Comparative life cycle assessment of heterotrophic microalgae Schizochytrium and fish oil in sustainable aquaculture feeds

Aquaculture will play a key role in meeting the challenge of feeding more than 9 billion people by 2050 (Godfray et al., 2010). It is the world’s most efficient animal-protein generator when considering feed conversion ratios (Sarker et al., 2013; Fry et al., 2018; Hua et al., 2019). Among global food production systems, aquaculture is the fastest growing sector and has recently outpaced wild seafood and beef production (Froehlich et al., 2018). Aquaculture’s explosive global growth involved a major shift from unfed to fed production using formulated aquafeeds, with fed aquaculture growing 158% from 2000 to 2018 when it comprised nearly 60 million tonnes (FAO, 2020). Consequently, approximately 51.3 million tonnes of aquafeeds were produced in 2017 and are expected to increase to 73.15 million tonnes by 2025 (Tacon et al., 2021). During this shift, aquafeeds relied on fishmeal and fish oil, derived from marine forage fisheries (e.g., on anchovy, sardine, herring) for protein, lipid, and energy sources. Approximately 16 million of the 29 million tonnes of the forage fish annual global catch currently go into aquaculture feed (Cottrell et al., 2020). Analysts project that, at current rates of fishmeal and fish oil consumption, aquafeed demands could outstrip the supply of forage fish by 2037 (Duarte et al., 2009; Pikitch et al., 2014; Cashion et al., 2017; Froehlich et al., 2018). Fed aquaculture, therefore, must continue to reduce dependence on fishmeal and fish oil in feeds.

Toward this end, Cottrell et al. (2020) found that removing fish oil yields the greatest reductions in global forage fish demand because considerably more forage fish is needed to yield a tonne of fish oil than a tonne of fishmeal. Despite more resource use in terms of forage fish to produce a unit mass of fish oil, fishmeal typically has a significantly higher inclusion percentage in aquaculture feed. The aquaculture sector consumes 68.2% of total global fish meal production and 88.5% of total global fish oil production (Tacon and Metian, 2008). Thus, alternatives to both fishmeal and fish oil in feed formulations are critically important to the sustainable growth of aquaculture.

Some analysts consider fishmeal and fish oil substitution by terrestrial plant ingredients to be environmentally sustainable, as it reduces dependency on finite marine resources (Gatlin et al., 2007; Naylor et al., 2009). Researchers have used life cycle assessment (LCA) as a tool to assess the sustainability of plant-based alternatives to fishmeal and fish oil (Samuel-Fitwi et al., 2013; Silva et al., 2018; Pelletier et al., 2018; Basto-Silva et al., 2019; Ghamkhar and Hicks, 2020). Samuel-Fitwi et al. (2013) compared the LCA of different fishmeal and terrestrial plant-based protein sources (i.e., soybean meal and rapeseed meal) for aquafeed formulations. Basto-Silva et al. (2019) studied the environmental impacts of partial substitution of fishmeal with plant-based alternatives (i.e., soybean meal, canola meal, corn gluten meal, and wheat gluten meal). Pelletier et al. (2018) and Silva et al. (2018) addressed the environmental performance of soybean products in addition to alternatives such as livestock and fishery by-products. The comparative LCA by Ghamkhar and Hicks (2020) evaluated formulated aquafeeds containing various ingredients as fishmeal replacements (including plant-based sources such as peanut meal and soybean meal) and fish oil replacement (including canola oil) that were practically formulated and tested instead of relying on hypothetical feed formulations. Although progress has been made in identifying plant-based substitutes for marine resources, recent studies have found that shifting from fishmeal and fish oil to plant-based substitutes shifts the socioeconomic and environmental drawbacks from ocean to land as this increases aquafeed’s demand for resources (i.e., freshwater, land, and phosphorus; Pahlow et al., 2015; Malcorps et al., 2019).

Plant-based substitutes for fish oil have human health implications in addition to environmental concerns. They lack 2 forms of omega-3 fatty acids (a type of dietary polyunsaturated fat), which diminishes the human health benefits of eating farmed fish. Marine-derived foods are rich in 2 forms of omega-3 fatty acids, docosahexaenoic acid (DHA) and eicosapentaenoic acid (EPA), which may protect against cardiovascular disease (Calder, 2018; Tacon and Metian, 2018; Spiller et al., 2019). Plant food sources (flax, walnuts, canola oil) are rich in another form of omega-3 fatty acids, alpha-linolenic acid (ALA). But foods rich in EPA and DHA, key fatty acids for human metabolism, are preferable because the human body has a low efficiency of converting ALA into EPA and DHA (Gester, 1998; Elina et al., 2018; Tocher et al., 2019).

Marine microalgae, unlike terrestrial plant-based oils, have high amounts of omega-3 fatty acids which makes them ideal candidates for fish oil replacement for salmonids and other species (Sarker et al., 2016; Sarker et al., 2020a; Sarker et al., 2020b; Gamble et al., 2021; Bélanger et al., 2021). A recent study successfully replaced fish oil with whole-cell Schizochytrium blended with vegetable oils in the diets of rainbow trout without negatively impacting the fatty-acid profile of fish flesh (Bélanger-Lamonde et al., 2018). Furthermore, several leading aquafeed companies recently started including DHA-rich oil from Schizochytrium into salmon feeds (Tocher et al., 2019). Microalgae are among the ingredients that innovators, producers, investors, and other stakeholders along the aquaculture supply chain are pursuing as part of the fish-free-feed (F3) challenge (Nemo, 2019; Wright, 2019).

LCAs to investigate the environmental impact of replacing fishmeal and fish oil have focused mostly on phototrophic microalgae (Batan et al., 2010; Sills et al., 2013; Beal et al., 2015; Smetana et al., 2017; Barr and Landis, 2018; Beal et al., 2018), with a smaller number investigating heterotrophic microalgae (e.g., Crypthecodinium cohnii, Chlorella vulgaris; Smetana et al., 2017; Deprá et al., 2020; Bartek et al., 2021). Ghamkhar and Hicks (2020) included oil from the heterotrophic genus Thraustochytrid in their analysis but did not specify the species. Only 2 studies have considered the heterotrophic marine microalga Schizochytrium sp. that is currently being cultivated in commercial facilities (Davis et al., 2021; Togarcheti and Padamati, 2021).

Published LCA studies of heterotrophic microalgae have several limitations. In some studies, the cultivation inputs are not representative of industry practices. For example, the LCA of C. cohnii (Bartek et al., 2021) modeled use of food waste as a carbon source. However, according to industry advisors, food waste as a carbon source for heterotrophic cultivation of Schizochytrium is not commercially viable due to logistics. In other instances, the life cycle inventory is not comprehensive or available in an open-access format. For example, Togarcheti and Padamati (2021) conducted an LCA of omega-3 fatty acids from Schizochytrium but did not include a detailed inventory of the oil extraction processes. Davis et al. (2021) conducted a comprehensive LCA of omega-3 DHA products using industry data, but the life cycle inventory was aggregated and in a file format for the commercial LCA software, Simapro—which limits the accessibility of the underlying data and the reproducibility of the results. Thus, a comprehensive and open-access LCA of Schizochytrium products as substitutes for fish oil in aquafeeds is lacking.

Previous studies investigating the potential of replacing fish oil with marine microalgae only considered oil extraction with solvents (Barr and Landis, 2018; Beal et al., 2018). Thus, an LCA that accounts for trade-offs among alternative production methods, that occur as the use of novel replacements for fish oil scales up, is lacking. The solvent-free microwave extraction method is a desirable alternative because of its lower toxicity and faster processing time when compared with conventional solvents (Park et al., 2017).

