(693c) Conflict Resolution of Economic and Environmental Metrics in Dairy Waste Management

The dairy sector in the United States is a multi-billion enterprise that fosters diverse economic and social activities; the total dairy exports of the State of Wisconsin (WI) reached nearly 800 million US dollars in 2018 [1]. However, the dairy industry is facing several environmental challenges; dairy waste management practices generate significant greenhouse gas (GHG) emissions due to methane and nitrogen oxide released during land application of animal manure as fertilizer [2]. Moreover, nutrients contained in manure (nitrogen, phosphorus, and potassium) pollute waterbodies through runoff (e.g., cause harmful algae blooms) [3, 4]. Ammonia pollution in agricultural systems has also become an important concern; evidence shows that ammonia emissions have negative impacts on biodiversity, and it can increase suspended solids in the atmosphere [5,6].

Environmental impacts have drawn the attention of governments and researchers, and various studies have focused on exploring policy incentives to revert these trends [7]. Policy design is affected by inherent conflicts and synergies of diverse environmental impacts and metrics. In this context, decision-making methodologies are needed to uncover these conflicts and synergies. These methodologies leverage the use of life cycle analysis (LCA) to derive appropriate metrics that capture diverse impacts and can leverage the use of supply chain optimization techniques to determine suitable transportation routes for waste and derived products and processing technologies to mitigate environmental impacts [8,9].

In this presentation, we provide a computational framework that integrates LCA analysis and supply chain optimization to analyze environmental impacts arising from livestock waste in the Upper Yahara watershed in WI. Life cycle analyses are conducted for most prevalent management plans in the area, elements including waste collection, anaerobic digestion, solid-liquid separation, storage, and land application, etc. A supply chain optimization model is developed to include an economic metric (system profit) and diverse environmental metrics (GHG, ammonia, fossil energy use, and nutrient pollution). We use sampling methodology to reveal the correlation structure for the economic and environmental metrics. We find that the economic metric is conflicting with only a subset of environmental metrics and thus there is room to mitigate some impacts without sacrificing economics. A utopia-point technique is used to obtain an optimal compromise solution for the system. Finally, a series of case studies are presented to illustrate how policy incentives can be used to manipulate the utopia point and compromise solutions and with this change decision behavior of stakeholders.

References:
[1] United States Department of Agriculture, Economic Research Service, State Fact Sheet

[2] Aguirre-Villegas, Horacio A., and Rebecca A. Larson. "Evaluating greenhouse gas emissions from dairy manure management practices using survey data and lifecycle tools." Journal of cleaner production 143 (2017): 169-179.

[3] López-Díaz, Dulce Celeste, et al. "Systems-Level Analysis of Phosphorus Flows in the Dairy Supply Chain." ACS Sustainable Chemistry & Engineering 7.20 (2019): 17074-17087.

[4] Hu, Yicheng, et al. "Logistics Network Management of Livestock Waste for Spatiotemporal Control of Nutrient Pollution in Water Bodies." ACS Sustainable Chemistry & Engineering 7.22 (2019): 18359-18374.

[5] Guthrie, Susan, et al. "The impact of ammonia emissions from agriculture on biodiversity." The Royal Society (2018).

[6] Warner, J. X., et al. "Increased atmospheric ammonia over the world's major agricultural areas detected from space." Geophysical research letters 44.6 (2017): 2875-2884.

[7] Sampat, Apoorva M., Gerardo J. Ruiz-Mercado, and Victor M. Zavala. "Economic and environmental analysis for advancing sustainable management of livestock waste: A Wisconsin Case Study." ACS sustainable chemistry & engineering 6.5 (2018): 6018-6031.

[8] Sampat, Apoorva M., et al. "Optimization formulations for multi-product supply chain networks." Computers & Chemical Engineering 104 (2017): 296-310.

[9] You, Fengqi, and Belinda Wang. "Life cycle optimization of biomass-to-liquid supply chains with distributed–centralized processing networks." Industrial & Engineering Chemistry Research 50.17 (2011): 10102-10127.