WWW.WAS.ORG • WORLD AQUACULTURE • SEPTEMBER 2024 41 available, ensuring data privacy and security (Magnusson 2023). • Environmental Map: The S3AM approach includes an environmental data “learning model” that allows the drone software to self-calibrate. This enables the creation of intuitive environmental maps capturing parameters such as temperature, dissolved oxygen, light penetration, turbidity, and salinity. Over time, these maps facilitate realistic environmental predictions for specific aquaculture sites. For efficient, organized oyster harvesting, the S3AM software will combine a crop inventory map, precise dredge position, and water current estimations to create an optimal dredging boat path, saving time and labor, and increasing efficiency. This way, it is easier for growers to estimate the amount of seed required for their lease area, and the time and location to dredge oysters for harvesting, hence, also expected to minimize the potential damage associated with excessive dredging. In the traditional bottomculture oyster industry, where many growers operate on a small scale, access to capital and resources for investing in advanced technology is often limited; it is of utmost importance that investment decisions are carefully made. Therefore, our team is conducting an economic analysis of technology adoption to assess its feasibility. Using enterprise budgets as in Engle and van Senten (2018), created from primary data collected during a 2018 survey of Maryland oyster farms employing bottom-culture oyster aquaculture methods, our team conducted a series of simulations to evaluate the economic impacts of technology adoption across different production scales. We simulated over 10,000 farms. The positive distribution of Net Present Value (NPV) from our analysis suggests that technological adoption would generate profits for medium and larger-scale farms, indicating potential benefits from economies of scale (Figure 5 and Figure 6, Table 1); however, our analysis also reveals that adoption may not be as advantageous for small farms, as indicated by the negative NPV distribution in Figure 7. It is important to acknowledge the limitations of our study, which include probabilistic assumptions regarding the benefits and costs of adoption. These assumptions encompass factors such as reduced total seed costs, decreased fuel and labor costs, and an increased yield of oysters. On the cost side, factors include increased initial investment, higher electricity costs, and increased depreciation and repair and maintenance costs associated with the technology. The results might change if we include data from actual farm trials in the future. While our findings suggest that technology may not yield significant profits for small farms, it is our belief that technology providers should consider offering their innovations at rental prices that make them economically viable for small-scale growers. As we move forward with this project, our focus will be on determining breakeven rental prices, ensuring that both growers and service providers can operate without incurring losses. It is important to note that our study is based on probabilistic assumptions. To make informed decisions, we must gather data on key farm performance metrics such as total revenue, seed usage, fuel requirements, and labor costs for farms utilizing this technology. Given that the technology is still undergoing refinement, we commit to updating our findings and recommendations based on real-world field trial data. Our study underscores the importance of considering both the usability and profitability of technology before introducing it to aquaculture communities. We advocate for thorough economic analyses to be conducted by any technology developers before FIGURE 5. NPV result for 6000 bushels/year-production farm size in Maryland. FIGURE 6. NPV result for 2000 bushels/year-production farm size in Maryland. FIGURE 7. NPV Distribution result for 200 bushels/year-production farm size in Maryland. (CONTINUED ON PAGE 42)
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