011). Changes in the viability of plants and animals; arid land expansion; reductions in water quantity and quality; increased threats from pests, diseases, and wildfire; and more frequent extreme weather events may induce a variety of economic and social impacts on rural communities. These impacts include increased uncertainty of food and fiber production, reduced supply of ecosystem services, changes in employment opportunities, and human (and nonhuman) population relocations (Lal et al. 2011). Reducing the vulnerability of rural communities requires developing agricultural and forestry production systems that are resilient to changing environmental conditions (Lin et al. 2008, Verchot et al. 2007). Chapters 2 and 3 of this assessment demonstrate the potential for agroforestry to mitigate climate change (through increased carbon sequestration and reduced greenhouse gas [GHG] emissions) and assist rural communities in adapting to climate change by providing alternatives to conventional agricultural that are more resilient to the impacts of extreme weather events, changes in rainfall patterns, and increased risks from pests and diseases. Agroforestry, however, will make a significant contribution to mitigating or adapting to climate change only if landowners across the landscape adopt it in the United States (Scherr et al. 2012).

Qualitative interviews with participants in the cocoa (Theobroma cacao) supply chain in Costa Rica and the United States were conducted and supplemented with an analysis of the marketing literature to examine the prospects of organic and Fairtrade certification for enhancing environmentally and socially responsible trade of cocoa from Costa Rica. Respondents were familiar with both systems, and most had traded at least organic cocoa for some period. However, most individuals said that they were seeking better product differentiation and marketing than has been achieved under the organic and Fairtrade systems. Many suggested that more direct recognition of individual growers and the unique value of their cocoa throughout the production chain would be more helpful than certification for small companies in the cocoa supply chain. These findings suggest new marketing techniques that convey an integration of meaning into the cocoa and chocolate supply chain as a differentiation strategy. This involves integration of the story of producers' commitment and dedication; shared producer and consumer values of social and environmental responsibility; and personal relationships between producers and consumers. This marketing approach could enhance the ability of smaller companies to successfully vie with their larger competitors and to produce cocoa in a more environmentally and socially acceptable manner.

There is growing interest in the study of agroforestry adoption because it is promoted as a technology that can generate a sustainable version of development in which economic growth occurs in tandem with ‘sustenance’ or protection of ecological capital. Conventional wisdom suggests that agroforestry provides substantial economic and ecological benefits to communities and households and, therefore, should be readily adopted by farmers. Yet, many attempts to promote agroforestry systems have resulted in inadequate rates of adoption. We review the literature on technological innovations in general to identify the determinants of adoption within an economic framework. We find five categories of determinants of technology adoption: preferences, resource endowments, economic incentives, biophysical factors, and risk and uncertainty. We then analyze 56 articles on adoption of agricultural and forestry technology by small holders to evaluate these factor-clusters. Ultimately, based on the criteria of (a) empirical analysis and (b) focus on agroforestry and soil-water conservation investments, we narrow our list down to 26 studies from 17 countries. We discuss in detail the direction of influence of variables in each category. Pattanayak is with the Research Triangle Institute, Mercer is with the US Forest Service, Sills and Cassingham are with North Carolina State University. Direct all correspondence to: Subhrendu K. Pattanayak, Center for Regulatory Economics and Policy Research, Research Triangle Institute, RTP, NC 27709-2194. Email: subrendu@rti.org. Agroforestry technology is sold on the promise of a sustainable version of development in which economic growth occurs in tandem with ‘sustenance’ or protection of ecological capital. Given these potential benefits to target communities, conventional wisdom suggests that agroforestry should be readily adopted. Nevertheless, attempts to promote agroforestry systems have often resulted in inadequate rates of adoption. Although a variety of reasons contribute to low adoption rates, they often result from inadequate assessments of farmer preferences, priorities, and constraints prior to designing new agroforestry systems (Current et al. 1995; Mercer and Miller 1998). In the mid-1990’s, agroforestry leaders began to plead for increased emphasis on research to understand the agroforestry adoption decision process (Sanchez 1995, Mercer and Miller 1998). As a result, a plethora of agroforestry adoption studies have emerged recently (see reference list in Pattanayak et al. 2001). Our goal is to see if it is possible to combine information from many studies in a simple meta-analysis so as to produce more general knowledge. SIMPLE META-ANALYSIS: A SYSTEMATIC WAY TO LEARN ABOUT AGROFORESTRY ADOPTION? In its most general form, meta-analysis offers a set of quantitative techniques for synthesizing results of many types of research, including opinion surveys, correlation studies, experimental and quasiexperimental studies, and regression analyses probing causal models. In our context, we investigate consistency across different adoption studies to evaluate whether the studies are generating more than random noise regarding the determinants of adoption. In this method, the investigator gathers together all the studies relevant to an issue and constructs at least one indicator of the relationships under investigation from each study. For most intents and purposes, study level data can be analyzed like any other data, permitting a wide variety of quantitative methods. The simplest of the meta-analytical methods is called vote-counting. In this method, the analyst counts all the studies that have a statistically significant result. We use a vote-counting meta-analysis of agroforestry adoption because of some severe data constraints. Specifically, we are restricted by the discrete choice nature of our dependent variable (probability of adoption) and the lack of detail on marginal probability of adoption (effects size) provided in our set of studies. In our concluding discussions, we offer some qualifications to our research findings, given the limitations of the vote-counting method employed and the studies reviewed. This does, however, position us to make specific recommendations for more sophisticated metaanalyses of agroforestry adoption. WHAT INFLUENCES ADOPTION OF AGRICULTURAL AND FORESTRY TECHNOLOGY? There is a large and growing literature on issues surrounding adoption of technologies, because technology is an engine for economic growth. We drew on approximately 60 empirical studies to identify the key determinants of technology adoption, paying particular attention to the seminal survey by Feder et al. (1986) and a recent study of sustainable agricultural intensification by Clay et al. (1998). Although there has been some debate in the literature regarding the uniqueness and complexity of agroforestry technology (Scherr, 1994), we believe that its basic features are common with farming and forestry technology. Consequently, we reviewed the agroforestry adoption studies within the general framework of agricultural and forestry technology adoption. That is, we viewed the explanatory variables in the agroforestry and soil and water conservation studies as elements of the factor clusters or categories described in the general literature. Our literature review revealed five categories of determinants of technology adoption. Preferences, resources, incentives, bio-physical factors, and risk and uncertainty constitute the 5 factor clusters. In discussing the results, we present specific examples of variables in these factor clusters. Preferences define the objectives and motivations of the economic agents choosing technologies. Resource endowments enable their technology choices. Economic and biophysical incentives condition the extent, timing and nature of the technology choices. Finally, the degree of risk and uncertainty can seriously undermine investment initiatives that pay dividends only in the long run. Caveats regarding this categorization are in Pattanayak et al. (2001).