Except for the aquafeed LCA by Ghamkhar and Hicks (2020)—which included 12 different environmental impacts—most recent aquafeed LCA studies examined a limited set of impact categories (global warming, eutrophication, acidification, and energy demand), and neglected crucial indicators for environmental sustainability of aquafeeds, such as biotic resource depletion, land use, and water consumption (Henriksson et al., 2012; Avadí et al., 2018; Bohnes and Laurent, 2019). Availability of terrestrial biotic resources has been a topic of concern in relation to deforestation to expand crop agriculture (Eisenmenger et al., 2016). For example, rainforests are one of the biggest hotspots for biodiversity and billions of people depend on their ecosystem services (e.g., food and shelter; Bach et al., 2017). Marine biotic resource use is a measure of the impacts of capture fisheries on marine ecosystems (e.g., on the diversity, structure and functioning of benthic communities; Cashion et al., 2016; Luong et al., 2020). A main concern is the direct extraction of marine biotic resources through fishing but also the indirect extraction of the net primary production synthesized by algae or seaweeds that is needed to sustain the harvested fish. Understanding land use is also vitally important because increased aquaculture production adds pressure on feed-crop requirements, which can result in changes in area and location of land use for crops and grazing (Froehlich et al., 2018). Furthermore, increasing aquaculture production can significantly increase the pressure on freshwater resources, due to water consumption and pollution in crop production for aquafeed (Pahlow et al., 2015). To avoid unintended consequences via burden shifting, LCA of aquafeed ingredients should utilize a comprehensive set of relevant impact metrics for aquafeeds, considering natural resource depletion (e.g., biotic resource use, land use, and water use) and pollutant emissions (e.g., eutrophication potential and global warming potential).

The present study addresses these limitations by using LCA to quantify 6 environmental consequences (global warming potential, water consumption, land use, marine eutrophication potential, freshwater eutrophication potential, and biotic resource use) of substituting fish oil in aquafeeds with blends of marine microalgae and vegetable oil. We considered production approaches currently used in the industrial cultivation of Schizochytrium at 2 different geographic locations, 2 different carbon sources for the cultivation cycle, and 2 different oil extraction methods: (1) Schizochytrium cultivated with sucrose sourced from a Brazilian sugarcane biorefinery as the carbon source; (2) Schizochytrium cultivated from sucrose sourced from sugar beets in the United States; (3) Schizochytrium oil extracted with solvent-free microwave extraction; and (4) Schizochytrium oil extracted with conventional solvent methods. We sought to test whether solvent-free microwave extraction methods provide environmental benefits over conventional solvent extraction methods for extracting the oil from Schizochytrium. We sought to test whether whole-cell Schizochytrium provides environmental benefits, compared to extracted Schizochytrium oil, when used as an omega-3 supplement to vegetable oil to replace fish oil. Finally, we sought to test whether Schizochytrium products combined with canola oil provides environmental benefits compared to the benchmark, fish oil (Figure 1).

The LCA study consisted of 3 main parts: (1) inventory modeling analysis of Schizochytrium production and processing (whole-cells and extracted oil) with available data; (2) attributional life cycle impact modelling and hotspots identification compared to the benchmark product; and (3) sensitivity analysis and identification of more sustainable scenarios of Schizochytrium production. The assessment followed the standard LCA approach (ISO 14040, 2006) and used professional SimaPro v.8.5.2.0 software (PRé Consultants B.V., Amsterfort, The Netherlands) and adapted Ecoinvent 3.4 datasets for background data (e.g., electricity, water supply, heat generation, and crop ingredients; Wernet et al., 2016). We used the ReCiPe 2016 Midpoint (H) v.1.02 method (Huijbregts et al., 2017) to calculate the global warming potential, water consumption, land use, freshwater eutrophication potential, and marine eutrophication potential categories. For biotic resource use estimates, we made calculations from values provided in the literature (Papatryphon et al., 2004; Pelletier and Tyedmers, 2007; Runge et al., 2012; Cashion et al., 2016; Sánchez-Sastre et al., 2018; Zhang, 2018; and see Text S1.1.1 for detailed calculations).

The goal of this LCA is to provide guidance on how the aquafeed industry could further utilize marine microalgae biorefineries to decrease environmental impact of aquafeeds without sacrificing the nutritional quality of farmed fish fillets.

We applied an attributional analysis (Ekvall, 2019) of the environmental impact of production stages of blends of Schizochytrium and canola oil and a comparative analysis to the benchmark, traditional fish oil. We conducted a contribution analysis of the input parameters to identify environmental impact hotspots. A hotspot is a life cycle stage, process or elementary flow which accounts for a significant proportion of the impact (Laurent et al., 2020). Additionally, we identified environmental impact reductions achievable by considering alternative production parameters in our sensitivity analysis.

We selected the functional unit based on the inclusion of essential nutrients: iso-lipidic and iso-DHA replacements for 1-kg fish oil which has a DHA content of 130-g (Sarker et al., 2016; Sarker et al., 2020b). We calculated the replacement blends as 556-g whole-cell Schizochytrium or 301-g Schizochytrium oil combined with 699-g canola oil (see Text S1.2 for detailed calculations), to replace both the DHA and lipids typically provided by fish oil in aquafeeds. However, we separately calculated the impacts of 1-kg whole-cell Schizochytrium and 1-kg Schizochytrium oil.

The system boundaries encompassed all direct material and energy inputs related to the agricultural production and processing systems from which the ingredients were derived—“cradle to factory-gate” (Figure 1). The attributional LCA required the allocation of environmental impact between coproducts of cultivation inputs and oil extraction processes for Schizochytrium, coproduct of oil extraction process for canola seed, and coproduct of both fish processing by-products (e.g., tuna and sardine) and small pelagic fish to reduction for fish oil (e.g., anchovy, herring, and menhaden).

Multiple products are produced within the system boundaries of our analysis, which required a decision about which allocation method (e.g., economic, or biophysical) to use in the attribution of environmental impact to the coproducts. Economic allocation is the most commonly used allocation method in agricultural LCA studies, particularly for crop production and the livestock feed supply chain (Ardente and Cellura, 2012; Brankatschk and Finkbeiner, 2014; Van Der Werf and Nguyen, 2015; Mackenzie et al., 2017). Economic allocation places higher importance on the more limiting coproducts generated and their relative demand, thus acts as a proxy for the nutritional value of ingredients (Kok et al., 2020). Biophysical allocation uses physical relationships between coproducts. However, biophysical allocation systems rely on economic value in that economic value informs whether to include or exclude whole sections of the mass balance in a model of an agricultural system (Mackenzie et al., 2017). For these reasons, we selected economic allocation for our analysis.

We modeled whole-cell Schizochytrium production based on an existing facility in the State of Sâo Paulo in Brazil that is colocated with a sugar cane biorefinery. The system boundaries of whole-cell Schizochytrium include the agricultural production of sugarcane; the processing of sugarcane into sucrose at the sugar mill which includes the coproduct bagasse; electricity from bagasse provides all the energy to power both the sugar mill and the Schizochytrium production facility, and excess electricity is fed back into the grid; sucrose is used to cultivate Schizochytrium but some is diverted to the production ethanol which is a coproduct; the Schizochytrium is harvested by dewatering processes to produce whole cells. We assumed whole-cell Schizochytrium would be combined with canola oil from the global market to replace both DHA and lipids (Figure 1a). We used an economic allocation of 32% and with biorefinery coproducts of electricity from sugarcane bagasse and sugarcane ethanol (see Text S1.1.2 for additional details). The coproduct of canola oil is canola meal with an economic allocation of 74% to canola oil (Ecoinvent v.3.4).

