Quantifying Ecosystem Payment Benefits

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Researchers have proposed many definitions of “ecosystem services.” We use the broad definition of “… aspects of ecosystems utilized (actively or passively) to produce human well-being [1, pp. 645].” Alcamo et al. [5] suggest that to manage that managing them requires an integrated, multidisciplinary approach to evaluating, measuring, and valuing ecosystem services. Here, we take a multidisciplinary modeling approach to evaluate incentives of agricultural landowners to provide ecosystem services in the form of improvements in water quality.
Agricultural production plays a significant role in water quality at various levels. For the most part, these impacts and benefits are not valued by existing markets. Because these values are not captured by existing markets, those producing and degrading an ecosystem service do not shoulder the burden or reap the benefits of their actions; this creates an externality [6]. Public policy often addresses the market failure created by these externalities [7]. Peterson, Boisvert, and de Gorter [8] suggest that public policy could address the market failure by pricing nonmarket ecosystem services and charging users accordingly. Such policy intervention typically takes the form of either direct regulation (command-and-control) or incentive-based policies. It has been suggested that incentive-based policies, including emissions trading, may be more efficient and effective than direct regulation [1,9–11].1 However, many emissions trading programs have not lived up to their theoretical expectations [12]. Horan and Shortle [13] suggest that poor institutional structure and a lack of consideration for specific ecological conditions may be to blame, while others point out that there is a lack of credit demand due to multi-policy interactions and poor incentive design [14–16]. Stephenson and Shabman [12] suggest that “a better understanding… of market-like principles can result in an improved design of trading… [pp. 15]”

Quantifying Ecosystem Payment Benefits

NPS pollution presents a unique challenge for policy design. Nonpoint source pollution is relatively difficult and expensive to directly monitor compared to point source pollution [17,18]. This difficulty may result in reduced environmental outcomes and reduced NPS participation.
Corrigan [162] suggests that WQT may be “fundamentally contrary to the language, structure, and purpose of the [Clean Water Act].” In this paper, we assume that emissions trading has legal footing under the Clean Water Act.
Several efforts have been made to design and evaluate WQT programs using a modeling, rather than a measurement, approach.

Biophysical modeling

The biophysical impacts of WQT programs have been evaluated using several models. In Virginia, policymakers and researchers have used various tools, such as BayFAST, CAST, and the USDA’s Nutrient Tracking Tool, which are user interfaces that simplify and facilitate access to complex biophysical models. SWAT, APEX, and EPIC are examples of models running behind the scenes. Although the user interfaces make the complex biophysical models more accessible to various types of users by making parameter assumptions and reducing the number of user inputs, this approach sacrifices flexibility. The tools are simultaneously too coarse to look at nuanced land management options, and too fine to be easily broken down and adapted to other policy schemes. An expandable, modular, modeling method may increase model accuracy by increasing model flexibility, and, with proper design, could remain accessible.

