Title : APY analysis: Integrating mathematical modeling in aquaculture to predict growth dynamics for high-yield and sustainable operations
Abstract:
Aquaculture, as a critical component of global food security and economic development, requires innovative approaches to ensure sustainable intensification without exacerbating environmental degradation. Recognizing the limitations imposed by finite land and water resources, recent efforts have focused on integrating digital technologies and data-driven methodologies to optimize production systems.
At Maritime Research Center (MRC) India, a novel framework, namely Area, Production and Yield (APY) Analysis, has been developed that synthesizes empirical knowledge from existing literature with machine learning-based modelling to construct a mathematical function mapping environmental variable—temperature, salinity, pH, dissolved oxygen, and stocking density—to organismal growth outcomes. By systematically encapsulating complex biological-environmental interactions within a mathematical model, this approach enables precision monitoring and adaptive management of aquaculture environments, thereby enhancing productivity without necessitating spatial expansion.
Validation studies underscore the model’s predictive fidelity, achieving significant accuracy in forecasting shrimp growth trajectories using solely physical parameters, and demonstrating its operational feasibility through the deployment of IoT-based monitoring systems. Furthermore, the framework facilitates spatial demarcation of high-yield zones based on climatic and hydro-chemical data, offering a scalable solution for resource optimization and risk mitigation in the face of climate variability. This research highlights the transformative potential of digital interventions in aquaculture, including remote farm operations, Integrated Multi Trophic Aquaculture (IoT), Precision farming, etc., positioning them as indispensable tools for future-proofing the sector against environmental, economic, and ecological uncertainties. The integration of environmental monitoring, system modelling, and machine learning represents a critical advancement towards achieving sustainable, resilient, and economically viable aquaculture systems.