We modeled Schizochytrium oil production based on a zero-waste facility in the State of Nebraska in the United States. Although the production facility can use a variety of carbon sources (dextrose, sucrose), we were advised they are currently using sucrose from sugar beets (Figure 1b). We assume the facility is powered with grid electricity, however, the anaerobically digested and lipid extracted residuals provide enough biogas to provide heat for the facility. The system boundaries of Schizochytrium oil include the agricultural production of sugar beets; processing of sugar beets into sucrose with coproducts molasses and beet pulp as feed; sucrose is used to cultivate Schizochytrium; the Schizochytrium is harvested by dewatering processes; the oil is extracted from the whole-cell Schizochytrium and the lipid extracted residual (biowaste) is anaerobically digested; the coproduct of the anaerobically digested biowaste is biogas which is burned in a boiler to provide heat for Schizochytrium production processes; the crude Schizochytrium oil is refined. We assumed Schizochytrium oil would be combined with canola oil from the global market to replace both DHA and lipids (Figure 1b). We used an economic allocation of 91.7% to sucrose from sugar beets, and with coproducts of molasses with an economic allocation of 4.5% to the coproduct molasses, and an economic allocation of 3.8% beet pulp as feed (see Text S1.1.2 for additional details).

As the benchmark for comparison, we modeled fish oil produced from small pelagic fish and fish processing byproducts from the global market. The system boundaries of fish oil from fish byproducts include fishing and processing with fishing byproducts and processed fish as coproducts; the byproducts are processed with crude fish oil and fishmeal as the coproducts; the crude fish oil is refined. The system boundaries of fish oil from small pelagic fish include fishing; processing with crude fish oil and fishmeal as the coproduct; the crude fish oil is refined (Figure 1c). Following Kok et al. (2020), we assumed that 33% of fish oil is produced from fish processing by-products and the remainder from small pelagic fish destined for reduction. The coproduct of fish oil from both fish processing by-products and small pelagic fish for reduction is fishmeal, with an economic allocation of 23% to fish oil (see Text S1.1.2 for additional details). The main allocation factors are based on the coproduct yields and prices (Equation S1).

We sourced Schizochytrium inventory data from patents and refereed literature. We sought feedback from representatives of 2 different companies that produce Schizochytrium on whether our assumptions were commercially relevant and whether our estimates were in general agreement with their estimates. We retained the freedom to decide specifics of our models and estimates. Furthermore, we did not receive funding from the companies, and they shared their advice without remuneration.

We reviewed the literature and patent data to develop the cultivation inventory and to estimate the biomass yield, cultivation period, cultivation volume, and composition of the cultivation medium for cultivating Schizochytrium (Tables S1–S2; Bailey et al., 2008; Orfield et al., 2015; Comini and Pora, 2018). Although the production of Schizochytrium is a multistep process which results in successively larger volumes of medium, we did not have sufficient process information to model the yields and medium composition of the smaller batches. We assumed that the bulk of the energy and material is consumed in the largest process volume. Thus, we only considered the largest cultivation volume, which is an approach other studies have used (e.g., Dunn et al., 2012). We used literature values to develop the inventory for sterilization, thermal control, mixing, water pumping, sparging, and agitating the medium with an impeller (Table S3; Lee Chang et al., 2015).

We reviewed the literature to develop the inventory for harvesting with a decanter-bowl centrifuge followed by rotary dryer (Table S4; Wiley et al., 2011; Frank et al., 2011; Davis et al., 2012; Beal et al., 2015; Orfield et al., 2015; Delele et al., 2015; Barr and Landis, 2018; Fasaei et al., 2018). We considered wet (20% wt. dry matter) biomass process trains for Schizochytrium produced in the United States. The wet biomass process train does not include rotary drying. Because the cultivation medium has high levels of salinity, we assumed tap water would be added to dilute the salinity of the effluent from the centrifuge. We calculated a dilution factor of 13.2 (see Text S1.3 for details of calculation).

An industry advisor informed us in personal communication that the U.S. facility uses a patented, solvent-free physical extraction method that has low energy consumption. Thus, we modeled solvent-free microwave extraction using literature values because this method is among the solvent-free physical processes used to extract microalgae with high efficiency (Segneanu et al., 2013; Tang et al., 2020; Table S5). For comparison, we modeled conventional hexane solvent extraction using literature values to estimate the material and energy inputs (Table S6).

For solvent-free microwave extraction, we assumed the power consumption and loading rates of an industrial microwave (Vernés et al., 2020) and efficiencies reported in the literature (Passos et al., 2015; Park et al., 2017). We assumed the oil extraction process would be followed by decanter-bowl centrifuge processing to separate the solid and liquid fractions (Table S4), and disc-stack centrifuge processing to separate the oil and water. For the oil and water separations, we used the electricity consumption and efficiencies of a disc-stack centrifuge (Szepessy and Thorwid, 2018; Fasaei et al., 2018). We modeled anaerobic digestion of the lipid extracted residuals which supplies enough biogas to generate heat for sterilization and thermal control of the fermenter, and oil refining activities.

For solvent extraction, we used literature values to model high-pressure homogenization followed by oil extraction with hexane solvent. The inventory for high-pressure homogenization includes electricity consumption (Frank et al., 2011; Davis et al., 2012; Tu et al., 2017). The inventory for hexane extraction of lipids from biomass includes hexane losses, electricity, heat for solvent recovery, and process efficiency (Frank et al., 2011; Vasudevan et al., 2012; Passell et al., 2013; Sills et al., 2013; Azadi et al., 2014; Souza et al., 2015; Beal et al., 2015; Tu et al., 2017; Barr and Landis, 2018; Beal et al., 2018). We modeled anaerobic digestion of the lipid extracted residuals which supplies a portion (approximately 56%) of biogas to generate heat for operations (e.g., sterilization and thermal control of the fermenter, recovery of solvent, and oil refining activities) but the remainder is supplied by natural gas. Unlike solvent-free microwave extraction methods which do not require heat for processing, solvent extraction methods do require heat for processing which explains why there is not sufficient biogas to provide heat in this scenario.

We used literature values to model the process of refining crude Schizochytrium oil (Table S7). The inputs for degumming (i.e., phosphoric acid), neutralization (i.e., sodium hydroxide), and bleaching (i.e., bleaching earth and activated carbon) were based on inputs for refining heterotrophic microalgal oil (Togarcheti and Padamati, 2021). We calculated the electricity demand based on the loading volume (i.e., water, oil, and degumming and neutralization inputs) and the specific energy for centrifuge used to refine algal oil (Barr and Landis, 2018). Inputs for processing steam, water, and wastewater were adapted from the Agri-footprint and the Ecoinvent databases. Degummed oil yields, neutralized oil yield, and bleached oil yields were based on literature values for refined algal oil (Barr and Landis, 2018).

We used the Ecoinvent and Agri-footprint databases for the inventory of inputs and process efficiencies for refined canola oil (Table S8).

We used literature values to model the fishing activities and processing to reduce small pelagic fish and fish byproducts into fish oil (Tables S9–S12).

For fish oil production from reduction of small pelagic fish, the fishing inputs included diesel fuel (Driscoll and Tyedmers, 2010; Fréon et al., 2014; Parker and Tyedmers, 2015; Silva et al., 2018), engine oil (Vazquez-Rowe et al., 2010; Almeida et al., 2014; Silva et al., 2018), concrete for the ballasts (Fréon et al., 2014), propulsion components (including batteries, copper wire, and cast iron for the engine; Fréon et al., 2014), anti-fouling (Vazquez-Rowe et al., 2010; Silva et al., 2018), paint (Vazquez-Rowe et al., 2010; Fréon et al., 2014; Silva et al., 2018), material for fishing gear (e.g., nylon, lead, and low linear density polyethylene; Fréon et al., 2014; Silva et al., 2018), steel for the hull (Vazquez-Rowe et al., 2010; Fréon et al., 2014), and ice (Vazquez-Rowe et al., 2010; Almeida et al., 2014). For processing of small pelagic reduction for fish oil, inputs included electricity (Samuel-Fitwi et al., 2013; Silva et al., 2018), fuel oil (Samuel-Fitwi et al., 2013; Fréon et al., 2017; Silva et al., 2018), sodium hydroxide (Samuel-Fitwi et al., 2013; Fréon et al., 2017; Silva et al., 2018), sodium chloride for processing (Fréon et al., 2017), and freshwater for processing (Samuel-Fitwi et al., 2013).

