Effects of continuous water monitoring on the productive performance of Tilapia

Abstract

Daily fluctuations in temperature, pH, and turbidity are primary drivers of stress, feed inefficiency, and mortality in small-scale aquaculture. Conventional manual sampling (1-2 readings/day) is often insufficient to capture rapid parameter shifts, particularly after feeding or during transient weather events. To overcome this limitation, we designed and implemented a low-cost, open-source monitoring system utilizing an embedded architecture, commercial sensors, wireless data transmission, an HMI interface, and automated logging at 5-minute intervals. The biological efficacy of this system was validated over a 12-week trial in a Nile tilapia (Oreochromis niloticus) culture in Pamplona, Colombia. The study compared a control group under manual monitoring with an experimental group under automated monitoring (n=3 tanks per treatment; with equivalent stocking densities and feeding regimes).

The automated system significantly reduced the variability of water quality parameters and the duration of excursions outside optimal ranges. This enhanced stability resulted in superior production outcomes. At harvest, fish in the automated treatment achieved a greater final weight (48.6 ± 3.4 g vs. 39.8 ± 4.2 g in controls; a 22% increase), a higher specific growth rate (SGR: 2.12 ± 0.11 %/day vs. 1.85 ± 0.14 %/day), and an improved feed conversion ratio (FCR: 1.42 ± 0.07 vs. 1.71 ± 0.09); all differences were statistically significant (p ? 0.021). Cumulative mortality was also markedly reduced in the automated group (4.3% vs. 11.7%), yielding a higher final biomass. The system demonstrated high operational reliability (98.5% uptime) with no appreciable sensor drift observed during the study period; minimal interruptions were attributed to power outages and were mitigated with backup solutions.

These findings indicate that continuous, affordable water quality monitoring can significantly stabilize the culture environment, leading to enhanced growth performance, feed efficiency, and survival rates in small-scale freshwater aquaculture. The proposed system is both replicable and scalable, providing a foundational platform for the future integration of additional parameters (e.g., dissolved oxygen, ammonium) and predictive analytics.