 Bioeconomic modeling

A common obstacle in WQT policy design is tightly coupling environmental outcomes and incentive schemes in a manner such that economic outcomes reflect ecosystem service values. The dispersed nature of NPS loading typically restricts physically measured biophysical outcome analysis to the watershed level, which does not allow polluter liability to be assigned [18]. Because polluter liability is difficult and expensive to assign, the farm-level effectiveness and efficiency of an incentive program is difficult to quantify. Therefore, simulation modeling, rather than in-stream measurement, may be required to couple the biophysical and economic elements of a policy.
Study scale, data availability, and data applicability also need to be considered when designing a bioeconomic model for policy analysis. For example, Corrales et al. [19] builds a WQT bioeconomic model that minimizes the costs of achieving a target level of abatement. This method allows for clear and concise comparison of different policy options (e.g. command-and-control vs. emissions trading) at the regional scale, but is not as useful when comparing policies at the farm level. This modeling approach is designed to address questions related to the efficient spatial distribution of abatement across heterogeneous farms. Given that the focus in these models is across farms, they often sacrifice nuance at the farm scale that can influence BMP efficacy and adoption costs. On the other hand, models that depend on detailed and site-specific data within a farm, such as Hite et al. [20], may be too restrictive for widespread application. Their approach requires year- and area-specific data that may make it difficult to perform ex-ante analysis on hypothetical farm-level compliance options.
In this study, we strive to balance these two approaches, developing a model with sufficient detail at the farm level to capture nuances affecting BMP adoption, while also remaining parsimonious in terms of data requirements in an effort to support prospective policy analysis. In addition, we want to maintain an ability to quickly and systematically alter input and control parameters in a manner that allows for both informed and entirely hypothetical state-of-the-world conditions. McKenney et al. [21] support elements of our approach when evaluating research priorities, stating that “…extensive sensitivity analyses and using elasticity-related metrics…” may provide insight into both bioeconomic model calibration and subsequent tradeoff analysis. McKenney et al. [21] go on to support a “…systematic, quantitative, and repeatable…” approach to a model that allows “…critical discussions around objectives…” Our approach to this model not only needs to allow two-sided bioeconomic analysis, it also needs to provide some insight to environmental-economic tradeoffs, under the assumption that some tradeoff must take place at the farm- or social-level [22,23].

Modeling Environmental-Economic Tradeoffs

We use the production possibility frontier (PPF) as the basis for our analysis of environmental-economic tradeoffs for a suite of land-use alternatives. From the literature, we find four primary lessons in developing such a model. First, we assume an efficient frontier exists. Eichorn and Dreschler [24] describe a set of management scenarios that produce an efficient curve of possible combinations of wind power production and bird fatalities based on wind turbine placement. Although the paper does not explicitly follow the PPF framework, it shows that this assumption is important because it defines the bounds of the problem; if an efficient frontier exists, than any movement across the PPF surface that brings a management strategy closer to the frontier is relatively efficient compared to the baseline strategy.
Second, a well-designed tradeoff model maintains generality. Calkin et al. [25] develop a model to evaluate tradeoffs between timber production and wildlife species persistence. They find that even though the initial model conditions are highly specific to the site and situation, the general modeling approach used allows the model to be re-parameterized and calibrated to handle a variety of situations. Because the application objectives of our model include the ability of a user to evaluate very different management types (including complex agroforestry practices) across different regions and conditions, this flexibility is essential for developing a useful analytical tool.
Third, landowner objectives need to be considered when developing and testing the model. Lichtenstein and Montgomery [26] develop a tradeoff model between timber production and biodiversity, and find that the complex sets of both management possibilities and natural conditions require a careful approach to set selection. The agroforestry systems we evaluate in this paper are also complex, and landowners with differing objectives may see the tradeoff possibilities in different lights. For more information on classifying landowner types and the applications those types have on policy design, see Skevas et al. [27], Valbuena et al. [28], Daloǧlu et al. [29], Kostrowicki et al. [30], Tavernier and Tolomeo [31], as well as similar literature concerning forest landowners [32–34].
Finally, the economic literature shows that the shape of the PPF is both readily impacted by model conditions, and important to analysis and recommendations. Wossink and Swinton [35] use a PPF in a tradeoff analysis between agricultural commodity production and ecosystem service production that can either complement or supplement each other.. For example, soil formation and retention provided by a riparian buffer rewarded with ecosystem service payments would also benefit commodity production by improving the efficiency of nutrient fertilizer via legacy additions. In this case, the PPF is concave over its domain, but increasing at low levels of ecosystem services. This suggests that farmers may place a higher value (both marginally and absolutely) on small ecosystem service improvements than large improvements while both values are increasing. Interestingly, Juutinen et al. [36] find a tradeoff between timber production and biodiversity in a Finnish case study yields an efficiency frontier that is relatively linear. This result suggests that multi-objective, complex approaches to management problems may be a cost-effective way to address tradeoffs. Swinton et al. [37] reinforces the importance of PPF shape, and also adds that the starting location (i.e., status quo landowner decisions) can make a big difference in decision making across different types of efficiency curves.

