Conference Agenda

Overview and details of the sessions of this conference. Please select a date or location to show only sessions at that day or location. Please select a single session for detailed view (with abstracts and downloads if available).

 
Session Overview
Session
FEW-2: Agriculture & Water
Time:
Tuesday, 25/Jun/2019:
4:00pm - 5:30pm

Session Chair: Yuan Yao
Location: Weidler/Halsey

Presentations
4:00pm - 4:20pm

Food-Grade Products from Food-Grade Wastes Applied to Urban Agriculture

Peter J Lammers1, Mark Seger1, John McGowen1, Weiss Taylor1, John Edel2, Leonard Lerer3

1Arizona State University, Mesa AZ, USA; 2Bubbly Dyamics, Chicago, IL USA; 3Back of the Yards Algae Sciences, Chicago, IL USA

Estimates of wastage in the global food supply chain vary widely by region but are believed to be as high as 50% of production totals (Parfitt et al., 2010). Much of this waste ends up in U.S. landfills contributing carbon dioxide and methane release to the atmosphere with attendant impacts on climate change. The logistics of intercepting these varied, food-grade waste streams is highly location specific. Here we describe a minimal “biorefinery” concept in which the biomass feedstock consists of pre- and post-consumer food-grade wastes gathered and processed within the boundaries of a large urban setting (The Plant, Chicago, IL). Feedstock components (distillers grains, food processing, restaurant and other collectable post-consumer food waste) are fed to an anaerobic disgester (AD) system yielding biogas, a low-carbon sludge and a nitrogen and phosphorus-rich wastewater. The biogas is combusted for process heat and the resulting carbon dioxide is recaptured via cultivation of two different photosynthetic microorganisms. Galdieria sulphuraria is a thermophilic, acidophilic microalga that yields 5-10 grams ash-free dry weight algal biomass per liter using undiluted AD wastewater as the nutrient source photobioreactors operating at 42-50 C and pH 2 will removing excess ammonium and phosphate and yielding clean water. We will show these hot, acidic conditions kill most microorganisms carried forward from the AD step. The cyanobacterium Spirulina (Arthrospira) is also readily cultivated in diluted AD wastewater and already FDA-approved for nutraceutical uses, thus providing additional downstream revenue options. Biomass from both strains provide a food-grade materials from which high-value nutraceuticals like phycocyanin pigment/antioxidant and plant bio-stimulants are extracted. The remaining carbohydrate fraction of the biomass is processed into flour and the protein fraction sold into the animal feed market. The overall process functionally “circularizes” the urban food supply chain while providing important ecosystem services.

Parfitt, J., Barthel, M., Macnaughton, S. 2010. Food waste within food supply chains: quantification and potential for change to 2050. Philosophical Transactions of the Royal Society B-Biological Sciences, 365(1554), 3065-3081.



4:20pm - 4:40pm

An Integrated Life-Cycle Modeling Framework for Dynamic Agriculture Systems

Kai Lan, Yuan Yao

North Carolina State University, United States of America

Given the rapid increase of food demand due to population growth, it is critical to develop effective policies and management strategies to enhance the environmental and economic sustainability of agricultural activities. System evaluations for different strategies and their potential in reducing environmental impacts are needed for decision making and strategy implementation. Previous studies have used Life Cycle Assessment (LCA) and Techno-Economic Analysis (TEA) to evaluate the environmental impacts and economic gains of different farming activities and strategic scenarios. However, most of the previous models are static without considering geographic and temporal dynamics, nor do they consider variability in individual farm’s decision that may have large impacts on the environmental impacts of overall agriculture systems.

This study addresses the gap by developing an integrated modeling framework that couples LCA, TEA, and agent-based modeling (ABM). ABM was used to model each farm as an autonomous agent who can make decisions on crop selection and fertilizer usage according to different attributes such as interactions with other farms, farm size, profitability, environmental awareness (or say the attitude towards environmental conservation), soil quality, and forecasting accuracy. More attributes could be added given the flexibility of the modeling framework. TEA was built upon dynamic simulation models of crop yields, crop cultivation costs, and crop selling prices. TEA, LCA, and ABM were mathematically linked to allow dynamic simulations of life-cycle environmental impacts and profitability of plantation activities when variability of individual farms was taken into consideration. A case study of 1,000 farms in 30 years timeframe at North Carolina was conducted for demonstration. Different scenarios were developed to investigate the impacts of farms’ attributes, socio-economic factors, and intervention strategies on farmers’ decision-making and the environmental impacts of overall agriculture systems.

The preliminary results of the case study indicate that information exchanges among farmers, environmental awareness of farmers, access to environmental footprint information, and farm size are the key factors driving the system-level environmental impacts. Enhancing the environmental awareness of farmers has potential to significantly reduce environmental impacts, (e.g., reducing GWP by 10- 31%, life-cycle primary energy by 10 - 29%, and water footprint by 8 - 35%). One interesting observation from the simulation was that switching to more environmentally friendly options (e.g., using fewer fertilizers) generally reduced profits, or say led to profit loss. However, profits losses were not linearly increased with environmental awareness. In general, farms with higher environmental awareness and more information exchange with other farmers have less profit loss than those with lower environmental awareness with limited information sharing with others. Another finding of the case study is that providing farms with timely feedback on the environmental impacts of farmer’s past decisions could affect farmers’ choices in the following years and as a result leads to more reductions in most environmental impacts. The results of this study can provide a variety of stakeholders (e.g., policymakers, nonprofits, agriculture companies) with insightful information to develop and tailor their strategies for effectively managing the environmental footprints of large-scale agriculture systems. The integrated modeling framework has the potential to be applied to other systems that are dynamic and have complex interactions among human and nature systems.