For fish oil from byproducts (from tuna and sardine canning), we used the inventory of Silva et al. (2018) to estimate the fishing inputs including diesel fuel, engine oil, anti-fouling, paint, material for fishing gear (e.g., nylon, paint, and low linear density polyethylene), and ice consumption. We used the inventory of Fréon et al. (2014) for the concrete for ballasts, and propulsion components including batteries, copper wire, and cast iron for the engine. We used the inventories of Vazquez-Rowe et al. (2010) and Fréon et al. (2014) for steel for the hull. For processing of byproducts for fish oil, we included electricity, fuel oil and biomass wood pellets for heat, and freshwater for processing (Silva et al., 2018). We also included sodium hydroxide (Samuel-Fitwi et al., 2013; Fréon et al., 2017; Silva et al., 2018) and sodium chloride for processing (Fréon et al., 2017).

For fish oil produced from fish processing byproducts and from small pelagic reduction, we used guidelines for commercial food service equipment to estimate the quantity of water and electricity required for ice production (Energy Star, 2018). We did not include the capital goods for fish oil processing in our inventories because: (1) we did not have sufficient data and (2) when using vegetable oil as a proxy for fish oil, the overall impact of capital goods is relatively small (see Supplementary Text S1.4 for additional details).

We used literature values to model the process of refining crude fish oil (Table S13). The inputs for degumming (i.e., phosphoric acid), neutralization (i.e., sodium hydroxide), bleaching (i.e., bleaching earth and activated carbon), and electricity demand were based on inputs for refining fish oil (Togarcheti and Padamati, 2021). Inputs for processing steam, water, and wastewater were adapted from the Agri-footprint and the Ecoinvent databases. Degummed oil yields, neutralized oil yield, and bleached oil yields were based on literature values for refined fish oil (Barr and Landis, 2018).

We considered 4 different hypotheses related to the environmental impact of Schizochytrium production. First, we tested the hypothesis that producing whole-cell Schizochytrium would have lower environmental impacts than producing Schizochytrium oil when used as an omega-3 supplement with vegetable oils to replace fish oil. Second, we tested the hypothesis that solvent-free microwave extraction methods would have lower environmental impacts than solvent extraction methods. Third, we tested the hypothesis that whole-cell Schizochytrium blended with canola oil would have lower environmental impacts than fish oil. Fourth, we tested the hypothesis that Schizochytrium oil blended with canola oil would have lower environmental impacts than fish oil. We used an independent-sample, single-tailed, unequal variance Student t test to test a one-sided hypothesis using Microsoft Excel. Significance was based on P values <.05.

We used established methods to calculate the uncertainty of several inventory parameters (McMurray et al., 2017). We fitted the distributions of selected inventory items with EasyFit Professional software (v. 5.6; Tables S14–S22). Using the best fit distribution, we ran Monte Carlo simulations of 10,000 samples. We ran a percentile bootstrap analysis of 1,000 replicates of the sample medians to estimate the 95% confidence intervals using the boot package in R (Canty and Ripley, 2020). We propagated the error for the selected inventory items using the derivative method (Bevington and Robinson, 2003).

We conducted a sensitivity analysis of Schizochytrium production to identify the key parameters of attributional modeling that affect the outcome of the impact assessment and the influence of different methodological choices.

To identify the model parameters that have the largest impact on results, and which parameters contribute most to the output variability, we conducted a sensitivity analysis using a one-parameter-at-a-time approach (Laurent et al., 2020). Our sensitivity analysis considered the perturbation effect of the standard deviations of the input parameters of whole-cell Schizochytrium (mean plus or minus the standard deviations given in Tables S1–S4) and Schizochytrium oil production using solvent-free microwave extraction methods (mean plus or minus the standard deviations given in Tables S1–S5 and S7).

Following other studies that considered multiple methods (e.g., Vandepaer et al., 2019; Smetana et al., 2019), we considered the Ecoinvent consequential model as an alternative to the allocation at point of substitution model for the environmental impact characterization factors of Schizochytrium products blended with canola oil. In consequential LCAs, activities in a product system are linked. The activities included in the product system are expected to change as a consequence of a change in demand for the functional unit; multioutput processes and recycling are handled using the substitution method and the system was expanded to include all affected processes to represent the full cause–effect chain, which results in the inclusion of indirect changes in addition to direct and physically connected consequences (for more details on the Ecoinvent v.3 consequential model, see Wernet et al., 2016).

We considered alternatives to several input parameters (e.g., sucrose, electricity, and canola oil) to identify whether or not they offer more sustainable scenarios using existing technologies. Several renewable waste raw materials are being considered as alternatives to sucrose for cultivation of heterotrophic organisms (e.g., whey, crude glycerol, lignocellulosic biomass; Ende and Noke, 2019; Oliver et al., 2020; Xu et al., 2020). We considered crude glycerol because this byproduct has been identified as a low cost and renewable alternative to more expensive carbon sources (da Silva Ruy et al., 2020; Xu et al., 2020) and a recent study has demonstrated promising results (Kujawska et al., 2021). We considered wind energy as an alternative electricity source because wind makes up the majority of the renewable energy sources in Brazil (Energy Information Administration [EIA], 2021a) and in Nebraska (EIA, 2021b). We selected canola oil as the vegetable oil to use in combination of Schizochytrium products as a substitute for fish oil because its lipid and fatty acids profile aligns well with nutritional needs of some farmed fish such as rainbow trout. We also considered other vegetable oils (a generic vegetable oil blend, soybean oil, palm oil, cottonseed oil, and maize oil) to understand the environmental impacts of alternatives.

We summarized the inputs and outputs of Schizochytrium production including cultivation (Table S23), harvesting (Tables S24 and S25), oil extraction (Tables S26 and S27), and oil refining model (Tables S28 and S29). Furthermore, we summarized the inputs and outputs of refined canola oil (Table S30). Finally, we summarized the inputs and outputs of small pelagic fishing activities (Table S31) and fish byproducts (Table S32), processing of small pelagic fish (Table S33) and fish byproducts (Table S34), and refining fish oil (Table S35).

We summarized the characterization factors used in the attributional analysis (Tables S36–S39), the consequential analysis (Tables S40–S42), and in the alternate parameters used in the sensitivity analysis (Table S43).

Here, we separately identified the environmental hot-spots of whole-cell Schizochytrium production. We also compared the life cycle impact results of whole-cell Schizochytrium production across locations (Figure 2). The resulting median and [95% confidence interval] of the environmental impacts of 1-kg whole-cell Schizochytrium produced in Brazil and in the United States include:

• global warming potential of 1.85 [1.82, 1.88] and 2.33 [2.29, 2.36] kg carbon dioxide equivalents (CO₂e)

• water consumption of 3.77 [3.63, 3.93] × 10^–1 and 3.68 [3.54, 3.84] × 10^–1 m³ water

• land use of 1.69 [1.67, 1.71] and 1.50 [1.48, 1.52] m² land

• marine eutrophication potential of 9.45 [9.31, 9.59] × 10^–4 and 3.09 [3.05, 3.13] × 10^–3 kg nitrogen

• freshwater eutrophication potential of 3.79 [3.71, 3.87] × 10^–4 and 7.39 [7.27, 7.52] × 10^–4 kg phosphorous

• biotic resource use of 1.92 [1.90, 1.94] and 4.39 [4.34, 4.44] kg carbon.