In this chapter we provide an overview of the socio-economic and ecological effects and trends of wildfire in the WUI, methods for assessing wildfire risk in the WUI, approaches to managing the wildfire problem including fuels management, home construction and design, and community action programs. This overview is combined with two case studies analyzing wildfire risk and the use of prescribed fire to reduce that risk in the Florida wildland-urban interface.

The main objective of this study was to assess the economics of alley cropping of loblolly pine (Pinus taeda L.) and switchgrass (Panicum virgatum) in the southern United States. Assuming a price range of switchgrass between $15 and $50 Mg−1 and yield of 12 Mg ha−1 year−1, we investigated the effect of switchgrass production on the optimal forest management for loblolly pine stands under different stumpage prices. We considered the following potential scenarios: no competition between species for resources; reduced loblolly pine productivity due to competition with switchgrass; and reduced productivity of both species due to competition for nutrients, water and light. Findings also suggested that the optimal system would depend on the competitive interactions between switchgrass and loblolly pine crops, and the expected prices for each crop. Loblolly pine monoculture would be the most profitable option for landowners compared to intercropping systems with switchgrass below $30 Mg−1. However, when switchgrass prices are ≥$30 Mg−1, landowners would be financially better off adopting intercropping if competitive interaction between crops were minimal. In order to realize higher economic returns for intercropping system, forest landowners must make some efforts in order to diminish the decline of productivity.

Efforts to restore the Lower Mississippi Alluvial Valley's forests have not achieved desired levels of ecosystem services production. We examined how the variability of returns and the flexibility to change or postpone decisions (option value) affects the economic potential of forestry and agroforestry systems to keep private land in production while still providing ecosystem services. A real options analysis examined the impact of flexibility in decision making under agriculture, forestry, and agroforestry and demonstrated that adoption of forestry or agroforestry systems is less feasible than would be predicted by deterministic capital budgeting models.

The economics of wildfire is complicated because wildfire behavior depends on the spatial and temporal scale at which management decisions made, and because of uncertainties surrounding the results of management actions. Like the wildfire processes they seek to manage, interventions through fire prevention programs, suppression, and fuels management are scale dependent and temporally and spatially dynamic. The objective of this chapter is to describe the status of research into the economics of fuels management. We review studies describing the economic question of fuel treatment choices in wildfire management. We discuss the importance of framing the questions and issues surrounding wildfire management to include influences of space and time on wildfire processes. Finally, we offer a case study that provides one example of evaluating the economics of fuel treatments.

This paper presents a synthesis of the mangrove ecosystem valuation literature through a meta-regression analysis. The main contribution of this study is that it is the first meta-analysis focusing solely on mangrove forests, whereas previous studies have included different types of wetlands. The number of studies included in the regression analysis is 44 for a total of 145 observations. We include several regressions with the objective of addressing outliers in the data as well as the possible correlations between observations of the same study. We also investigate possible interaction effects between type of service and GDP per capita. Our findings indicate that mangroves exhibit decreasing returns to scale, that GDP per capita has a positive effect on mangrove values and that using the replacement cost and contingent valuation methods produce higher estimates than do other methods. We also find that there are statistically significant interaction effects that influence the data. Finally, the results indicate that employing weighted regressions provide a better fit than others. However, in terms of forecast performance we find that all the estimated models performed similarly and were not able to conclude decisively that one outperforms the other.