4:40pm - 5:00pm

Full Batch Reverse Osmosis Integration with different Renewable Sources

Eduardo J. Fenollal1,2, Timothy Robert Simon1, Bernadi Restrepo Torres2, David Warsinger1, Luciano Castillo1

1Purdue University, United States of America; 2Universidad Ana G Mendez

Societies all over the world struggle to live every day without access to clean water. This becomes a growing issue as climate change exacerbates worldwide leaving regions in severe drought and flooding conditions. With 96% of Earths water being sea water a prominent solution to the water crisis is desalination. Production of desalination plants using Reverse Osmosis (RO) are becoming more prevalent all over the world. Nevertheless, RO plants exhibit some severe issues including energy consumption resulting in high costs and greater atmospheric emissions. RO is a pressure driven process with pressure values dependent on feedwater characteristics such as salt concentration, pH, heavy metals, and others. The high-pressure requirement from RO systems is the source of the majority of energy consumption in the system. The purpose of this research project is to consider an experimental setup which optimizes RO design to run a Full Batch RO system with different-integrated (water-energy nexus) renewable energy sources such as solar, wind, geothermal, and hydroelectric. The Full Batch system will minimize energy consumption while increasing the efficiency of water production employing less RO membrane modules. The region of focus for this project is Peru, a country known for its clean water scarcity. With a high solar index, abundant hydroelectric potential, high costal wind speeds, and immense geothermal activity Peru is a region that offers a variety of renewable energy resources capable of supplying ample amounts of electricity to an RO system for lower cost clean water. Additionally, by using RO the system would be capable of filtering water containing impurities commonly found from mining practices in the region of Peru. With a solution to the Peruvian water crisis more affordable fresh water can be supplied to a society in need while limiting the environmental impact.



5:00pm - 5:20pm

Updating Life Cycle Assessment of Protein Rich Foods to Account for Digestible Indispensable Amino Acid Scores

Andrew James Berardy, Carol Johnston, Alexandra Plukis, Maricarmen Vizcaino, Christopher Wharton

Arizona State University, United States of America

The decision-making process related to sustainability in the food system likely requires novel approaches that evaluate food products in terms of both quality and impact. Life cycle assessment (LCA) enables one aspect of decision-making by examining the environmental footprint of a product from material extraction through use and final disposal. Along these lines, applications of LCA to dietary choices, in particular protein-rich foods, often indicate that plant-based options are environmentally preferable to animal-based options. However, such comparisons are usually based on the functional unit of protein per amount of product, which captures quantity, but not quality, of protein. Given the biological importance of protein quality to health outcomes, we developed a new methodology for the incorporation of protein quality as well as quantity using the digestible indispensable amino acid scores (DIAAS) integrated with LCA data. DIAAS is a state-of-the-art scoring system that provides the most comprehensive and accurate evaluation of how protein is utilized in the body, providing the fairest available comparison basis when considering protein sources that may have differing qualities in terms of both completeness of amino acid profile and digestibility. Further, our new methodology incorporates the US Food and Drug Administration’s reference amounts customarily consumed (RACCs) to better represent the typical amount of protein foods consumed. Through integrating DIAAS and RACCs into LCA-data based comparisons, we identified foods that offer the best balance between the biological value of protein in a given food and associated environmental impacts in scenarios that reflect actual consumption patterns. To integrate these components, we developed a weighted protein score formula, “PRO”. PRO is equal to DIAAS (D) as a percentage multiplied by RACCs (R) and weight of protein (P) in 100 grams, all divided by 100.

PRO=(D/100*R*P)/100

We demonstrate the utilization of this methodology through its application to LCA data for global warming potential (GWP) in grams CO2 equivalents per serving on more than two dozen commonly consumed foods, including plant-based and animal-based food products. Through utilization of this comparison methodology, we were able to identify foods with the best balance of high PRO and low GWP as well as foods with the worst of both. We express PRO and GWP both separately and as a ratio to provide more complete information as well as a ranking of the efficiency across compared foods. Selected top performing foods include whey protein (44.9 PRO/4.5 GWP = 10 ratio), pea protein (15.1 PRO/2.5 GWP = 6 ratio), and soy protein (13.9 PRO/4.8 GWP = 2.9 ratio). In contrast, poor performers include beef (25.8 PRO/188.7 GWP = 0.1 ratio) and lentils (3.4 PRO/21.4 GWP = 0.2 ratio). Integrating protein quality as well as content in a metric that also accounts for serving size enables a more sophisticated understanding of the tradeoffs involved when selecting foods for their environmental performance, creating a more complete picture for smarter food choices.