Except for water consumption, most of the environmental impact of whole-cell Schizochytrium was attributed to cultivation processes (e.g., all the biotic resource use, nearly all the land use, over 90% of marine eutrophication potential, roughly 90% of the global warming potential, and over 80% of freshwater eutrophication potential) in both locations (Figure 2a). Roughly 60% of the water consumption was attributed to harvesting processes in both locations. The contribution of harvesting to the environmental impact is larger for whole-cell Schizochytrium produced in Brazil than in the United States because the process train in Brazil includes drying biomass (95% dwt.) with a rotary dryer whereas the process train in the United States uses wet biomass (20% dwt.).

The carbon source (sucrose from sugar cane in Brazil and sucrose from sugar beets in the United States) had the largest impact across locations for several environmental indicators including global warming potential, land use, and marine eutrophication potential (Figure 2b). Sucrose from sugar beets also had the largest impact on freshwater eutrophication for Schizochytrium production in the United States. Monopotassium phosphate had the largest impact on freshwater eutrophication potential for Schizochytrium production in Brazil. Water supply had the largest impact on water consumption across locations. The large water consumption value exists (largely due to the supply water for the cultivation medium and the postharvesting addition of water to the effluents of the centrifuge to dilute salts) even though some of the water supplied in cultivation and harvesting activities is recycled in the wastewater treatment process.

Although whole-cell Schizochytrium produced in the United States avoids the rotary heater drying of biomass, we found the global warming potential and eutrophication potential was higher than for biomass produced in Brazil due to the U.S. carbon and the electricity sources (Figure 2a). The impact of sucrose from sugar beets (in United States) is higher than sucrose from sugar cane (in Brazil) by a factor of 1.5, 4.1, and 4.9 for the global warming potential, marine eutrophication potential, and freshwater eutrophication potentials, respectively (Tables S36 and S37). The lower impact of sucrose from sugarcane can be attributed to the higher economic allocation to ethanol production and electricity from sugarcane bagasse than the coproducts of sugar beet sucrose (e.g., beet pulp and molasses). The impact characterization factors for the Nebraska electricity-mix, used in the U.S. production scenario, was higher than the sugar-cane bagasse electricity by a factor of 2.2 and 1.6 for the global warming potential, and freshwater eutrophication potentials, respectively (Tables S36 and S37). The higher environmental impact of electricity used in U.S. production is due to the greater use of fossil fuels (e.g., coal and natural gas).

We found whole-cell Schizochytrium produced in Brazil, compared to that produced in the United States had higher land use and water consumption values. The higher land use can be attributed to the lower yield of sugar from a sugarcane biorefinery (e.g., 7.1 kg sugarcane per kg sugar and 0.27 m² land/kg sugarcane) compared with the yield of sugar from sugar beets (e.g., 5.1 kg sugar beets per kg sugar and 0.13 m² land/kg sugar beets; Ecoinvent v. 3.4). The higher water consumption for biomass produced in Brazil is due to the larger quantity of water for processing sugar in a sugarcane biorefinery (7.6 kg water per kg sugar) compared with water for processing sugar beets (5.5 kg water per kg sugar; Ecoinvent v. 3.4).

Here, we separately identified the environmental hot spots of Schizochytrium oil production in the U.S. We also compared the life cycle impact results of oil production using 2 different technologies for extracting oil—solvent-free microwave extraction and conventional methods using a hexane solvent (Figure 3, Table S44). The resulting median and [95% confidence interval] of the environmental impacts of 1-kg Schizochytrium oil produced in the United States using solvent-free microwave extraction and solvent extraction methods include:

• global warming potential of 9.09 [8.93, 9.26] and 8.88 [8.71, 9.05] kg CO₂e

• water consumption of 1.27 [1.22, 1.33] and 1.13 [1.09, 1.18] m³ water

• land use of 5.17 [5.12, 5.23] and 4.62 [4.57, 4.66] m² land

• marine eutrophication potential of 1.07 [1.05, 1.09] × 10^–2 and 9.54 [9.40, 9.68] × 10^–3 kg nitrogen

• freshwater eutrophication potential of 3.64 [3.57, 3.71] × 10^–3 and 3.40 [3.33, 3.47] × 10^–3 kg phosphorous

• biotic resource use of 15.1 [14.9, 15.3] and 13.4 [13.2, 13.6] kg carbon.

Across environmental indicators, conventional solvent extraction methods had a significantly lower impact than solvent-free microwave extraction methods except not global warming potential (P values 9.78 × 10^–2 for global warming potential; 1.47 × 10^–2 for water use; 2.84 × 10^–4 for land use; 3.52 × 10^–4 for marine eutrophication potential; 6.56 × 10^–3 for freshwater eutrophication potential; and 3.31 × 10^–5 for biotic resource use; Table S44).

Similar to the whole-cell Schizochytrium results, the majority of the environmental impact of Schizochytrium oil was attributed to cultivation processes (e.g., 81% and 74% of the global warming potential, nearly all of the land use, 97% of marine eutrophication potential, and 62% and 58% of freshwater eutrophication potential for solvent-free microwave extraction and solvent extraction methods, respectively) except not water use (Figure 3a). Nearly 60% of the water consumption was attributed to harvesting processes for both oil extraction methods. Oil extraction activities also had a large freshwater eutrophication potential impact. The higher environmental impacts of solvent-free microwave extraction technology can be attributed to the lower oil recovery yield (approximately 80%) compared to the oil recovery yield of solvent extraction methods (approximately 93%).

The results of our contribution analysis by substance for Schizochytrium oil indicate that, like the whole-cell Schizochytrium results, sucrose had the largest impact across environmental indicators except not for water use (Figure 3b). Electricity consumption was also a key driver for the global warming potential and freshwater eutrophication potential. Water supply (e.g., water for the cultivation medium, the postharvesting addition of water to the effluents of the centrifuge to dilute salts, and water for oil refining processes) was the largest driver of water consumption but also had a large impact on the freshwater eutrophication potential. Nutrients such as urea (as nitrogen source) was also an important driver of global warming potential. Although water supply was a significant driver of water consumption, water recycling in the wastewater treatment process reduced overall water consumption. Solvent extraction methods had lower overall environmental impacts compared with solvent-free microwave extraction methods but had a higher demand for heat for solvent recovery.

Here, we compare whole-cell Schizochytrium blended with canola oil to Schizochytrium oil blended with canola oil and compare these 2 blends to the benchmark, fish oil from small pelagic fish and fish byproducts (Figure 4). The resulting median and [95% confidence interval] of the environmental impacts of whole-cell Schizochytrium (functional unit of 556-g whole-cell Schizochytrium combined with 699-g canola oil), Schizochytrium oil that was extracted with solvent-free microwave extraction methods (functional unit 301-g Schizochytrium oil combined with 699-g canola oil), and 1-kg fish oil (blend of fish oil from small pelagic fish and fish oil from fish processing byproducts), respectively, include:

• global warming potential of 2.55 [2.53, 2.57], 4.26 [4.21, 4.31], and 2.69 [2.64, 2.75] kg CO₂e

• water consumption of 0.2.80 [0.2.72, 0.2.89] × 10^–1, 4.54 [4.38, 4.71] × 10^–1, and 1.67 [1.65, 1.70] × 10^–2 m³ water

• land use of 5.45 [5.43, 5.46], 6.06 [6.04, 6.09], and 1.66 [1.64, 1.67] × 10^–1 m² land

• marine eutrophication potential of 8.62 [8.61, 8.63] × 10^–3, 1.13 [1.13, 1.14] × 10^–2, and 2.34 [2.32, 2.36] × 10^–3 kg nitrogen

• freshwater eutrophication potential of 6.76 [6.71, 6.81] × 10^–4, 1.56 [1.54, 1.45] × 10^–3, and 4.39 [4.32, 4.45] × 10^–4 kg phosphorous

• biotic resource use of 1.85 [1.84, 1.86], 5.33 [5.28, 5.38], and 46.2 kg carbon.