Uncertainty surrounding the future supply of timber in the southern United States prompted the question, “Where is all the wood?” (Cubbage et al. 1995). We ask a similar question about the potential of southern forests to mitigate greenhouse gas (GHG) emissions by sequestering carbon. Because significant carbon sequestration potential occurs on individual nonindustrial private forest (NIPF) lands owned by individuals, the accuracy of projections depends on how NIPF landowners respond to prices and their ability and willingness to participate in carbon offset programs. Striving to produce a more realistic assessment of the potential for southern forests to sequester carbon in response to future markets or policies, we use National Woodland Owner Survey data from the Forest Inventory and Analysis program to link landowner demographic and behavioral data with forest conditions. We also examine barriers to NIPF participation in carbon offset programs and offer recommendations for overcoming those barriers.

Directly or indirectly, positively or negatively, climate change will affect all sectors and regions of the United States. The impacts, however, will not be homogenous across regions, sectors, population groups or time. The literature specifically related to how climate change will affect rural communities, their resilience, and adaptive capacity in the United States (U. S.) is scarce. This article bridges this knowledge gap through an extensive review of the current state of knowledge to make inferences about the rural communities vulnerability to climate change based on Intergovernmental Panel on Climate Change (IPCC) scenarios. Our analysis shows that rural communities tend to be more vulnerable than their urban counterparts due to factors such as demography, occupations, earnings, literacy, poverty incidence, and dependency on government funds. Climate change impacts on rural communities differs across regions and economic sectors; some will likely benefit while others lose. Rural communities engaged in agricultural and forest related activities in the Northeast might benefit, while those in the Southwest and Southeast could face additional water stress and increased energy cost respectively. Developing adaptation and mitigation policy options geared towards reducing climatic vulnerability of rural communities is warranted. A set of regional and local studies is needed to delineate climate change impacts across rural and urban communities, and to develop appropriate policies to mitigate these impacts. Integrating research across disciplines, strengthening research-policy linkages, integrating ecosystem services while undertaking resource valuation, and expanding alternative energy sources, might also enhance coping capacity of rural communities in face of future climate change.

This paper contrasts alternate methodological approaches of investigating public preferences, the random parameter logit (RPL) where tastes and preferences of respondents are assumed to be heterogeneous and the conditional logit (CL) approach where tastes and preferences remain fixed for individuals. We conducted a choice experiment to assess preferences for woody biomass based electricity in Arkansas, Florida, and Virginia. Reduction of CO2 emissions and improvement of forest habitat by decreasing risk of wildfires and pest outbreaks were presented to respondents as attributes of using green electricity. The results indicate that heterogeneous preferences might be a better fit for assessing preferences for green electricity. All levels of both attributes were positive contributors to welfare but they were no statistically significant. Respondents expressed a positive mean marginal willingness to pay (WTP) for each attribute level. The total WTP for green electricity per kilowatt hour was $0.049kWh or $40.5 per capita year−1 when converted into future total annual expenditures.

The 650 million ac of federal lands are facing increased scrutiny for wind energy development. As a result, the US Forest Service has been directed to develop policies and procedures for siting wind energy projects. We incorporate geospatial site suitability analysis with applicable policy and management principles to illustrate the use of a Spatial Decision Support System (SDSS) for evaluating the potential for wind energy development in the national forests. The SDSS is applied in a case study of the Nantahala and Pisgah National Forests (N PNF), ranked by the National Renewable Energy Laboratory as one of the top 25 national forests for wind energy development based on wind power, distance from transmission lines, distance from major roads, inventoried roadless areas and other specially designated areas, distance from urban areas, and topography (Karsteadt, R. et al. 2005. Assessing the potential for renewable energy on National Forest Systems lands. National Renewable Energy Laboratory and the US For. Serv. Available online at www.nrel.gov/wind/pdfs/36759.pdf ; last accessed Mar. 14, 2009). Our analysis further evaluates the N PNF potential for wind energy development using 16 environmental, construction, land designation, and policy variables. We find that the majority of the N PF is highly sensitive or exclusionary to wind energy development. Recommendations include the need for agencywide clarification of evaluation criteria for wind energy projects and prioritization of variables for evaluating future wind projects.

This report provides a comprehensive picture of current conditions and trends in our Nation's forests, its forest industries, and its forest communities. Although the first five criteria are centered in the environmental sphere of sustainability (with the exception of Criterion 2, which clearly overlaps the economic sphere), Criterion 6 is centered firmly in the economic sphere. As the sole criterion with an economic focus, it has more (20) indicators than any of the environmental criteria. Its first two subcategories reflect the basic economic breakdown of goods (e.g., wood products) and services (e.g., tourism). The full report is available at: http://www.fs.fed.us/research/sustain/