Socio-psychological factors influencing intent to adopt conservation practices in the Minnesota River Basin.

In the upper Midwestern United States, one of the central goals of agri-environmental policy is to reduce environmental and water quality degradation resulting from agriculture without sacrificing production. The primary tool available to policymakers is offering farmers incentives to voluntarily adopt more conservation practices, often known as Best Management Practices (BMPs). Using the Theory of Planned Behavior (TPB) and Diffusion of Innovation (DoI) frameworks, we surveyed 2000 agricultural landowners in the Minnesota River Basin to explore the socio-psychological drivers of the adoption decisions for specific BMPs such as wetlands, cover crops, and nutrient management. We found that attitude (both favorable and unfavorable), awareness of environmental problems, and appreciation of ecosystem services significantly affected landowners' adoption intentions for the three BMPs. We applied landowner segmentation analysis and compared both the socio-psychological and socio-demographic features among different landowner segments (i.e. environmentally-conscious landowners, engaging-absentee landowners, and adoption-averse landowners). Our study can inform the development of targeted conservation policies for various landowner types to motivate BMPs adoption.

Integrated assessment modeling reveals near-channel management as cost-effective to improve water quality in agricultural watersheds.

Despite decades of policy that strives to reduce nutrient and sediment export from agricultural fields, surface water quality in intensively managed agricultural landscapes remains highly degraded. Recent analyses show that current conservation efforts are not sufficient to reverse widespread water degradation in Midwestern agricultural systems. Intensifying row crop agriculture and increasing climate pressure require a more integrated approach to water quality management that addresses diverse sources of nutrients and sediment and off-field mitigation actions. We used multiobjective optimization analysis and integrated three biophysical models to evaluate the cost-effectiveness of alternative portfolios of watershed management practices at achieving nitrate and suspended sediment reduction goals in an agricultural basin of the Upper Midwestern United States. Integrating watershed-scale models enabled the inclusion of near-channel management alongside more typical field management and thus directly the comparison of cost-effectiveness across portfolios. The optimization analysis revealed that fluvial wetlands (i.e., wide, slow-flowing, vegetated water bodies within the riverine corridor) are the single-most cost-effective management action to reduce both nitrate and sediment loads and will be essential for meeting moderate to aggressive water quality targets. Although highly cost-effective, wetland construction was costly compared to other practices, and it was not selected in portfolios at low investment levels. Wetland performance was sensitive to placement, emphasizing the importance of watershed scale planning to realize potential benefits of wetland restorations. We conclude that extensive interagency cooperation and coordination at a watershed scale is required to achieve substantial, economically viable improvements in water quality under intensive row crop agricultural production.

Quantifying cumulative effectiveness of green stormwater infrastructure in improving water quality.

Stormwater runoff is one of the main sources of pollution in streams and receiving water bodies of major cities. Green Stormwater Infrastructure (GSI) is a set of distributed stormwater best management practices that absorb excess water, filter out sediment and pollutants, and help recharge groundwater. Despite the increasing popularity of GSI as means of stormwater management, our knowledge of their cumulative performance is limited. In this research, we apply an empirical approach to study the effectiveness of GSI in improving the water quality of four major receiving water bodies of Seattle, Washington, at the watershed scale. We use a Bayesian structural time series model and synthetic control method to build counterfactual scenarios of water quality in absence of GSI implementation and estimate the causal impact of GSI on water quality. We use monthly time series data of water quality parameters (water temperature, dissolved oxygen, surface PAR, chlorophyll a, Secchi depth, pH, light transmission, and fecal coliform) in Seattle's urban watersheds from 2004 to 2017. We also use a set of nine control variables to estimate the counterfactual water quality parameters. Our findings show that GSI improve some water quality parameters such as chlorophyll a, light transmission and Secchi depth, but increase water temperature and decrease dissolved oxygen in some water bodies.

Forward-looking farmers owning multiple potential wetland restoration sites: implications for efficient restoration.

Wetland restoration can increase the provision of multiple non-market ecosystem services. Environmental and socio-economic factors need to be accounted for when land is withdrawn from agriculture and wetlands are restored. We build multi-objective optimization models to provide decision support for wetland restoration in the Le Sueur river watershed in Southern Minnesota. We integrate environmental objectives of sediment reduction and habitat protection with socio-economic factors associated with the overlap of private land with potential wetland restoration sites in the watershed and the costs representing forward-looking farmers voluntarily taking land out of agricultural production in favor of wetland restoration. Our results demonstrate that the inclusion of these factors early on in the restoration planning process affects both the total costs of the restoration project and the spatial distribution of optimally selected wetland restoration sites.

Measuring recreational visitation at U.S. National Parks with crowd-sourced photographs.

Land managers rely on visitation data to inform policy and management decisions. However, visitation data is often costly and burdensome to obtain, and provides a limited depth of information. In this paper, we assess the validity of using crowd-sourced, online photographs to infer information about the habits and preferences of recreational visitors by comparing empirical data from the National Park Service to photograph data from the online platform Flickr for 38 National Parks in the western United States. Using multiple regression analysis, we find that the number of photos posted monthly in a park can reliably indicate the number of visitors to a park in a given month. Through additional statistical testing we also find that the home locations of photo-takers, provided voluntarily on an online profile, accurately show the home origins of park visitors. Together, these findings validate a new method for measuring recreational visitation, opening an opportunity for land managers worldwide to track and understand visitation by augmenting current data collection methods with crowd-sourced, online data that is easy and inexpensive to obtain. In addition, it enables future research on how visitation rates change with changes in access, management or infrastructure, weather events, or ecosystem health, and facilitates valuation research, such as travel cost studies.