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Incorporating risk

Agriculture is fraught with uncertainty, and that uncertainty is an important factor in farmers’ decision-making [38,39]. Producer income is the product of complex global and local ecological systems. Significant effort has been made to predict agricultural production, but farming is still intrinsically risky [40]. For an overview of empirical risk analysis in agriculture, see Anderson and Dillon [41]. Here, we focus on the risk literature most directly relevant to the study.
Early risk analysis in agriculture, such as Pratt [42] and Newberry and Stiglitz [43], tends to focus on eliciting individual utility functions and risk aversion coefficients. This approach has been used in studies that look at the benefit of income variance reduction of farmers [44–46].
However, Lien et al. [47] find that individual utility functions for forest producers can be difficult to elicit, and suggest using certainty equivalent (CE), defined as the amount a decision-maker would be willing to pay to avoid a gamble, and risk premium (RP), defined as the amount a decision-maker would need to be compensated to take on a given amount of risk, as metrics that are comparable across farmers and management types. Chavas and Shi [48] estimate CE to show that the introduction of genetically modified corn seeds in Wisconsin, USA may be preferable to risk averse farmers.
The literature also tells us risk should be considered when looking at agroforestry and ecosystem service payments for three primary reasons: 1) diverse management systems may be attractive to risk averse farmers; 2) new management systems may be difficult for risk averse farmers to adopt; and 3) the complexity and lack of knowledge of new systems may make efficient and effective policy design difficult [49].
Portfolio theory shows that investment diversification may lead to positive outcomes for risk averse participants [50]. Blandon [51,52] shows that agroforestry producers may react the same way to risk reduction through diversification as investors. However, Babu and Rajasekaran [53] point out that the various sources and relative levels of risk within a complex “portfolio” need to be carefully considered, as it is possible to diversify a farm into negative outcomes. So, farm diversification can cause either positive or negative outcomes to farmers, and is largely a function of the level of farmer risk aversion and relative levels of the sources of risk within the system.
Adoption of conservation practices is also partly a function of risk. For example, Kingwell [54] finds that risk aversion principles impact farmer decisions both in the long and short run. In the short run (annual or even seasonal), farmers are quick to react to risky conditions by altering their riskiest crops. Agroforestry management practices may be useful in this sense, because the spatially and temporally varied systems can be relatively easily modified. Aimin [38] suggests that riskier conditions in China lead to farmers intercropping more frequently, which supports the same notion. On the other hand, adopting new technology and practices by risk averse participants, in any context, often proves to be a barrier that time and social persuasion need to address [55].
Additionally, either in agroforestry or emissions trading, risk presents issues in all parts of policy design and implementation [56]. Acs et al. [57], while using a dynamic programming model to analyze farmer adoption of organic crops, found that the small markets for organic products may exaggerate long term risk. As small markets continue to develop, such as those for the niche products and WQT credits examined in this study, a more accurate risk assessment can be made. As far as policy design goes, Canales et al. [58] point out that the interaction of risk aversion, contract length, and institutional trust impacts farmer participation in a carbon trading program. Contract type and length and institutional framework may also be barriers to BMP adoption in Virginia that are not currently included in this analysis. Finally, data availability is an issue when empirically estimating the effects of risk. Variance data for the crops and credits associated with agroforestry and WQT are scant, as discussed by Debnath et al. [59]. By using vegetative models and data drawn from similar crops, they estimate the risk effects in switchgrass bioenergy markets. Using their work as an example, we overcome issues with data availability by using vegetative simulation models.

1.1. Objectives
1.2. Organization
2.1. Quantifying Ecosystem Payment Benefits
2.2. Incorporating Risk
3.1. Biophysical Theory
3.2. Economic Theory
4.1. Chesapeake Bay
4.2. Agroforestry
5.1. Software
5.2. Model Layers
5.3. Biophysical Model
5.4. Economic Model
5.5. Risk and Uncertainty
5.6. Sensitivity Analysis
6.1. Baseline Simulation Results
6.2. Sensitivity Analysis Results
6.3. Risk Analysis Results
7.1. Implications
7.2. Further Research
7.3. Conclusions

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