Framework for Estimating Environmental Carrying Capacity in Diverse Climatic Conditions and Fish Farming Production in Neotropical Reservoirs - de Godoy, Elisa Maia; Camargo, Tavani Rocha; Toniato, Moranne; Proenca, Danilo Cintra; Obando, Johana Marcela Concha; Roubach, Rodrigo; Gallardo Ojeda, Pablo Rodrigo; Bueno, Guilherme Wolff (My University, 2025)
Effective sustainable fish farming necessitates enhanced models that incorporate environmental variability and contemporary monitoring methods. This research presents an innovative framework for assessing and modeling the environmental carrying capacity based on phosphorus (ECCp) in tropical and neotropical lakes and reservoirs. The model evaluates phosphorus waste from tilapia farming (Oreochromis niloticus) under diverse climatic conditions and production scenarios in cage systems. Using bioenergetic modeling and Monte Carlo simulations, we estimated phosphorus retention in fish and maximum production limits across different temperatures (21 degrees C, 25 degrees C, 29 degrees C) and dietary phosphorus concentrations (0.8%, 1.2%, 2.1%) in Brazil's Chavantes reservoir. Results indicated that phosphorus retention diminished with higher dietary phosphorus and increased temperatures, ranging from 51% (0.8% P) to 20% (2.1% P). Phosphorus discharge ranged from 3.3 to 20.5 kg/ton of fish produced. The ECCp model forecasted an allowable production of roughly 40 tons per year at full operational capacity, reflecting a 41% increase compared to current regulations. The model's accuracy (96%) surpassed that of traditional regulatory frameworks, which rely on static parameters, emphasizing the shortcomings of existing practices. The findings promote enhanced modeling strategies, sophisticated monitoring, adaptive management, and revised public policies to mitigate phosphorus emissions and support sustainable aquaculture in tropical and neotropical regions.
Keywords: aquaculture; bioeconomy; environmental carrying capacity; nile tilapia; phosphorus retention; climatic and environmental scenarios; probability analysis