Paying for Forest Ecosystem Services: Voluntary Versus Mandatory Payments.

The emergence of new markets for forest ecosystem services can be a compelling opportunity for market diversification for private forest landowners, while increasing the provision of public goods from private lands. However, there is limited information available on the willingness-to-pay (WTP) for specific forest ecosystem services, particularly across different ecosystem market mechanisms. We utilize survey data from Oregon and Washington households to compare marginal WTP for forest ecosystem services and the total WTP for cost-effective bundles of forest ecosystem services obtained from a typical Pacific Northwest forest across two value elicitation formats representing two different ecosystem market mechanisms: an incentive-compatible choice experiment involving mandatory tax payments and a hypothetical private provision scenario modeled as eliciting contributions to the preferred forest management alternative via a provision point mechanism with a refund. A representative household's total WTP for the average forest management program was estimated at $217.59 per household/year under a mandatory tax mechanism and $160.44 per household/per year under a voluntary, crowdfunding-style, contribution mechanism; however, these estimates are not statistically different. Marginal WTP estimates were assessed for particular forest ecosystem service attributes including water quality, carbon storage, mature forest habitat, and public recreational access. This study finds that survey respondents place significant economic value on forest ecosystem services in both elicitation formats and that the distributions of the marginal WTP are not statistically significantly different.

Cost-effective targeting of conservation investments to reduce the northern Gulf of Mexico hypoxic zone.

A seasonally occurring summer hypoxic (low oxygen) zone in the northern Gulf of Mexico is the second largest in the world. Reductions in nutrients from agricultural cropland in its watershed are needed to reduce the hypoxic zone size to the national policy goal of 5,000 km(2) (as a 5-y running average) set by the national Gulf of Mexico Task Force's Action Plan. We develop an integrated assessment model linking the water quality effects of cropland conservation investment decisions on the more than 550 agricultural subwatersheds that deliver nutrients into the Gulf with a hypoxic zone model. We use this integrated assessment model to identify the most cost-effective subwatersheds to target for cropland conservation investments. We consider targeting of the location (which subwatersheds to treat) and the extent of conservation investment to undertake (how much cropland within a subwatershed to treat). We use process models to simulate the dynamics of the effects of cropland conservation investments on nutrient delivery to the Gulf and use an evolutionary algorithm to solve the optimization problem. Model results suggest that by targeting cropland conservation investments to the most cost-effective location and extent of coverage, the Action Plan goal of 5,000 km(2) can be achieved at a cost of $2.7 billion annually. A large set of cost-hypoxia tradeoffs is developed, ranging from the baseline to the nontargeted adoption of the most aggressive cropland conservation investments in all subwatersheds (estimated to reduce the hypoxic zone to less than 3,000 km(2) at a cost of $5.6 billion annually).

Spatial multiobjective optimization of agricultural conservation practices using a SWAT model and an evolutionary algorithm.

Finding the cost-efficient (i.e., lowest-cost) ways of targeting conservation practice investments for the achievement of specific water quality goals across the landscape is of primary importance in watershed management. Traditional economics methods of finding the lowest-cost solution in the watershed context (e.g.,(5,12,20)) assume that off-site impacts can be accurately described as a proportion of on-site pollution generated. Such approaches are unlikely to be representative of the actual pollution process in a watershed, where the impacts of polluting sources are often determined by complex biophysical processes. The use of modern physically-based, spatially distributed hydrologic simulation models allows for a greater degree of realism in terms of process representation but requires a development of a simulation-optimization framework where the model becomes an integral part of optimization. Evolutionary algorithms appear to be a particularly useful optimization tool, able to deal with the combinatorial nature of a watershed simulation-optimization problem and allowing the use of the full water quality model. Evolutionary algorithms treat a particular spatial allocation of conservation practices in a watershed as a candidate solution and utilize sets (populations) of candidate solutions iteratively applying stochastic operators of selection, recombination, and mutation to find improvements with respect to the optimization objectives. The optimization objectives in this case are to minimize nonpoint-source pollution in the watershed, simultaneously minimizing the cost of conservation practices. A recent and expanding set of research is attempting to use similar methods and integrates water quality models with broadly defined evolutionary optimization methods(3,4,9,10,13-15,17-19,22,23,25). In this application, we demonstrate a program which follows Rabotyagov et al.'s approach and integrates a modern and commonly used SWAT water quality model(7) with a multiobjective evolutionary algorithm SPEA2(26), and user-specified set of conservation practices and their costs to search for the complete tradeoff frontiers between costs of conservation practices and user-specified water quality objectives. The frontiers quantify the tradeoffs faced by the watershed managers by presenting the full range of costs associated with various water quality improvement goals. The program allows for a selection of watershed configurations achieving specified water quality improvement goals and a production of maps of optimized placement of conservation practices.

Least-cost control of agricultural nutrient contributions to the Gulf of Mexico hypoxic zone.

In 2008, the hypoxic zone in the Gulf of Mexico, measuring 20 720 km2, was one of the two largest reported since measurement of the zone began in 1985. The extent of the hypoxic zone is related to nitrogen and phosphorous loadings originating on agricultural fields in the upper Midwest. This study combines the tools of evolutionary computation with a water quality model and cost data to develop a trade-off frontier for the Upper Mississippi River Basin specifying the least cost of achieving nutrient reductions and the location of the agricultural conservation practices needed. The frontier allows policymakers and stakeholders to explicitly see the trade-offs between cost and nutrient reductions. For example, the cost of reducing annual nitrate-N loadings by 30% is estimated to be US$1.4 billion/year, with a concomitant 36% reduction in P and the cost of reducing annual P loadings by 30% is estimated to be US$370 million/year, with a concomitant 9% reduction in nitrate-N.