Across environmental indicators, a combination of whole-cell Schizochytrium and canola oil has significantly lower environmental impacts than Schizochytrium oil combined with canola oil (P values < 2.54 × 10^–4) owing to the avoided oil extraction and oil refining steps (Table S44).

Comparing whole-cell Schizochytrium combined with canola oil with the benchmark, fish oil, revealed that fish oil had significantly lower environmental impacts (P values < 1.74 × 10^–4) except not global warming potential or biotic resource use. Whole-cell Schizochytrium blended with canola oil had significantly lower global warming potential and biotic resource use (P values 2.40 × 10^–2 and 6.75 × 10^–9, respectively; Table S44). Fish oil had a global warming potential that was 1.05 times higher and a biotic resource use that was 29 times higher whole-cell Schizochytrium combined with canola oil.

Comparing Schizochytrium oil blended with canola oil with the benchmark, fish oil, revealed that fish oil had significantly lower impacts across environmental indicators (P values < 2.37 × 10^–4) except not for biotic resource use. Schizochytrium oil blended with canola oil had significantly lower biotic resource use (P value 2.31 × 10^–7; Table S44). The biotic resource use of fish oil is a factor 8.6 times higher than Schizochytrium oil combined with canola oil.

We considered the parameters that contributed most to the variance in the standard deviations of whole-cell Schizochytrium (Figure S1) and Schizochytrium oil production (Figure S2). For both whole-cell Schizochytrium and Schizochytrium oil production, the production cycle period, biomass yield, and sucrose parameters had the largest impact on the variance in the environmental impact results (see Text S2.1.1 for expanded results).

We found that when we used consequential instead of attributional methods, there were lower environmental impacts for whole-cell Schizochytrium production except not land use or biomass produced in Brazil (Figure S3 and Table S45). Similarly, we found that the environmental impacts for Schizochytrium oil production had lower environmental impacts (Figure S4 and Table S45). This trend was also observed with Schizochytrium oil blends (Figure S5 and Table S45). For example, the global warming potential of the Schizochytrium oil blends were 91% lower (mean value of 0.239 kg CO₂e kg oil blend^–1 for consequential) and 74% lower (mean value of 1.11 kg CO₂e kg oil blend^–1 for consequential) for whole-cell Schizochytrium blended with canola oil and Schizochytrium oil blended with canola oil, respectively. This substantial difference in values is largely due to the avoided production of the by-product, canola meal (see Text S2.1.2 for additional details).

We found mixed results when we considered alternative parameters to identify more sustainable scenarios (Tables S46–S48). When we modeled crude glycerol as an alternative to sugar beet sucrose and sugar cane sucrose, we found reduced impacts for some environmental indicators but a modest increase in land use across the scenarios we considered (e.g., whole-cell Schizochytrium, Schizochytrium oil, whole-cell Schizochytrium combined with canola oil, and Schizochytrium oil combined with canola oil; Table S46). For example, there was a 41% reduction in global warming potential, 25% reduction in water use, and a 73% reduction in marine eutrophication potential but an increase of 10% in land use for whole-cell Schizochytrium produced in Brazil. Similar trends were observed for whole-cell Schizochytrium produced in the United States, with a 53% reduction in global warming potential, 25% reduction in water use, and a 93% reduction in marine eutrophication potential but an increase of 19% in land use. For Schizochytrium oil, glycerol as alternative to sucrose resulted in reductions across environmental impacts except land use (Table S47). For Schizochytrium products (e.g., whole-cell and extracted oil) combined canola oil, similar trends were observed overall but with more modest reductions in impacts. For example, there was a 17% reduction in global warming potential, 19% reduction in water use, and a 4% reduction in marine eutrophication potential but an increase of 2% in land use for whole-cell Schizochytrium produced in Brazil combined with canola oil (Table S48). Schizochytrium oil produced in the United States using microwave extraction combined with canola oil had a 30% reduction in global warming potential, 21% reduction in water use, and a 26% reduction in marine eutrophication potential but an increase of 5% in land use (Table S48). Schizochytrium oil produced in the United States using solvent extraction combined with canola oil had a 27% reduction in global warming potential, 21% reduction in water use, and a 24% reduction in marine eutrophication potential but an increase of 4% in land use (Table S48).

When we modeled wind energy as an alternative to grid-electricity in the United States and an alternative to electricity from sugar cane bagasse, we found modest benefits across all environmental indicators (Tables S46–S48). When we considered alternative vegetable oils to blend with Schizochytrium products, we found environmental trade-offs (Table S48). For example, modeling cottonseed oil as an alternative to canola oil showed a modest decrease in the global warming potential (e.g., 4% for whole-cell combined with cottonseed oil and 3% for Schizochytrium oil combined with cottonseed oil) and a large decrease in the marine eutrophication potential (e.g., 81% for whole-cell combined with cottonseed oil and over 60% for Schizochytrium oil combined with cottonseed oil) but a substantial increase in water consumption (e.g., over 300% for whole-cell combined with cottonseed oil and roughly 200% for Schizochytrium oil combined with cottonseed oil). Palm oil also had trade-offs with modest increases in the global warming potential and water use but substantial decreases across the other indicators.

This study presents the first comprehensive and open-access LCA of biorefinery Schizochytrium products for replacing fish oil in aquaculture feeds that represents current commercial practices. We have reported environmental impacts of whole-cell Schizochytrium vs. extracted Schizochytrium oil in 2 regions and with 2 sugar sources of solvent extraction vs. solvent-free microwave extraction of Schizochytrium oil, and of Schizochytrium products vs. the fish oil benchmark. Under our model assumptions, we identified several environmental trade-offs of replacing fish oil with Schizochytrium products. Below we highlight environmental, fish performance and human health implications of these trade-offs, as well as study limitations and guidance for future aquafeed LCA studies.

Our results supported the first hypothesis that whole-cell Schizochytrium would have lower environmental impacts than Schizochytrium oil under our model assumptions. In addition to environmental benefits, feeding fish whole-cell Schizochytrium may offer nutritional benefits to the farmed fish. For example, recent studies have shown that microalgae in a paste form or in whole-cell form as an aquafeed ingredient provides antioxidant, carotenoid, astaxanthin, and bioactive compounds that can upregulate the cellular immune response in fish, reduce oxidative stress, and improve fish growth performance and feed conversion efficiency (Glencross and Rutherford, 2011; Raji et al., 2018; Ma et al., 2020; Sarker et al., 2020a).

Our results did not support the second hypothesis that solvent-free microwave extraction methods would have lower environmental impacts than conventional solvent methods for extracting Schizochytrium oil under our model assumptions. The modest increase in environmental impacts, however, should be considered against other benefits of solvent-free methods. For example, this method avoids safety and toxicity concerns posed by organic solvents (Park et al., 2017) and has faster processing time than conventional methods (Passos et al., 2015).

Our results concerning the third hypothesis that whole-cell Schizochytrium combined with canola oil would have lower environmental impacts than fish oil had mixed results. Comparing whole-cell Schizochytrium combined with canola oil to the benchmark, fish oil, revealed environmental trade-offs. On the one hand, biotic resource use of fish oil was one order of magnitude larger than whole-cell Schizochytrium combined with canola oil. Furthermore, the global warming potential of fish oil was modestly larger than whole-cell Schizochytrium combined with canola oil. On the other hand, the other environmental indicators (water consumption, land use, marine eutrophication potential, and freshwater eutrophication potential) had much lower impacts for fish oil.

