Continued large-scale public investment in declining ecosystems depends on demonstrations of “success”. While the public conception of “success” often focuses on restoration to a pre-disturbance condition, the scientific community is more likely to measure success in terms of improved ecosystem health. Using a combination of literature review, workshops and expert solicitation we propose a generalized framework to improve ecosystem health in highly altered river basins by reducing ecosystem stressors, enhancing ecosystem processes and increasing ecosystem resilience. We illustrate the use of this framework in the Mississippi-Atchafalaya River Basin (MARB) of the central United States (U.S.), by (i) identifying key stressors related to human activities, and (ii) creating a conceptual ecosystem model relating those stressors to effects on ecosystem structure and processes. As a result of our analysis, we identify a set of landscape-level indicators of ecosystem health, emphasizing leading indicators of stressor removal (e.g., reduced anthropogenic nutrient inputs), increased ecosystem function (e.g., increased water storage in the landscape) and increased resilience (e.g., changes in the percentage of perennial vegetative cover). We suggest that by including these indicators, along with lagging indicators such as direct measurements of water quality, stakeholders will be better able to assess the effectiveness of management actions. For example, if both leading and lagging indicators show improvement over time, then management actions are on track to attain desired ecosystem condition. If, however, leading indicators are not improving or even declining, then fundamental challenges to ecosystem health remain to be addressed and failure to address these will ultimately lead to declines in lagging indicators such as water quality. Although our model and indicators are specific to the MARB, we believe that the generalized framework and the process of model and indicator development will be valuable in an array of altered river basins.

Greenhouse gas (GHG) emissions from beef production in the United States are unevenly distributed across the supply chain and production regions, complicating where and how to reduce emissions most effectively. Using spatially explicit life cycle assessment methods, we quantify the baseline GHG emissions and mitigation opportunities of 42 practices spanning the supply chain from crop and livestock production to processing. We find that the potential to reduce GHGs across the beef sector ranges up to 30% (20 million tonnes CO2e reduced and 58 million tonnes CO2 sequestered each year relative to the baseline) under ubiquitous adoption assumptions, largely driven by opportunities in the grazing stage. Opportunities to reduce GHGs in the feed, grazing and feedlot stages vary across regions, yet large-scale adoption across the entire beef supply chain is important. These findings reveal promising locations and practices to invest in to advance mitigation goals and an upper-end theoretical potential for mitigation in the beef industry. The United States is the world's largest beef producer. Identifying strategies to mitigate its GHG emissions remains a challenge due to sector complexity and heterogeneity. This study takes an LCA approach to quantify potential mitigation opportunities available or soon to be available for the beef sector.

Purpose The environmental impact of a product may change according to who adopts it, where it is adopted, and how it is used. Market forces are an inherent part of consequential LCA and the practice of coupling economic models with life cycle inventory data has increased in popularity. Nevertheless, the actual relationship between the price of a commodity and potential changes to its life cycle inventory has rarely been discussed explicitly. The adoption price effect refers to a change in a product's environmental impact associated with a change in price, calculated on a functional unit basis. The price of a product influences the type and quantity of incumbent product(s) it displaces. This study provides insights on when the adoption price of a product is likely to influence its life cycle inventory and also identifies conditions where adoption price is expected to have negligible effects on inventory results. Methods A switchgrass bioenergy case is used to demonstrate the adoption price effect on life cycle inventory results when introducing a new product (i.e., switchgrass) into a system with multiple incumbents (i.e., crops, hay, pasture). This study estimates the adoption price effect on nutrient emissions by coupling biogeochemical models with a simplified economic breakeven model that estimates potential switchgrass adoption. Results and discussion In this case study, high switchgrass prices correspond to nitrate emission reductions that are three times greater than low switchgrass prices (0.67 kg N reduced/Mg switchgrass vs 0.21 kg N reduced/Mg switchgrass). The large adoption price effect found within the Southeastern USA is due to the highly heterogeneous landscape in the region. There is no single dominant land use, each incumbent product has a different environmental baseline, and each is displaced at a different switchgrass price range. Meanwhile, the adoption price effect is expected to be negligible in mostly homogenous landscapes, such as the more commonly studied Corn Belt, which has a single dominant incumbent in the form of corn-soy production. In addition to the specific case study, this analysis discusses general adoption conditions likely to lead to adoption price effects when conducting consequential LCA.

The bioenergy mandate under Energy Independence and Security Act (EISA 2007) will result in large scale land-use changes in the US if it is ever fully realized. Energy crops such as switchgrass (Panicum virgatum) and miscanthus (Miscanthus X giganteus) can grow well throughout the continental United States; however, most studies on the water quality impacts of land-use change from bioenergy policy are based in the Midwest. Insufficient research has been conducted to determine whether the results from the Midwest are applicable to other regions in the United States. This study compares water quality impacts of converting from corn to switchgrass in the Midwest (Upper Cedar watershed) and Southeast (Lumber watershed) with nitrogen loading to surface water as a proxy metric for overall water quality impacts using the Soil and Water Assessment Tool (SWAT). The analysis shows that the fraction of nitrogen runoff compared to the fertilizer applied is 36% in Upper Cedar as compared to 6% in Lumber. Existing management practices like tile-drainage and land-cover like riparian wetlands contribute to this difference in the baseline nitrate export from each watershed. The potential reduction in nitrogen loading per potential liter of ethanol to surface waters, which account for both nitrate export and productivities, from the baseline corn-soy to switchgrass is about 40% for the Upper Cedar watershed and around 80% for the Lumber watershed. Although, the trend in reduction is similar in both watersheds, this study shows that results extrapolated from the Midwest may not be representative of other bioenergy producing regions.