The Perception Paradox: Why Accurate Data is Key in Fish Production

By: Victor Vargas Zelaya.

In aquaculture, a common scene unfolds: a production cycle ends with below-expected results. The initial, almost instinctive reaction is to blame external factors. "The feed was of poor quality," "the batch genetics were inferior," "the water temperature wasn't ideal." This is the Perception, as illustrated by Clive Talbot, where 100% of the responsibility is attributed to biology and the environment. It is an understandable defense mechanism, but a profoundly misleading one, leading us to externalize the problem and, consequently, miss the opportunity to improve what is truly under our control.

Upon delving into a technical analysis, this initial perception begins to crumble, revealing the Reality. The blame no longer falls exclusively on feed or genetics. According to Talbot's model, that initial 100% shrinks to just 30%. The remaining 70% is exposed as the result of inefficient culture technique. What does this mean? It means that operational decisions—from stocking density and feeding frequency to sampling protocols and harvest timing—have a far greater impact than is commonly believed. Inadequate management can negate even the best environmental conditions and the most balanced feed. This is the first crucial step towards productive maturity: recognizing that the greatest potential for improvement lies not outside, but within our own management system.

However, the evolution does not stop there. The Current Situation presents a more complex and revealing picture. When a farm succeeds in standardizing and validating its techniques, based on robust protocols and historical results, the causal landscape is surprisingly redistributed. External factors (feed, environment) are contained to a maximum of 20%. Technique, now optimized, sees its influence reduced to 40%. And then a new, powerful actor emerges: "Data Noise", which accounts for 50% of the variation.

This "noise" is not background music; it is signal distortion. It is the average weight calculated from an unrepresentative sample; it is the Feed Conversion Ratio (FCR) estimated with inaccurate consumption records; it is mortality that is not fully accounted for. These are imprecise, inconsistent, or incomplete data that cloud our understanding and prevent us from seeing the true picture of the culture. As Taylor (1997) points out, there is a critical difference between precision (repeatable values) and accuracy (values close to reality). A poorly calibrated scale can give us misleading precision, leading us to make decisions with a false sense of security. A minimal error, multiplied by thousands of individuals over the production cycle, becomes a significant financial gap (Piper et al., 1982).

The danger of inaccurate data is compounded by human cognitive biases. We tend towards "confirmation," seeking data that supports our initial beliefs, or "apophenia," finding patterns where there is only randomness (Kahneman, 2011). If we believe a new feed is better, we might unconsciously measure only the largest fish, generating "proof" that confirms our perception but is far from statistical reality (Zar, 1999). This "selective data" builds a comfortable but fictitious operational narrative where "everything is fine," until the harvest reveals the harsh biological truth, which does not forgive beliefs (Talbot & Hole, 1994).

The transition from perception to reality, and from there to the current situation, is, in essence, a journey towards data reliability. It is not simply about collecting numbers, but about instilling a culture of accurate data. This entails:

• Standard Protocols: Representative sample sizes, frequent equipment calibration, and uniform measurement methods.

• Internal Audit: Constantly verifying that what is recorded is what actually happens in the water.

• Critical Analysis: Transforming raw data into actionable information, always questioning results and seeking the root causes of variations.

Fig. 1 : Cause of variation in growth and FCR in aquaculture.

Source: Clive Talbot

In conclusion, the battle for productivity in aquaculture is not won solely with better feed or technologies, but with better data. The accuracy of information is the bridge that allows us to cross from subjective and blame-shifting perception, through the technical analysis that reveals our own deficiencies, to a management model where technique is optimized, and "noise" is controlled. In an environment where every gram of feed and every degree of temperature counts, measuring accurately is not an administrative task; it is the core strategy for mastering the art and science of farming fish.