Our results concerning the fourth hypothesis that Schizochytrium oil combined with canola oil would have lower environmental impacts than fish oil also had mixed results. The biotic resource use of fish oil was one order of magnitude larger than Schizochytrium oil combined with canola oil but fish oil had significantly smaller impacts across all other categories. Moving toward fish oil-free diets is a critically important strategy in meeting the growing demand for aquafeeds. These results, thus, highlight the need to reduce the resource-based (e.g., water use, land use) and emission-based (e.g., global warming potential, eutrophication potential) impacts of alternatives.

Manufacturers of aquafeeds and other parties interested in human health issues should consider the environmental trade-offs of Schizochytrium and canola oil for replacing fish oil alongside the human health risks. We raise this point because a growing percentage of fish oil is derived from byproducts of larger pelagic fish (e.g., tuna; Kok et al., 2020) that have higher concentrations of heavy metals (e.g., mercury) and persistent organic pollutants than small pelagic fish (Okpala et al., 2018; Panseri et al., 2019). Recent studies have shown that a blend of algae and vegetable oils as a replacement for fish oil reduced concentrations of contaminants of concern while preserving the omega-3 fatty acid levels in farmed rainbow trout (Bélanger-Lamonde et al., 2018).

Moreover, replacing fish oil with Schizochytrium products should maintain fish flesh fatty acid profiles that benefit human health. Our prior study showed that the lipid and fatty acids contents including DHA—a key omega-3 fatty acid for human health—in Schizochytrium sp. are highly digestible for rainbow trout, and we detected an equal amount of DHA in trout fillet when fish were fed fish-free diet (blend of Schizochytrium and canola oil) and fish oil-containing conventional diet (Sarker et al., 2020b; Bélanger et al., 2021).

The functional unit plays an important role in LCA and can affect comparative performance of modeled systems (Sills et al., 2020). Our selection of an iso-lipid and iso-DHA functional unit was based on the inclusion of essential nutrients. We recommend that future LCA studies adopt this approach and make an equivalent comparison of the functional nutrition of alternative ingredients with conventional aquafeed ingredients.

The choice of coproduct allocation method also plays an important role in LCA and can affect comparative performance of modeled systems (Sills et al., 2020). Although there is no consensus on attributional allocation methods (e.g., economic, or biophysical; Mackenzie et al., 2017), our choice of economic allocation influenced the final quantified results. A recent LCA of Schizochytrium for omega-3 fatty acid production found that mass or economic allocation method did not significantly change the results (Davis et al., 2021); yet additional studies that compare economic and biophysical allocation methods are needed.

Our analysis revealed that the sucrose feedstocks (e.g., sugar from sugar beets and from sugar cane) used as a carbon and energy source in the heterotrophic cultivation process was an environmental hot spot. We limited the scope of our analysis to sucrose and did not consider dextrose as an alternative. It is likely that dextrose sourced from edible-crop ingredients would also be an environmental hot spot, but future studies should include such alternatives.

Our analysis did not consider the additional material and energy inputs associated with the additional processing steps with fish oil to remove heavy metals, dioxins, polychlorinated biphenyls, persistent organic pollutants, and organochlorine pesticides that bioaccumulate in the fatty tissues of forage fishes (Oterhals et al., 2007; Sprague et al., 2010; Berntssen et al., 2016; Ng et al., 2018). Thus, this may lead to an underestimate of some environmental impacts of fish oil.

Several different pathways could reduce environmental impacts of Schizochytrium production. For instance, producers of Schizochytrium whole-cell or extracted oil could enroll in compliance offsets used to meet legally binding caps on carbon in schemes like the California Air Resources Board (CARB, 2021). Market-based approaches may lower emission reduction costs and strengthen industry support for environmental impact mitigation policies (Stavins, 2008), but compliance offsets should be complemented by other measures. Other measures include, for example, sources of carbon from renewable waste sources. Our sensitivity analysis revealed that crude glycerol from biodiesel production as an alternative source of carbon resulted in substantial reductions in global warming potential and marine eutrophication potential impacts. Crude glycerol is a waste product, hence commercially attractive for its low cost. Other sources such as cellulosic-derived sugars merit examination because they have the dual advantage of being both a low-cost waste product and avoiding the use of edible crops (Liu et al., 2019). However, feedstock logistics remain a significant hurdle to commercial production of Schizochytrium production on cellulosic-derived sugars (Liu et al., 2019). Therefore, decreasing organic carbon demand for the heterotrophic production of DHA would be needed to prevent burden shifting (i.e., Schizochytrium reduces marine biotic resource use but increases impacts in other environmental categories). One potential pathway that avoids the need for organic carbon sources is the cultivation of phototrophic marine species that have high concentrations of DHA (e.g., Isochrysis; Alkhamis and Qin, 2013). Future LCA studies should compare the production of phototrophic Isochrysis to heterotrophic Schizochytrium to understand the relative differences in environmental impacts.

This study focused on certain categories of environmental sustainability but a more comprehensive sustainability assessment, in line with the nearly universally adopted Sustainable Development Goals (United Nations, 2021), should also consider economic and social challenges (Govindan et al., 2021), and a more comprehensive assessment of threats to biodiversity when comparing marine microalgae to the benchmark, fish oil. The increasing price of fish oil is a major driver in the search for suitable alternatives (Naylor et al., 2021). Researchers are beginning to address the economic sustainability of fish meal and fish oil alternatives. One example is the multicriteria decision analysis by Ghamkhar and Hicks (2021) that evaluates the sustainability of varying formulated aquafeeds based on their relevant economic, environmental, commercial, and technical aspects. However, there is a need for additional industry-relevant technoeconomic assessments to understand the potential economic trade-offs and to generate new insights based on commercial Schizochytrium production in the United States and elsewhere. With respect to social challenges, the issues reported in the literature of supply chain management (e.g., violations of human rights, child labor, forced labor, discrimination, forced overtime, low wages, poor health and safety, and sexual harassment; Govindan et al., 2021) are important considerations in sustainable consumption and sustainable production. Although social welfare is a relatively new impact metric that has not been applied as widely as environmental impact in LCA (Huertas-Valdivia et al., 2020), it should be considered in future studies when making sustainability comparisons between marine microalgae and fish oil. Lastly, although our study included several metrics that are linked to pressures on biodiversity (e.g., land use, water use, biotic resource, greenhouse gas emissions, and eutrophication potential are all pressures that can drive habitat loss; Winter et al., 2017), a more comprehensive analysis will be required to understand the impact that expanding the production of Schizochytrium and canola oil as a replacement of fish oil will have on biodiversity. Because biodiversity is site-specific, often complex, and difficult to generalize within an LCA framework, a more comprehensive analysis will require improved methods that bridge the current gaps in land-use modeling and the interactions of species among each other and with their habitats (Lindner et al., 2019; Turner et al., 2019).

The results of our study reveal that replacing fish oil with Schizochytrium and canola oil has more nuanced effects on environmental sustainability than previously understood. Combining Schizochytrium and canola oil will significantly decrease the use of marine biotic resources but cultivation technologies for heterotrophic microalgae need improvement to mitigate environmental sustainability trade-offs. Mitigating such trade-offs will require cross-cutting innovations across domains of production (e.g., carbon from sugar feedstocks produced from renewable waste products such as plant cellulose) and sugar feedstock supply chains. Beyond environmental considerations alone, replacing fish oil with Schizochytrium and canola oil would provide human health benefits because this avoids heavy metal pollution and other contaminants of concern while maintaining the omega-3 fatty acids in farmed fish flesh. Thus, environmental sustainability trade-offs should be weighed against such human health benefits of using Schizochytrium to replace fish oil.

Increasing demand for feed inputs to aquaculture over the coming decades will make it critically important to understand the relative environmental performance of alternative feed inputs and identify trade-offs between different types and sources of inputs. The information presented here can be used to inform feed formulation decisions based on the environmental impacts of Schizochytrium products combined with canola oil as an alternative to fish oil. Furthermore, the information presented here can help aquafeed companies understand how they can make targeted improvements in their production processes to achieve their environmental sustainability goals.

The life cycle impact models (Appendix A), the hypothesis tests (Appendix B), the uncertainty analysis (Appendix C), the sensitivity analysis (Appendix D), and the files used to make the figures are available in the DRYAD repository at the following link: https://doi.org/10.6071/M3M961. The other data sets used in this article have been included in the supplemental material.

The supplemental files for this article can be found as follows:

We have provided supplemental text (S1 and S2), supplemental equation (S1), supplemental tables (S1–S48), and supplemental figures (S1–S5).

Text S1. Supplementary Methods.

Text S2. Supplementary Results.

Table S1. Fermentation volume, biomass yield and operating periods of Schizochytrium as reported in the literature.

Table S2. Composition of the Schizochytrium sp. medium.

Table S3. Primary energy consumption for fermentation of Schizochytrium sp.

Table S4. Primary energy consumption and efficiency for harvesting and drying Schizochytrium sp.

Table S5. Primary energy consumption and efficiency for solvent-free microwave extraction of oil from Schizochytrium sp.

Table S6. Primary energy consumption, hexane losses, and efficiencies of solvent oil extraction from Schizochytrium sp.

Table S7. Primary energy consumption, material inputs, and efficiencies of refining 1-kg crude oil from Schizochytrium sp.

Table S8. Primary energy consumption, material inputs, and efficiencies of refining 1-kg crude canola oil.

Table S9. Primary energy consumption and materials for fishing small pelagic fish.

Table S10. Primary energy consumption and materials for fishing byproducts.

Table S11. Primary energy consumption and materials for processing oil from small pelagic fish.

Table S12. Primary energy consumption and materials for processing oil from byproducts of fish.

Table S13. Primary energy consumption, material inputs, and efficiencies of refining 1-kg crude fish oil.

Table S14. Goodness of fit and fitting parameters used in uncertainty analysis of whole-cell Schizochytrium.

Table S15. Goodness of fit and fitting parameters used in uncertainty analysis of Schizochytrium oil extraction.

Table S16. Fitting parameters used in uncertainty analysis of Schizochytrium oil refining.

Table S17. Fitting parameters used in uncertainty analysis of canola oil refining.

Table S18. Goodness of fit and fitting parameters used in uncertainty analysis of fishing inputs of small pelagic fish.

Table S19. Fitting parameters used in uncertainty analysis of fishing inputs of byproducts.

Table S20. Goodness of fit and fitting parameters used in uncertainty analysis of oil extraction inputs of small pelagic fish.

Table S21. Fitting parameters used in uncertainty analysis of oil extraction inputs of fish byproducts.

Table S22. Fitting parameters used in uncertainty analysis of fish oil refining.

Table S23. Annual inputs and outputs for the cultivation of Schizochytrium in a 155 m³ fermenter.

Table S24. Annual inputs and outputs for harvesting of Schizochytrium in a 155 m³ fermenter in Brazil for the dry biomass (95% dwt) process train.

Table S25. Annual inputs and outputs for harvesting of Schizochytrium in a 155 m³ fermenter in the U.S. for the wet biomass (20% dwt) process train.

Table S26. Annual inputs and outputs for extracting oil from wet (20% dwt) Schizochytrium using solvent-free microwave extraction methods in the U.S.

Table S27. Annual inputs and outputs for extracting oil from wet (20% dwt) Schizochytrium using solvent methods in the U.S.

Table S28. Annual inputs and outputs for refining crude Schizochytrium oil in the microwave oil extraction scenario.

Table S29. Annual inputs and outputs for refining crude Schizochytrium oil in the solvent oil extraction scenario.

Table S30. Annual inputs and outputs for refining 1-kg crude canola oil.

Table S31. Fishing inputs and outputs of small pelagic fish.

Table S32. Fishing inputs and outputs of byproducts.

Table S33. Processing inputs and outputs of fish oil from small pelagic fish.

Table S34. Processing inputs and outputs of fish oil from fish byproducts.

Table S35. Annual inputs and outputs for refining 1-kg crude fish oil.

Table S36. Characterization factors from Simapro software with attributional database for Brazilian produced whole-cell Schizochytrium sp. and whole-cell Schizochytrium blended with refined canola oil.

Table S37. Characterization factors from Simapro software with attributional database for U.S. produced whole-cell Schizochytrium, Schizochytrium oil, and Schizochytrium oil blended with refined canola oil.

Table S38. Characterization factors from Simapro software with attributional database for refined canola oil.

Table S39. Characterization factors from Simapro software with attributional database for fish oil from fish byproducts and small pelagic fish.

Table S40. Characterization factors from Simapro software with consequential database for Brazilian produced whole-cell Schizochytrium sp. and whole-cell Schizochytrium blended with refined canola oil.

Table S41. Characterization factors from Simapro software with consequential database for U.S. produced whole-cell Schizochytrium, Schizochytrium oil, and Schizochytrium oil blended with refined canola oil.

Table S42. Characterization factors from Simapro software with consequential database for refined canola oil.

Table S43. Characterization factors from Simapro software used as alternative parameters in the sensitivity analysis.

Table S44. Results of single-tailed, unequal variance Student t tests to test hypotheses.

Table S45. Percent difference between environmental impacts calculated using attribution and consequential methods.

Table S46. Percent difference between environmental impacts of baseline values and those calculated using alternative parameters for whole cell Schizochytrium.

Table S47. Percent difference between environmental impacts of baseline values and those calculated using alternative parameters for Schizochytrium oil.

Table S48. Percent difference between environmental impacts of baseline values and those calculated using alternative parameters for Schizochytrium oil blends.

Figure S1. Sensitivity analysis results of standard deviation parameters of whole cell Schizochytrium.

Figure S2. Sensitivity analysis results of standard deviation parameters of Schizochytrium oil.

Figure S3. Life cycle impacts of whole-cell Schizochytrium sp. produced in two different regions calculated with consequential methods.

Figure S4. Life cycle impacts of Schizochytrium sp. oil produced with solvent-free microwave extraction methods in the U.S. calculated with consequential methods.

Figure S5. Life cycle impacts of Schizochytrium sp. oil blends calculated with consequential methods.

Equation S1. Relative economic allocation partitioning factor.

We gratefully acknowledge helpful feedback on model assumptions from Ana Morao [Corbion, Inc.]. We also thank Dr. Ramin Ghamkhar for providing comments during peer review that helped us to strengthen the paper.

This work was supported by NOAA Sea Grant, Award: NOAA-OAR-SG-2019-2005953 and by the University of California, Santa Cruz, Social Sciences Division (ARK startup funds).

The authors have no competing interests to declare. ARK is one of the Elementa Sustainability domain Editors-in-Chief. She was not involved in the review process of this article.

Contributed to conception and design: BLM, ARK, PKS.

Contributed to acquisition of data: BLM, ARK, PKS.

Contributed to analysis and interpretation of data: BLM, ARK, PKS, NC, AC, BS, CG.

Drafted and/or revised the article: BLM, ARK, PKS, NC, AC, BS, CG.

Approved the submitted version for publication: BLM, ARK, PKS, NC, AC, BS, CG.

How to cite this article: McKuin, BL, Kapuscinski, AR, Sarker, PK, Cheek, N, Colwell, A, Schoffstall, B, Greenwood, C. 2022. Comparative life cycle assessment of heterotrophic microalgae Schizochytrium and fish oil in sustainable aquaculture feeds. Elementa: Science of the Anthropocene 10(1). DOI: https://doi.org/10.1525/elementa.2021.00098

Domain Editor-in-Chief: Alastair Iles, University of California, Berkeley, CA, USA

Knowledge Domain: Sustainability Transitions